1
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Lin PH, Kuo PH, Chen KL. Developmental Prediction of Poststroke Patients in Activities of Daily Living by Using Tree-Structured Parzen Estimator-Optimized Stacking Ensemble Approaches. IEEE J Biomed Health Inform 2024; 28:2745-2758. [PMID: 38437144 DOI: 10.1109/jbhi.2024.3372649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Poststroke injuries limit the daily activities of patients and cause considerable inconvenience. Therefore, predicting the activities of daily living (ADL) results of patients with stroke before hospital discharge can assist clinical workers in formulating more personalized and effective strategies for therapeutic intervention, and prepare hospital discharge plans that suit the patients needs. This study used the leave-one-out cross-validation procedure to evaluate the performance of the machine learning models. In addition, testing methods were used to identify the optimal weak learners, which were then combined to form a stacking model. Subsequently, a hyperparameter optimization algorithm was used to optimize the model hyperparameters. Finally, optimization algorithms were used to analyze each feature, and features of high importance were identified by limiting the number of features to be included in the machine learning models. After various features were fed into the learning models to predict the Barthel index (BI) at discharge, the results indicated that random forest (RF), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) produced suitable results. The most critical prediction factor of this study was the BI at admission. Machine learning models can be used to assist clinical workers in predicting the ADL of patients with stroke at hospital discharge.
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2
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Hiraishi M, Wan K, Tanioka K, Yadohisa H, Shimokawa T. Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects. Stat Methods Med Res 2024:9622802241247728. [PMID: 38676367 DOI: 10.1177/09622802241247728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.
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Affiliation(s)
- Mayu Hiraishi
- Clinical Study Support Center, Wakayama Medical University Hospital, Wakayama, Japan
- Graduate School of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Ke Wan
- Department of Medical Data Science, Graduate School of Medicine, Wakayama Medical University, Wakayama, Japan
| | - Kensuke Tanioka
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Hiroshi Yadohisa
- Department of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Toshio Shimokawa
- Department of Medical Data Science, Graduate School of Medicine, Wakayama Medical University, Wakayama, Japan
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3
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Jiang X, Li W, Li R, Ning J. Addressing subject heterogeneity in time-dependent discrimination for biomarker evaluation. Stat Med 2024; 43:1341-1353. [PMID: 38287471 DOI: 10.1002/sim.10024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/29/2023] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Accurate discrimination has been the central goal in identifying biomarkers for monitoring disease progression and early detection. Acknowledging the fact that discrimination accuracy of biomarkers for a time-to-event outcome often changes over time, local measures such as the time-dependent receiver operating characteristic curve and its area under the curve (AUC) are used to assess time-dependent predictive discrimination. However, such measures do not address subject heterogeneity, although the impact of covariates including demographics, disease-related characteristics, and other clinical information on the discriminatory performance of biomarkers needs to be investigated before their clinical use. We propose the covariate-specific time-dependent AUC, a measure for covariate-adjusted discrimination. We develop a regression model on the covariate-specific time-dependent AUC to understand how and in what magnitude the covariates influence biomarker performance. Then we construct a pseudo partial-likelihood for estimation and inference. This is followed by our establishing the asymptotic properties of the proposed estimators and provide variance estimation. The simulation studies and application to the AIDS Clinical Trials Group 175 data demonstrate that the proposed method offers an informative tool for inferring covariate-specific and time-dependent predictive discrimination.
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Affiliation(s)
- Xinyang Jiang
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Wen Li
- Department of Internal Medicine, The University of Texas McGovern Medical School, Houston, Texas, USA
| | - Ruosha Li
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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4
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Lee D, Gao C, Ghosh S, Yang S. Transporting survival of an HIV clinical trial to the external target populations. J Biopharm Stat 2024:1-22. [PMID: 38520697 DOI: 10.1080/10543406.2024.2330216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 02/20/2024] [Indexed: 03/25/2024]
Abstract
Due to the heterogeneity of the randomized controlled trial (RCT) and external target populations, the estimated treatment effect from the RCT is not directly applicable to the target population. For example, the patient characteristics of the ACTG 175 HIV trial are significantly different from that of the three external target populations of interest: US early-stage HIV patients, Thailand HIV patients, and southern Ethiopia HIV patients. This paper considers several methods to transport the treatment effect from the ACTG 175 HIV trial to the target populations beyond the trial population. Most transport methods focus on continuous and binary outcomes; on the contrary, we derive and discuss several transport methods for survival outcomes: an outcome regression method based on a Cox proportional hazard (PH) model, an inverse probability weighting method based on the models for treatment assignment, sampling score, and censoring, and a doubly robust method that combines both methods, called the augmented calibration weighting (ACW) method. However, as the PH assumption was found to be incorrect for the ACTG 175 trial, the methods that depend on the PH assumption may lead to the biased quantification of the treatment effect. To account for the violation of the PH assumption, we extend the ACW method with the linear spline-based hazard regression model that does not require the PH assumption. Applying the aforementioned methods for transportability, we explore the effect of PH assumption, or the violation thereof, on transporting the survival results from the ACTG 175 trial to various external populations.
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Affiliation(s)
- Dasom Lee
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Chenyin Gao
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Sujit Ghosh
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Shu Yang
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
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5
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Chen J, Zhou Y, Wei X, Xu X, Qin Z, Ong CP, Ye ZW, Jin DY, Boitrel B, Yuan S, Chan JFW, Li H, Sun H. Development of Pan-Anti-SARS-CoV-2 Agents through Allosteric Inhibition of nsp14/nsp10 Complex. ACS Infect Dis 2024; 10:858-869. [PMID: 37897418 DOI: 10.1021/acsinfecdis.3c00356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/30/2023]
Abstract
SARS-CoV-2 nsp14 functions both as an exoribonuclease (ExoN) together with its critical cofactor nsp10 and as an S-adenosyl methionine-dependent (guanine-N7) methyltransferase (MTase), which makes it an attractive target for the development of pan-anti-SARS-CoV-2 drugs. Herein, we screened a panel of compounds (and drugs) and found that certain compounds, especially Bi(III)-based compounds, could allosterically inhibit both MTase and ExoN activities of nsp14 potently. We further demonstrated that Bi(III) binds to both nsp14 and nsp10, resulting in the release of Zn(II) ions from the enzymes as well as alternation of protein quaternary structures. The in vitro activities of the compounds were also validated in SARS-CoV-2-infected mammalian cells. Importantly, we showed that nsp14 serves as an authentic target of Bi(III)-based antivirals in SARS-CoV-2-infected mammalian cells by quantification of both the protein and inhibitor. This study highlights the importance of nsp14/nsp10 as a potential target for the development of pan-antivirals against SARS-CoV-2 infection.
