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Gao F, Luo J, Liu J, Wan F, Wang G, Gordon M, Xiong C. Comparing statistical methods in assessing the prognostic effect of biomarker variability on time-to-event clinical outcomes. BMC Med Res Methodol 2022; 22:201. [PMID: 35869438 PMCID: PMC9308219 DOI: 10.1186/s12874-022-01686-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
In recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability.
Methods
A joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model, and regression calibration) were illustrated using data from a large multi-center randomized phase III trial, the Ocular Hypertension Treatment Study (OHTS), regarding the association between the variability of intraocular pressure (IOP) and the development of primary open-angle glaucoma (POAG). The model performance was also evaluated in terms of bias using simulated data from the joint model of longitudinal IOP and time to POAG. The parameters for simulation were chosen after OHTS data, and the association between longitudinal and survival data was introduced via underlying, unobserved, and error-free parameters including subject-specific variance.
Results
In the OHTS data, joint modeling and two-stage methods reached consistent conclusion that IOP variability showed no significant association with the risk of POAG. In the simulated data with no association between IOP variability and time-to-POAG, all the