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Affiliation(s)
- Jingxin Chen
- Department of Chemistry, State Key Laboratory of Synthetic Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Ying Zhou
- Department of Chemistry, State Key Laboratory of Synthetic Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Xueying Wei
- Department of Chemistry, State Key Laboratory of Synthetic Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong 999077, P. R. China
- Department of Microbiology, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Xiaohan Xu
- Department of Chemistry, State Key Laboratory of Synthetic Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Zhenzhi Qin
- Department of Microbiology, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Chon Phin Ong
- School of Biomedical Sciences, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Zi-Wei Ye
- School of Biomedical Sciences, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Dong-Yan Jin
- School of Biomedical Sciences, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Bernard Boitrel
- University of Rennes, CNRS, ISCR (Institut des Sciences Chimiques de Rennes)-UMR 6226, Rennes 35000, France
| | - Shuofeng Yuan
- Department of Microbiology, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Jasper F-W Chan
- Department of Microbiology, The University of Hong Kong, Sassoon Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Hongyan Li
- Department of Chemistry, State Key Laboratory of Synthetic Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong 999077, P. R. China
| | - Hongzhe Sun
- Department of Chemistry, State Key Laboratory of Synthetic Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong 999077, P. R. China
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6
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Balzer LB, Cai E, Godoy Garraza L, Amaranath P. Adaptive selection of the optimal strategy to improve precision and power in randomized trials. Biometrics 2024; 80:ujad034. [PMID: 38446441 PMCID: PMC10916702 DOI: 10.1093/biomtc/ujad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/06/2023] [Accepted: 12/15/2023] [Indexed: 03/07/2024]
Abstract
Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types. Their findings build on a long history, starting in 1932 with R.A. Fisher and including more recent endorsements by the U.S. Food and Drug Administration and the European Medicines Agency. Here, we address an important practical consideration: how to select the adjustment approach-which variables and in which form-to maximize precision, while maintaining Type-I error control. Balzer et al. previously proposed Adaptive Pre-specification within TMLE to flexibly and automatically select, from a prespecified set, the approach that maximizes empirical efficiency in small trials (N < 40). To avoid overfitting with few randomized units, selection was previously limited to working generalized linear models, adjusting for a single covariate. Now, we tailor Adaptive Pre-specification to trials with many randomized units. Using V-fold cross-validation and the estimated influence curve-squared as the loss function, we select from an expanded set of candidates, including modern machine learning methods adjusting for multiple covariates. As assessed in simulations exploring a variety of data-generating processes, our approach maintains Type-I error control (under the null) and offers substantial gains in precision-equivalent to 20%-43% reductions in sample size for the same statistical power. When applied to real data from ACTG Study 175, we also see meaningful efficiency improvements overall and within subgroups.
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Affiliation(s)
- Laura B Balzer
- Division of Biostatistics, University of California Berkeley, Berkeley, CA 94720, United States
| | - Erica Cai
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, United States
| | - Lucas Godoy Garraza
- Department of Biostatistics, University of Massachusetts Amherst, Amherst, MA 01003, United States
| | - Pracheta Amaranath
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, United States
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7
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Jin M. Imputation methods for informative censoring in survival analysis with time dependent covariates. Contemp Clin Trials 2024; 136:107401. [PMID: 37995968 DOI: 10.1016/j.cct.2023.107401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/16/2023] [Accepted: 11/18/2023] [Indexed: 11/25/2023]
Abstract
Cox proportional hazards model has been an established model for survival analysis. The flexibility of incorporating time dependent covariates has made the analysis more suitable in many clinical trials when the time dependent covariates may be predictive factors for the events. Subjects are censored for various reasons, but they are usually nonnormatively censored in the analysis. Methods for informative censoring are not well studied for settings with time dependent covariates. In this paper, we propose a few methods for informative censoring in survival analysis by Cox model with time dependent covariates, including tipping point method and Reference Based Imputation (Jump to Reference and Copy Reference). The implementation of these methods by multiple imputation is described and illustrated with two data examples.
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Affiliation(s)
- Man Jin
- Data and Statistical Sciences, AbbVie Inc., North Chicago 60064, IL, USA.
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8
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Shah KS, Fu H, Kosorok MR. Stabilized direct learning for efficient estimation of individualized treatment rules. Biometrics 2023; 79:2843-2856. [PMID: 36585916 DOI: 10.1111/biom.13818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 12/23/2022] [Indexed: 01/01/2023]
Abstract
In recent years, the field of precision medicine has seen many advancements. Significant focus has been placed on creating algorithms to estimate individualized treatment rules (ITRs), which map from patient covariates to the space of available treatments with the goal of maximizing patient outcome. Direct learning (D-Learning) is a recent one-step method which estimates the ITR by directly modeling the treatment-covariate interaction. However, when the variance of the outcome is heterogeneous with respect to treatment and covariates, D-Learning does not leverage this structure. Stabilized direct learning (SD-Learning), proposed in this paper, utilizes potential heteroscedasticity in the error term through a residual reweighting which models the residual variance via flexible machine learning algorithms such as XGBoost and random forests. We also develop an internal cross-validation scheme which determines the best residual model among competing models. SD-Learning improves the efficiency of D-Learning estimates in binary and multi-arm treatment scenarios. The method is simple to implement and an easy way to improve existing algorithms within the D-Learning family, including original D-Learning, Angle-based D-Learning (AD-Learning), and Robust D-learning (RD-Learning). We provide theoretical properties and justification of the optimality of SD-Learning. Head-to-head performance comparisons with D-Learning methods are provided through simulations, which demonstrate improvement in terms of average prediction error (APE), misclassification rate, and empirical value, along with a data analysis of an acquired immunodeficiency syndrome (AIDS) randomized clinical trial.
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Affiliation(s)
- Kushal S Shah
- Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, USA
| | - Haoda Fu
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, USA
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, USA
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9
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Li P, Qin J, Liu Y. Instability of inverse probability weighting methods and a remedy for nonignorable missing data. Biometrics 2023; 79:3215-3226. [PMID: 37221141 DOI: 10.1111/biom.13881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/03/2023] [Indexed: 05/25/2023]
Abstract
Inverse probability weighting (IPW) methods are commonly used to analyze nonignorable missing data (NIMD) under the assumption of a logistic model for the missingness probability. However, solving IPW equations numerically may involve nonconvergence problems when the sample size is moderate and the missingness probability is high. Moreover, those equations often have multiple roots, and identifying the best root is challenging. Therefore, IPW methods may have low efficiency or even produce biased results. We identify the pitfall in these methods pathologically: they involve the estimation of a moment-generating function (MGF), and such functions are notoriously unstable in general. As a remedy, we model the outcome distribution given the covariates of the completely observed individuals semiparametrically. After forming an induced logistic regression (LR) model for the missingness status of the outcome and covariate, we develop a maximum conditional likelihood method to estimate the underlying parameters. The proposed method circumvents the estimation of an MGF and hence overcomes the instability of IPW methods. Our theoretical and simulation results show that the proposed method outperforms existing competitors greatly. Two real data examples are analyzed to illustrate the advantages of our method. We conclude that if only a parametric LR is assumed but the outcome regression model is left arbitrary, then one has to be cautious in using any of the existing statistical methods in problems involving NIMD.
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Affiliation(s)
- Pengfei Li
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Jing Qin
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Yukun Liu
- KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, China
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10
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Tran TD, Abad AA, Verbeke G, Molenberghs G, Van Mechelen I. Reflections on the concept of optimality of single decision point treatment regimes. Biom J 2023; 65:e2200285. [PMID: 37736675 DOI: 10.1002/bimj.202200285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 06/28/2023] [Accepted: 07/30/2023] [Indexed: 09/23/2023]
Abstract
In many areas, applied researchers as well as practitioners have to choose between different solutions for a problem at hand; this calls for optimal decision rules to settle the choices involved. As a key example, one may think of the search for optimal treatment regimes (OTRs) in clinical research, that specify which treatment alternative should be administered to each patient under study. Motivated by the fact that the concept of optimality of decision rules in general and treatment regimes in particular has received so far relatively little attention and discussion, we will present a number of reflections on it, starting from the basics of any optimization problem. Specifically, we will analyze the search space and the to be optimized criterion function underlying the search of single decision point OTRs, along with the many choice aspects that show up in their specification. Special attention is paid to formal characteristics and properties as well as to substantive concerns and hypotheses that may guide these choices. We illustrate with a few empirical examples taken from the literature. Finally, we discuss how the presented reflections may help sharpen statistical thinking about optimality of decision rules for treatment assignment and to facilitate the dialogue between the statistical consultant and the applied researcher in search of an OTR.
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Affiliation(s)
- Trung Dung Tran
- Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium
| | | | - Geert Verbeke
- I-BioStat, KU Leuven, Leuven, Belgium
- I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Geert Molenberghs
- I-BioStat, KU Leuven, Leuven, Belgium
- I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Iven Van Mechelen
- Quantitative Psychology and Individual Differences, KU Leuven, Leuven, Belgium
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11
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Rodriguez Duque D, Moodie EEM, Stephens DA. Bayesian inference for optimal dynamic treatment regimes in practice. Int J Biostat 2023; 19:309-331. [PMID: 37192544 DOI: 10.1515/ijb-2022-0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 03/21/2023] [Indexed: 05/18/2023]
Abstract
In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they are most interested in identifying optimal DTRs. Recent work has developed Bayesian methods for identifying optimal DTRs in a family indexed by ψ via Bayesian dynamic marginal structural models (MSMs) (Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)); we review the proposed estimation procedure and illustrate its use via the new BayesDTR R package. Although methods in Rodriguez Duque D, Stephens DA, Moodie EEM, Klein MB. (Semiparametric Bayesian inference for dynamic treatment regimes via dynamic regime marginal structural models. Biostatistics; 2022. (In Press)) can estimate optimal DTRs well, they may lead to biased estimators when the model for the expected outcome if everyone in a population were to follow a given treatment strategy, known as a value function, is misspecified or when a grid search for the optimum is employed. We describe recent work that uses a Gaussian process ( G P ) prior on the value function as a means to robustly identify optimal DTRs (Rodriguez Duque D, Stephens DA, Moodie EEM. Estimation of optimal dynamic treatment regimes using Gaussian processes; 2022. Available from: https://doi.org/10.48550/arXiv.2105.12259). We demonstrate how a G P approach may be implemented with the BayesDTR package and contrast it with other value-search approaches to identifying optimal DTRs. We use data from an HIV therapeutic trial in order to illustrate a standard analysis with these methods, using both the original observed trial data and an additional simulated component to showcase a longitudinal (two-stage DTR) analysis.
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Affiliation(s)
| | - Erica E M Moodie
- Department of Epidemiology & Biostatistics, McGill University, Montréal, QC, Canada
| | - David A Stephens
- Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada
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12
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Hirsch MS. The Joys of Research and Mentorship: Personal Observations Over Six Decades. J Infect Dis 2023; 228:975-978. [PMID: 37145101 DOI: 10.1093/infdis/jiad139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 05/03/2023] [Indexed: 05/06/2023] Open
Affiliation(s)
- Martin S Hirsch
- Infectious Disease Division, Massachusetts General Hospital, Boston, MA, USA
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13
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Song X. A corrected smoothed score approach for semiparametric accelerated failure time model with error-contaminated covariates. Stat Med 2023; 42:4043-4055. [PMID: 37443445 DOI: 10.1002/sim.9847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 06/05/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
Abstract
We consider the semiparametric accelerated failure time (AFT) model with multiple covariates measured with error. Existing methods for the AFT model are either inconsistent, computationally intensive, or require stringent assumptions. To overcome these limitations, we develop a correction approach for a general smooth function of error-contaminated variables. We apply this method to the smoothed rank-based score function for the AFT model. The estimator is consistent and asymptotically normal. The finite-sample performance of the method is assessed by simulation studies. The approach is illustrated by application to data from an HIV clinical trial.
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Affiliation(s)
- Xiao Song
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA
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14
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Dahabreh IJ, Robins JM, Haneuse SJP, Saeed I, Robertson SE, Stuart EA, Hernán MA. Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population. Stat Med 2023; 42:2029-2043. [PMID: 36847107 PMCID: PMC10219839 DOI: 10.1002/sim.9550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 05/20/2022] [Accepted: 07/21/2022] [Indexed: 03/01/2023]
Abstract
Extending (i.e., generalizing or transporting) causal inferences from a randomized trial to a target population requires assumptions that randomized and nonrandomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that directly parameterize violations of the assumptions using bias functions and do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of nonrandomized individuals, as well as to nested trial designs, where the trial is embedded within a cohort sampled from the target population.
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Affiliation(s)
- Issa J. Dahabreh
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - James M. Robins
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Iman Saeed
- Center for Evidence Synthesis in Health, Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI
| | - Sarah E. Robertson
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Elizabeth A. Stuart
- Departments of Mental Health, Biostatistics, and Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Miguel A. Hernán
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA
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15
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Sun Y, Zhou Q, Gilbert PB. Analysis of the Cox Model with Longitudinal Covariates with Measurement Errors and Partly Interval Censored Failure Times, with Application to an AIDS Clinical Trial. Stat Biosci 2023; 15:430-454. [PMID: 37313548 PMCID: PMC10198790 DOI: 10.1007/s12561-023-09372-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 04/18/2023] [Accepted: 04/27/2023] [Indexed: 06/15/2023]
Abstract
Time-dependent covariates are often measured intermittently and with measurement errors. Motivated by the AIDS Clinical Trials Group (ACTG) 175 trial, this paper develops statistical inferences for the Cox model for partly interval censored failure times and longitudinal covariates with measurement errors. The conditional score methods developed for the Cox model with measurement errors and right censored data are no longer applicable to interval censored data. Assuming an additive measurement error model for a longitudinal covariate, we propose a nonparametric maximum likelihood estimation approach by deriving the measurement error induced hazard model that shows the attenuating effect of using the plug-in estimate for the true underlying longitudinal covariate. An EM algorithm is devised to facilitate maximum likelihood estimation that accounts for the partly interval censored failure times. The proposed methods can accommodate different numbers of replicates for different individuals and at different times. Simulation studies show that the proposed methods perform well with satisfactory finite-sample performances and that the naive methods ignoring measurement error or using the plug-in estimate can yield large biases. A hypothesis testing procedure for the measurement error model is proposed. The proposed methods are applied to the ACTG 175 trial to assess the associations of treatment arm and time-dependent CD4 cell count on the composite clinical endpoint of AIDS or death. Supplementary Information The online version contains supplementary material available at 10.1007/s12561-023-09372-y.
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Affiliation(s)
- Yanqing Sun
- Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC USA
| | - Qingning Zhou
- Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC USA
| | - Peter B. Gilbert
- Vaccine and Infectious Disease and Public Health Sciences Divisions, Fred Hutchinson Cancer Center, Seattle, WA USA
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16
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Wang C, Adam GC, Burlein C, Carroll S, Dankulich W, Diamond T, Grobler J, Heath J, Johnson A, Klein D, Krosky D, Narayan K, Ou Y, Sanders J, Sharma S, Xu M, Converso A. Discovery of arylsulfonamides as a novel class of allosteric integrase inhibitors with antiviral activity. Bioorg Med Chem Lett 2023; 89:129303. [PMID: 37146837 DOI: 10.1016/j.bmcl.2023.129303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/21/2023] [Accepted: 04/21/2023] [Indexed: 05/07/2023]
Abstract
Lens epithelial-derived growth factor (LEDGF) increases the efficiency of proviral DNA integration into the host genome by interacting with HIV integrase (IN) and directing it to a chromatin environment that favors viral transcription. Allosteric integrase inhibitors (ALLINIs), such as known 2-(tert-butoxy)acetic acid (1), bind to the LEDGF pocket on the catalytic core domain (CCD) of IN, but exert more potent antiviral activities by inhibition of late-stage HIV-1 replication events than through disruption of proviral integration at an earlier phase. A high-throughput screen (HTS) for compounds that disrupt IN-LEDGF interaction led to the identification of a novel arylsulfonamide series, as exemplified by 2, possessing ALLINI-like properties. Further SAR studies led to more potent compound 21 and provided key chemical biology probes revealing that arylsulfonamides are a novel class of ALLINIs with a distinct binding mode than that of 2-(tert-butoxy)acetic acids.
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Affiliation(s)
- Cheng Wang
- Discovery Chemistry, Merck & Co., Inc., West Point, PA 19486, USA
| | - Gregory C Adam
- Screening and Protein Science, Merck & Co., Inc., West Point, PA 19486, USA
| | | | - Steven Carroll
- Pharmacology, Merck & Co., Inc., West Point, PA 19486, USA
| | | | - Tracy Diamond
- Infectious Disease Biology Discovery, Merck & Co., Inc., West Point, PA 19486, USA
| | - Jay Grobler
- Infectious Disease Biology Discovery, Merck & Co., Inc., West Point, PA 19486, USA
| | - Jeffrey Heath
- Infectious Disease Biology Discovery, Merck & Co., Inc., West Point, PA 19486, USA
| | - Adam Johnson
- Discovery Chemistry, Merck & Co., Inc., West Point, PA 19486, USA
| | - Daniel Klein
- Computational and Structural Chemistry, Merck & Co., Inc., West Point, PA 19486, USA
| | - Daniel Krosky
- Pharmacology, Merck & Co., Inc., West Point, PA 19486, USA
| | - Kartik Narayan
- Pharmacology, Merck & Co., Inc., West Point, PA 19486, USA
| | - Yangsi Ou
- Pharmacology, Merck & Co., Inc., West Point, PA 19486, USA
| | - John Sanders
- Computational and Structural Chemistry, Merck & Co., Inc., West Point, PA 19486, USA
| | - Sujata Sharma
- Screening and Protein Science, Merck & Co., Inc., West Point, PA 19486, USA
| | - Min Xu
- Pharmacology, Merck & Co., Inc., West Point, PA 19486, USA
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17
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Feagan BG, Sands BE, Sandborn WJ, Germinaro M, Vetter M, Shao J, Sheng S, Johanns J, Panés J. Guselkumab plus golimumab combination therapy versus guselkumab or golimumab monotherapy in patients with ulcerative colitis (VEGA): a randomised, double-blind, controlled, phase 2, proof-of-concept trial. Lancet Gastroenterol Hepatol 2023; 8:307-320. [PMID: 36738762 DOI: 10.1016/s2468-1253(22)00427-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Despite the introduction of new monoclonal antibodies and oral therapies for the treatment of ulcerative colitis, clinical remission rates remain low, underscoring the need for innovative treatment approaches. We assessed whether guselkumab plus golimumab combination therapy was more effective for ulcerative colitis than either monotherapy. METHODS We did a randomised, double-blind, controlled, proof-of-concept trial at 54 hospitals, academic medical centres, or private practices in nine countries. Eligible adults (aged ≥18 to 65 years) had a confirmed diagnosis of ulcerative colitis at least 3 months before screening and moderately-to-severely active ulcerative colitis (Mayo score 6-12) with a centrally-read baseline endoscopy subscore of 2 or higher. Patients were randomly assigned (1:1:1) using a computer-generated randomisation schedule to combination therapy (subcutaneous golimumab 200 mg at week 0, subcutaneous golimumab 100 mg at weeks 2, 6, and 10, and intravenous guselkumab 200 mg at weeks 0, 4, and 8, followed by subcutaneous guselkumab monotherapy 100 mg every 8 weeks for 32 weeks), golimumab monotherapy (subcutaneous golimumab 200 mg at week 0 followed by subcutaneous golimumab 100 mg at week 2 and every 4 weeks thereafter for 34 weeks), or guselkumab monotherapy (intravenous guselkumab 200 mg at weeks 0, 4, and 8, followed by subcutaneous guselkumab 100 mg every 8 weeks thereafter for 32 weeks). The primary endpoint was clinical response at week 12 (defined as a ≥30% decrease from baseline in the full Mayo score and a ≥3 points absolute reduction with either a decrease in rectal bleeding score of ≥1 point or a rectal bleeding score of 0 or 1). Efficacy was analysed in the modified intention-to-treat population up to week 38, which included all randomly assigned patients who received at least one (partial or complete) study intervention dose. Safety was analysed up to week 50, according to study intervention received among all patients who received at least one (partial or complete) dose of study intervention. This trial is complete and is registered with ClinicalTrials.gov, NCT03662542. FINDINGS Between Nov 20, 2018, and Nov 15, 2021, 358 patients were screened for eligibility, of whom 214 patients were randomly assigned to combination therapy (n=71), golimumab monotherapy (n=72), or guselkumab monotherapy (n=71). Of the 214 patients included, 98 (46%) were women and 116 (54%) were men and the mean age was 38·4 years (SD 12·0). At week 12, 59 (83%) of 71 patients in the combination therapy group had achieved clinical response compared with 44 (61%) of 72 patients in the golimumab monotherapy group (adjusted treatment difference 22·1% [80% CI 12·9 to 31·3]; nominal p=0·0032) and 53 (75%) of 71 patients in the guselkumab monotherapy group (adjusted treatment difference 8·5% [-0·2 to 17·1; nominal p=0·2155). At week 50, 45 (63%) of 71 patients in the combination therapy group, 55 (76%) of 72 patients in the golimumab monotherapy group, and 46 (65%) of 71 patients in the guselkumab monotherapy group had reported at least one adverse event. The most common adverse events were ulcerative colitis, upper respiratory tract infection, headache, anaemia, nasopharyngitis, neutropenia, and pyrexia. No deaths, malignancies, or cases of tuberculosis were reported during the combination induction period. One case of tuberculosis was reported in the combination therapy group and one case of colon adenocarcinoma was reported in the guselkumab monotherapy group; both occurred after week 12. Two deaths were reported after the final dose of study intervention (poisoning in the combination therapy group and COVID-19 in the guselkumab monotherapy group). INTERPRETATION Data from this proof-of-concept study suggest that combination therapy with guselkumab and golimumab might be more effective for ulcerative colitis than therapy with either drug alone. These findings require confirmation in larger trials. FUNDING Janssen Research and Development.
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Affiliation(s)
| | - Bruce E Sands
- Dr Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Marion Vetter
- Janssen Research and Development, Spring House, PA, USA
| | - Jie Shao
- Janssen Research and Development, Spring House, PA, USA
| | - Shihong Sheng
- Janssen Research and Development, Spring House, PA, USA
| | - Jewel Johanns
- Janssen Research and Development, Spring House, PA, USA
| | - Julián Panés
- Department of Gastroenterology, Hospital Clinic of Barcelona, IDIBAPS, CIBEREHD, Barcelona, Spain
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18
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Guillaudeux M, Rousseau O, Petot J, Bennis Z, Dein CA, Goronflot T, Vince N, Limou S, Karakachoff M, Wargny M, Gourraud PA. Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis. NPJ Digit Med 2023; 6:37. [PMID: 36899082 DOI: 10.1038/s41746-023-00771-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 02/07/2023] [Indexed: 03/12/2023] Open
Abstract
While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients' trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients' privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an "avatar data", for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statistical information. Compared to Synthpop and CT-GAN, the Avatar method shows a similar level of signal maintenance while allowing to compute additional privacy metrics. In the light of distance-based privacy metrics, each individual produces an avatar simulation that is on average indistinguishable from 12 other generated avatar simulations for the clinical trial and 24 for the observational study. Data transformation using the Avatar method both preserves, the evaluation of the treatment's effectiveness with similar hazard ratios for the clinical trial (original HR = 0.49 [95% CI, 0.39-0.63] vs. avatar HR = 0.40 [95% CI, 0.31-0.52]) and the classification properties for the observational study (original AUC = 99.46 (s.e. 0.25) vs. avatar AUC = 99.84 (s.e. 0.12)). Once validated by privacy metrics, anonymous synthetic data enable the creation of value from sensitive pseudonymized data analyses by tackling the risk of a privacy breach.
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19
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Chen C, Yu T, Shen B, Wang M. Synthesizing secondary data into survival analysis to improve estimation efficiency. Biom J 2023; 65:e2100326. [PMID: 36192158 DOI: 10.1002/bimj.202100326] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 07/19/2022] [Accepted: 08/14/2022] [Indexed: 11/09/2022]
Abstract
The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross-sectional disease-relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two-stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical-likelihood-based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW-AFT) and the Cox PH model (ELW-Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.
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Affiliation(s)
- Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Tonghui Yu
- School of Mathematics, Hefei University of Technology, Anhui, China
| | - Biyi Shen
- Bristol Myers Squibb, Lawrence Township, New Jersey, U.S.A
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve university, Cleveland, Ohio, USA
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20
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Song X, Chao EC, Wang CY. A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error-contaminated continuous time-dependent exposure. Biometrics 2023; 79:437-448. [PMID: 34694632 PMCID: PMC9399755 DOI: 10.1111/biom.13595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 10/09/2021] [Accepted: 10/19/2021] [Indexed: 11/27/2022]
Abstract
We consider the proportional hazards model in which the covariates include the discretized categories of a continuous time-dependent exposure variable measured with error. Naively ignoring the measurement error in the analysis may cause biased estimation and erroneous inference. Although various approaches have been proposed to deal with measurement error when the hazard depends linearly on the time-dependent variable, it has not yet been investigated how to correct when the hazard depends on the discretized categories of the time-dependent variable. To fill this gap in the literature, we propose a smoothed corrected score approach based on approximation of the discretized categories after smoothing the indicator function. The consistency and asymptotic normality of the proposed estimator are established. The observation times of the time-dependent variable are allowed to be informative. For comparison, we also extend to this setting two approximate approaches, the regression calibration and the risk-set regression calibration. The methods are assessed by simulation studies and by application to data from an HIV clinical trial.
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Affiliation(s)
- Xiao Song
- Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA
| | | | - Ching-Yun Wang
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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21
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Yang S, Zhang Y, Liu GF, Guan Q. SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale. Biometrics 2023; 79:230-240. [PMID: 34453313 PMCID: PMC8882199 DOI: 10.1111/biom.13555] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 08/20/2021] [Indexed: 11/30/2022]
Abstract
Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the nonparametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinical trial.
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Affiliation(s)
- Shu Yang
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | | | | | - Qian Guan
- Merck & Co., Inc., Kenilworth, New Jersey, USA
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22
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Yuki S, Tanioka K, Yadohisa H. Estimation and visualization of heterogeneous treatment effects for multiple outcomes. Stat Med 2023; 42:693-715. [PMID: 36574770 DOI: 10.1002/sim.9638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 12/12/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022]
Abstract
We consider two-arm comparison in clinical trials. The objective is to identify a population with characteristics that make the treatment effective. Such a population is called a subgroup. This identification can be made by estimating the treatment effect and identifying the interactions between treatments and covariates. For a single outcome, there are several ways available to identify the subgroups. There are also multiple outcomes, but they are difficult to interpret and cannot be applied to outcomes other than continuous values. In this paper, we thus propose a new method that allows for a straightforward interpretation of subgroups and deals with both continuous and binary outcomes. The proposed method introduces latent variables and adds Lasso sparsity constraints to the estimated loadings to facilitate the interpretation of the relationship between outcomes and covariates. The interpretation of the subgroups is made by visualizing treatment effects and latent variables. Since we are performing sparse estimation, we can interpret the covariates related to the treatment effects and subgroups. Finally, simulation and real data examples demonstrate the effectiveness of the proposed method.
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Affiliation(s)
- Shintaro Yuki
- Graduate School of Culture and Information Science, Doshisha University, Kyoto, Japan
| | - Kensuke Tanioka
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Hiroshi Yadohisa
- Department of Culture and Information Science, Doshisha University, Kyoto, Japan
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23
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Li Z, Chen J, Laber E, Liu F, Baumgartner R. Optimal Treatment Regimes: A Review and Empirical Comparison. Int Stat Rev 2023. [DOI: 10.1111/insr.12536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Affiliation(s)
- Zhen Li
- Department of Statistics North Carolina State University Raleigh 27607 NC USA
| | - Jie Chen
- Department of Biometrics Overland Pharmaceuticals Dover 19901 DE USA
| | - Eric Laber
- Department of Statistical Science, Department of Biostatistics and Bioinformatics Duke University Durham 27708 NC USA
| | - Fang Liu
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
| | - Richard Baumgartner
- Biostatistics and Research Decision Sciences Merck & Co., Inc. Kenilworth NJ 07033 USA
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24
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Li C, Li W, Zhu W. Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects. J Appl Stat 2023; 51:1151-1170. [PMID: 38628447 PMCID: PMC11018073 DOI: 10.1080/02664763.2023.2180167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 02/05/2023] [Indexed: 02/22/2023]
Abstract
The growing popularity of personalized medicine motivates people to explore individualized treatment regimes according to heterogeneous characteristics of the patients. For the large-scale data analysis, however, the data are collected at different times and different locations, i.e. subjects are usually from a heterogeneous population, which causes that the optimal treatment regimes also vary for patients across different subgroups. In this paper, we mainly focus on the estimation of optimal treatment regimes for subjects come from a heterogeneous population with high-dimensional data. We first remove the main effects of the covariates for each subgroup to eliminate non-ignorable residual confounding. Based on the centralized outcome, we propose a penalized robust learning that estimates the coefficient matrix of the interactions between covariates and treatment by penalizing pairwise differences of the coefficients of any two subgroups for the same covariate, which can automatically identify the latent complex structure of the coefficient matrix with heterogeneous and homogeneous columns. At the same time, the penalized robust learning can also select the important variables that truly contribute to the individualized treatment decisions with commonly used sparsity structure penalty. Extensive simulation studies show that our proposed method outperforms current popular methods, and it is further illustrated in the real analysis of the Tamoxifen breast cancer data.
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Affiliation(s)
- Canhui Li
- Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, People's Republic of China
| | - Weirong Li
- Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, People's Republic of China
| | - Wensheng Zhu
- Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, People's Republic of China
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25
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Choi T, Lee H, Choi S. Accountable survival contrast-learning for optimal dynamic treatment regimes. Sci Rep 2023; 13:2250. [PMID: 36755137 DOI: 10.1038/s41598-023-29106-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Dynamic treatment regime (DTR) is an emerging paradigm in recent medical studies, which searches a series of decision rules to assign optimal treatments to each patient by taking into account individual features such as genetic, environmental, and social factors. Although there is a large and growing literature on statistical methods to estimate optimal treatment regimes, most methodologies focused on complete data. In this article, we propose an accountable contrast-learning algorithm for optimal dynamic treatment regime with survival endpoints. Our estimating procedure is originated from a doubly-robust weighted classification scheme, which is a model-based contrast-learning method that directly characterizes the interaction terms between predictors and treatments without main effects. To reflect the censorship, we adopt the pseudo-value approach that replaces survival quantities with pseudo-observations for the time-to-event outcome. Unlike many existing approaches, mostly based on complicated outcome regression modeling or inverse-probability weighting schemes, the pseudo-value approach greatly simplifies the estimating procedure for optimal treatment regime by allowing investigators to conveniently apply standard machine learning techniques to censored survival data without losing much efficiency. We further explore a SCAD-penalization to find informative clinical variables and modified algorithms to handle multiple treatment options by searching upper and lower bounds of the objective function. We demonstrate the utility of our proposal via extensive simulations and application to AIDS data.
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26
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Yan Y, Ren M, de Leon A. Measurement error correction in mediation analysis under the additive hazards model. COMMUN STAT-SIMUL C 2023. [DOI: 10.1080/03610918.2023.2170412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Ying Yan
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Mingchen Ren
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
| | - Alexander de Leon
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
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27
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Ndashimye E, Reyes PS, Arts EJ. New antiretroviral inhibitors and HIV-1 drug resistance: more focus on 90% HIV-1 isolates? FEMS Microbiol Rev 2023; 47:fuac040. [PMID: 36130204 PMCID: PMC9841967 DOI: 10.1093/femsre/fuac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/13/2022] [Accepted: 09/18/2022] [Indexed: 01/21/2023] Open
Abstract
Combined HIV antiretroviral therapy (cART) has been effective except if drug resistance emerges. As cART has been rolled out in low-income countries, drug resistance has emerged at higher rates than observed in high income countries due to factors including initial use of these less tolerated cART regimens, intermittent disruptions in drug supply, and insufficient treatment monitoring. These socioeconomic factors impacting drug resistance are compounded by viral mechanistic differences by divergent HIV-1 non-B subtypes compared to HIV-1 subtype B that largely infects the high-income countries (just 10% of 37 million infected). This review compares the inhibition and resistance of diverse HIV-1 subtypes and strains to the various approved drugs as well as novel inhibitors in clinical trials. Initial sequence variations and differences in replicative fitness between HIV-1 subtypes pushes strains through different fitness landscapes to escape from drug selective pressure. The discussions here provide insight to patient care givers and policy makers on how best to use currently approved ART options and reduce the emergence of drug resistance in ∼33 million individuals infected with HIV-1 subtype A, C, D, G, and recombinants forms. Unfortunately, over 98% of the literature on cART resistance relates to HIV-1 subtype B.
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Affiliation(s)
- Emmanuel Ndashimye
- Department of Microbiology and Immunology, Western University Schulich School of Medicine & Dentistry, Western University, N6A 3K7, London, Ontario, Canada
- Joint Clinical Research Centre, -Center for AIDS Research Laboratories, 256, Kampala, Uganda
| | - Paul S Reyes
- Department of Microbiology and Immunology, Western University Schulich School of Medicine & Dentistry, Western University, N6A 3K7, London, Ontario, Canada
| | - Eric J Arts
- Department of Microbiology and Immunology, Western University Schulich School of Medicine & Dentistry, Western University, N6A 3K7, London, Ontario, Canada
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28
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Siriwardhana C, Kulasekera K, Datta S. Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures. J Appl Stat 2023; 51:891-912. [PMID: 38524800 PMCID: PMC10956931 DOI: 10.1080/02664763.2022.2164759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 12/29/2022] [Indexed: 01/11/2023]
Abstract
We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.
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Affiliation(s)
- Chathura Siriwardhana
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - K.B. Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
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29
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Ding J, Li J, Han Y, McKeague IW, Wang X. Fitting additive risk models using auxiliary information. Stat Med 2023; 42:894-916. [PMID: 36599810 DOI: 10.1002/sim.9649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 10/06/2022] [Accepted: 11/09/2022] [Indexed: 01/06/2023]
Abstract
There has been a growing interest in incorporating auxiliary summary information from external studies into the analysis of internal individual-level data. In this paper, we propose an adaptive estimation procedure for an additive risk model to integrate auxiliary subgroup survival information via a penalized method of moments technique. Our approach can accommodate information from heterogeneous data. Parameters to quantify the magnitude of potential incomparability between internal data and external auxiliary information are introduced in our framework while nonzero components of these parameters suggest a violation of the homogeneity assumption. We further develop an efficient computational algorithm to solve the numerical optimization problem by profiling out the nuisance parameters. In an asymptotic sense, our method can be as efficient as if all the incomparable auxiliary information is accurately acknowledged and has been automatically excluded from consideration. The asymptotic normality of the proposed estimator of the regression coefficients is established, with an explicit formula for the asymptotic variance-covariance matrix that can be consistently estimated from the data. Simulation studies show that the proposed method yields a substantial gain in statistical efficiency over the conventional method using the internal data only, and reduces estimation biases when the given auxiliary survival information is incomparable. We illustrate the proposed method with a lung cancer survival study.
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Affiliation(s)
- Jie Ding
- School of Mathematical Sciences, Dalian University of Technology, Liaoning, China
| | - Jialiang Li
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
- Duke University-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
| | - Yang Han
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
| | - Ian W McKeague
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, USA
| | - Xiaoguang Wang
- School of Mathematical Sciences, Dalian University of Technology, Liaoning, China
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30
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Gupta S, Granich R, Williams BG. Update on treatment as prevention of HIV illness, death, and transmission: sub-Saharan Africa HIV financing and progress towards the 95–95–95 target. Curr Opin HIV AIDS 2022; 17:368-373. [DOI: 10.1097/coh.0000000000000761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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31
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Pan A, Song X, Huang H. Bayesian analysis for partly linear Cox model with measurement error and time-varying covariate effect. Stat Med 2022; 41:4666-4681. [PMID: 35899596 PMCID: PMC9489624 DOI: 10.1002/sim.9531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 05/30/2022] [Accepted: 07/05/2022] [Indexed: 01/07/2023]
Abstract
The Cox proportional hazards model is commonly used to estimate the association between time-to-event and covariates. Under the proportional hazards assumption, covariate effects are assumed to be constant in the follow-up period of study. When measurement error presents, common estimation methods that adjust for an error-contaminated covariate in the Cox proportional hazards model assume that the true function on the covariate is parametric and specified. We consider a semiparametric partly linear Cox model that allows the hazard to depend on an unspecified function of an error-contaminated covariate and an error-free covariate with time-varying effect, which simultaneously relaxes the assumption on the functional form of the error-contaminated covariate and allows for nonconstant effect of the error-free covariate. We take a Bayesian approach and approximate the unspecified function by a B-spline. Simulation studies are conducted to assess the finite sample performance of the proposed approach. The results demonstrate that our proposed method has favorable statistical performance. The proposed method is also illustrated by an application to data from the AIDS Clinical Trials Group Protocol 175.
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Affiliation(s)
- Anqi Pan
- Department of Epidemiology and Biostatistics, College of Public HealthUniversity of GeorgiaAthensGeorgiaUSA
| | - Xiao Song
- Department of Epidemiology and Biostatistics, College of Public HealthUniversity of GeorgiaAthensGeorgiaUSA
| | - Hanwen Huang
- Department of Epidemiology and Biostatistics, College of Public HealthUniversity of GeorgiaAthensGeorgiaUSA
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32
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Wang J, Zeng D, Lin DY. Semiparametric single-index models for optimal treatment regimens with censored outcomes. Lifetime Data Anal 2022; 28:744-763. [PMID: 35939142 DOI: 10.1007/s10985-022-09566-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
There is a growing interest in precision medicine, where a potentially censored survival time is often the most important outcome of interest. To discover optimal treatment regimens for such an outcome, we propose a semiparametric proportional hazards model by incorporating the interaction between treatment and a single index of covariates through an unknown monotone link function. This model is flexible enough to allow non-linear treatment-covariate interactions and yet provides a clinically interpretable linear rule for treatment decision. We propose a sieve maximum likelihood estimation approach, under which the baseline hazard function is estimated nonparametrically and the unknown link function is estimated via monotone quadratic B-splines. We show that the resulting estimators are consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. The optimal treatment rule follows naturally as a linear combination of the maximum likelihood estimators of the model parameters. Through extensive simulation studies and an application to an AIDS clinical trial, we demonstrate that the treatment rule derived from the single-index model outperforms the treatment rule under the standard Cox proportional hazards model.
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Affiliation(s)
- Jin Wang
- Department of Biostatistics, University Of North Carolina, Chapel Hill, NC, United States
| | - Donglin Zeng
- Department of Biostatistics, University Of North Carolina, Chapel Hill, NC, United States
| | - D Y Lin
- Department of Biostatistics, University Of North Carolina, Chapel Hill, NC, United States.
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33
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Cho Y, Zhan X, Ghosh D. Nonlinear predictive directions in clinical trials. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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34
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Tang J, Yuan Y, Sun L, Wu B, Yu L. A study of the top-cited studies on drug therapy for HIV. Front Pharmacol 2022; 13:1007491. [PMID: 36120330 PMCID: PMC9473148 DOI: 10.3389/fphar.2022.1007491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Research on drug therapy for HIV remained major hot-spots, but relevant data were not satisfactory. We aimed to assess the status and trends of the most cited studies on drug therapy for HIV by using bibliometric methods.Methods The Web of Science Core Collection database was searched for the drug therapy for HIV studies. The period for retrieval was from the beginning of the database to July 26, 2022. The 100 top cited studies were selected. These general information and bibliometric data were collected and analyzed. VOS viewer software was used for visualization analysis.Results The number of citations for the 100 top cited studies ranged from 451 to 5597 and were published from 1987 to 2017. These studies were published in 29 journals. The top 3 journals in terms of the number of studies were the New England Journal of Medicine (n = 22), Lancet (n = 15), and JAMA (n = 13). The most frequently nominated author was Matthias Eiger from the University of Bern, who has contributed 5 studies. United States, Switzerland, and England contributed most of the highly cited studies. Research hot spots reflected clinical trials, treatment adverse events, basic research, and clinical adherence.Conclusion The majority of 100 top-cited studies have been published in the United States, and primarily focused on treatment adverse events, basic research, and clinical adherence. They provide a basic list of the most important and influential academic contributions to literature of HIV drug treatment for researchers.
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Affiliation(s)
- Jie Tang
- Editorial Board of Journal of Sichuan University (Medical Science Edition), Sichuan University, Chengdu, China
| | - Yanwen Yuan
- Editorial Board of Journal of Sichuan University (Medical Science Edition), Sichuan University, Chengdu, China
| | - Lei Sun
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Bo Wu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Lin Yu
- Editorial Board of Journal of Sichuan University (Medical Science Edition), Sichuan University, Chengdu, China
- *Correspondence: Lin Yu,
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35
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Xu HX, Fan GL, Wang JF. Jackknife empirical likelihood for the error variance in linear errors-in-variables models with missing data. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2020.1824274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Hong-Xia Xu
- Department of Mathematics, Shanghai Maritime University, Shanghai, China
| | - Guo-Liang Fan
- School of Economics and Management, Shanghai Maritime University, Shanghai, China
| | - Jiang-Feng Wang
- School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China
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36
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Zhao Y, Zhou H, Gu J, Ye H. Estimating the Individual Treatment Effect on Survival Time Based on Prior Knowledge and Counterfactual Prediction. Entropy 2022; 24:975. [PMID: 35885198 PMCID: PMC9322711 DOI: 10.3390/e24070975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/10/2022] [Accepted: 07/12/2022] [Indexed: 12/10/2022]
Abstract
The estimation of the Individual Treatment Effect (ITE) on survival time is an important research topic in clinics-based causal inference. Various representation learning methods have been proposed to deal with its three key problems, i.e., reducing selection bias, handling censored survival data, and avoiding balancing non-confounders. However, none of them consider all three problems in a single method. In this study, by combining the Counterfactual Survival Analysis (CSA) model and Dragonnet from the literature, we first propose a CSA–Dragonnet to deal with the three problems simultaneously. Moreover, we found that conclusions from traditional Randomized Controlled Trials (RCTs) or Retrospective Cohort Studies (RCSs) can offer valuable bound information to the counterfactual learning of ITE, which has never been used by existing ITE estimation methods. Hence, we further propose a CSA–Dragonnet with Embedded Prior Knowledge (CDNEPK) by formulating a unified expression of the prior knowledge given by RCTs or RCSs, inserting counterfactual prediction nets into CSA–Dragonnet and defining loss items based on the bounds for the ITE extracted from prior knowledge. Semi-synthetic data experiments showed that CDNEPK has superior performance. Real-world experiments indicated that CDNEPK can offer meaningful treatment advice.
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37
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Cai H, Lu W, Marceau West R, Mehrotra DV, Huang L. CAPITAL: Optimal subgroup identification via constrained policy tree search. Stat Med 2022; 41:4227-4244. [PMID: 35799329 PMCID: PMC9544117 DOI: 10.1002/sim.9507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 05/04/2022] [Accepted: 06/06/2022] [Indexed: 11/10/2022]
Abstract
Personalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicine is to identify a subgroup of patients, based on baseline covariates, that benefits more from the targeted treatment than other comparative treatments. Most of the current subgroup identification methods only focus on obtaining a subgroup with an enhanced treatment effect without paying attention to subgroup size. Yet, a clinically meaningful subgroup learning approach should identify the maximum number of patients who can benefit from the better treatment. In this article, we present an optimal subgroup selection rule (SSR) that maximizes the number of selected patients, and in the meantime, achieves the pre‐specified clinically meaningful mean outcome, such as the average treatment effect. We derive two equivalent theoretical forms of the optimal SSR based on the contrast function that describes the treatment‐covariates interaction in the outcome. We further propose a constrained policy tree search algorithm (CAPITAL) to find the optimal SSR within the interpretable decision tree class. The proposed method is flexible to handle multiple constraints that penalize the inclusion of patients with negative treatment effects, and to address time to event data using the restricted mean survival time as the clinically interesting mean outcome. Extensive simulations, comparison studies, and real data applications are conducted to demonstrate the validity and utility of our method.
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Affiliation(s)
- Hengrui Cai
- Department of Statistics, University of California Irvine, Irvine, California, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Rachel Marceau West
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, Pennsylvania, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, Pennsylvania, USA
| | - Lingkang Huang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
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38
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Bizuayehu SB, Xu J. Model-free screening for variables with treatment interaction. Stat Methods Med Res 2022; 31:1845-1859. [DOI: 10.1177/09622802221102624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Precision medicine is a medical paradigm that focuses on making effective treatment decision based on individual patient characteristics. When there are a large amount of patient information, such as patient’s genetic information, medical records and clinical measurements, available, it is of interest to select the covariates which have interactions with the treatment, for example, in determining the individualized treatment regime where only a subset of covariates with treatment interactions involves in decision making. We propose a marginal feature ranking and screening procedure for measuring interactions between the treatment and covariates. The method does not require imposing a specific model structure on the regression model and is applicable in a high dimensional setting. Theoretical properties in terms of consistency in ranking and selection are established. We demonstrate the finite sample performance of the proposed method by simulation and illustrate the applications with two real data examples from clinical trials.
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Affiliation(s)
| | - Jin Xu
- School of Statistics, East China Normal University, Shanghai, China
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, East China Normal University, Shanghai, China
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39
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Li L, Zhou N, Zhu L. Outcome regression-based estimation of conditional average treatment effect. ANN I STAT MATH. [DOI: 10.1007/s10463-022-00821-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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40
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Sun S, Yang Y, Wang L. Dimension-reduced empirical likelihood estimation and inference for M-estimators with nonignorable nonresponse. STATISTICS-ABINGDON 2022. [DOI: 10.1080/02331888.2022.2065677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Siying Sun
- School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China
| | - Yaohong Yang
- School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China
| | - Lei Wang
- School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China
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41
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Teira R, Diaz-cuervo H, Aragão F, Castaño M, Romero A, Roca B, Montero M, Galindo MJ, Muñoz-sánchez MJ, Espinosa N, Peraire J, Martínez E, de la Fuente B, Domingo P, Deig E, Merino MD, Geijo P, Estrada V, Sepúlveda MA, García J, Berenguer J, Currán A. Shorter Time to Discontinuation Due to Treatment Failure in People Living with HIV Switched to Dolutegravir Plus Either Rilpivirine or Lamivudine Compared with Integrase Inhibitor-Based Triple Therapy in a Large Spanish Cohort. Infect Dis Ther. [PMID: 35399147 PMCID: PMC9124284 DOI: 10.1007/s40121-022-00630-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 03/18/2022] [Indexed: 11/27/2022] Open
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42
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Qi Z, Pang JS, Liu Y. On Robustness of Individualized Decision Rules. J Am Stat Assoc 2022; 118:2143-2157. [PMID: 38143785 PMCID: PMC10746134 DOI: 10.1080/01621459.2022.2038180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/21/2022] [Indexed: 10/18/2022]
Abstract
With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize the expected outcome for each individual. Motivated by complex individualized decision making procedures and the popular conditional value at risk (CVaR) measure, we propose a new robust criterion to estimate optimal IDRs in order to control the average lower tail of the individuals' outcomes. In addition to improving the individualized expected outcome, our proposed criterion takes risks into consideration, and thus the resulting IDRs can prevent adverse events. The optimal IDR under our criterion can be interpreted as the decision rule that maximizes the "worst-case" scenario of the individualized outcome when the underlying distribution is perturbed within a constrained set. An efficient non-convex optimization algorithm is proposed with convergence guarantees. We investigate theoretical properties for our estimated optimal IDRs under the proposed criterion such as consistency and finite sample error bounds. Simulation studies and a real data application are used to further demonstrate the robust performance of our methods. Several extensions of the proposed method are also discussed.
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Affiliation(s)
- Zhengling Qi
- Department of Decision Sciences, George Washington University
| | - Jong-Shi Pang
- Department of Industrial and Systems Engineering, University of Southern California, LA
| | - Yufeng Liu
- Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, NC 27599, USA
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43
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Schinazi RF, Patel D, Ehteshami M. The best backbone for HIV prevention, treatment, and elimination: Emtricitabine+tenofovir. Antivir Ther 2022; 27:13596535211067599. [PMID: 35491570 DOI: 10.1177/13596535211067599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The advent of antiretroviral combination therapy has significantly impacted the HIV/AIDS epidemic. No longer a death sentence, HIV infection can be controlled and suppressed using cocktail therapies that contain two or more small molecule drugs. This review aims to highlight the discovery, development, and impact of one such molecule, namely, emtricitabine (FTC, emtriva), which is one of the most successful drugs in the fight against HIV/AIDS and has been taken by over 94% of individuals infected with HIV in the USA. We also pay tribute to Dr. John C. Martin, former CEO and Chairman of Gilead Sciences, who unexpectedly passed away in 2021. A true visionary, he was instrumental in delivering FTC, as part of combination therapy with TDF (tenofovir, viread) to the global stage. As the fight to eradicate HIV marches on, we honor Dr. Martin's legacy of collaboration, achievement, and hope.
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Affiliation(s)
- Raymond F Schinazi
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, 1371Emory University School of Medicine and Children Healthcare of Atlanta, Atlanta, GA, USA
| | - Dharmeshkumar Patel
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, 1371Emory University School of Medicine and Children Healthcare of Atlanta, Atlanta, GA, USA
| | - Maryam Ehteshami
- Center for AIDS Research, Laboratory of Biochemical Pharmacology, Department of Pediatrics, 1371Emory University School of Medicine and Children Healthcare of Atlanta, Atlanta, GA, USA
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44
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Su M, Wang Q. A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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45
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Wang H, Lu Z, Liu Y. Score test for missing at random or not under logistic missingness models. Biometrics 2022. [PMID: 35348206 DOI: 10.1111/biom.13666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 03/02/2022] [Accepted: 03/22/2022] [Indexed: 11/28/2022]
Abstract
Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct identification of the underlying missingness mechanism. Although the problem of testing whether this mechanism is MCAR or MAR has been extensively studied, there has been very little research on testing MAR versus MNAR. A critical challenge that is faced when dealing with this problem is the issue of model identification under MNAR. In this paper, under a logistic model for the missing probability, we develop two score tests for the problem of whether the missingness mechanism is MAR or MNAR under a parametric model and a semiparametric location model on the regression function. The implementation of the score tests circumvents the identification issue as it requires only parameter estimation under the null MAR assumption. Our simulations and analysis of human immunodeficiency virus data show that the score tests have well-controlled type I errors and desirable powers. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hairu Wang
- KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, 200062, China
| | - Zhiping Lu
- KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, 200062, China
| | - Yukun Liu
- KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, 200062, China
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46
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Ungolo F, van den Heuvel ER. Inference on latent factor models for informative censoring. Stat Methods Med Res 2022; 31:801-820. [PMID: 35077263 PMCID: PMC9014689 DOI: 10.1177/09622802211057290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This work discusses the problem of informative censoring in survival studies. A
joint model for the time to event and the time to censoring is presented. Their
hazard functions include a latent factor in order to identify this joint model
without sacrificing the flexibility of the parametric specification.
Furthermore, a fully Bayesian formulation with a semi-parametric proportional
hazard function is provided. Similar latent variable models have been described
in literature, but here the emphasis is on the performance of the inferential
task of the resulting mixture model with unknown number of components. The
posterior distribution of the parameters is estimated using Hamiltonian Monte
Carlo methods implemented in Stan. Simulation studies are provided to study its
performance and the methodology is implemented for the analysis of the ACTG175
clinical trial dataset yielding a better fit. The results are also compared to
the non-informative censoring case to show that ignoring informative censoring
may lead to serious biases.
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Affiliation(s)
- Francesco Ungolo
- Chair of Mathematical Finance, 9184Technical University of Munich, Garching bei München, Germany
| | - Edwin R van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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47
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Peng X, Wang HJ. A Generalized Quantile Tree Method for Subgroup Identification. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2032723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Xiang Peng
- Department of Statistics, George Washington University
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48
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Lüscher-Dias T, Siqueira Dalmolin RJ, de Paiva Amaral P, Alves TL, Schuch V, Franco GR, Nakaya HI. The evolution of knowledge on genes associated with human diseases. iScience 2022; 25:103610. [PMID: 35005554 PMCID: PMC8719018 DOI: 10.1016/j.isci.2021.103610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022] Open
Abstract
Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.
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Affiliation(s)
- Thomaz Lüscher-Dias
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Rodrigo Juliani Siqueira Dalmolin
- Bioinformatics Multidisciplinary Environment—BioME, IMD, Federal University of Rio Grande do Norte, Natal, RN, Brazil
- Department of Biochemistry, CB, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | | | - Tiago Lubiana Alves
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Viviane Schuch
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Glória Regina Franco
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Helder I. Nakaya
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
- Scientific Platform Pasteur-University of São Paulo, São Paulo, Brazil
- Hospital Israelita Albert Einstein, São Paulo, Brazil
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Meng H, Qiao X. Augmented direct learning for conditional average treatment effect estimation with double robustness. Electron J Stat 2022. [DOI: 10.1214/22-ejs2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Haomiao Meng
- Department of Mathematical Sciences, Binghamton University, State University of New York, Binghamton, NY 13902-6000, USA
| | - Xingye Qiao
- Department of Mathematical Sciences, Binghamton University, State University of New York, Binghamton, NY 13902-6000, USA
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50
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Chen LP, Yi GY. Sufficient dimension reduction for survival data analysis with error-prone variables. Electron J Stat 2022. [DOI: 10.1214/22-ejs1977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Li-Pang Chen
- Department of Statistics, National Chengchi University
| | - Grace Y. Yi
- Department of Statistical and Actuarial Sciences, Department of Computer Science, University of Western Ontario
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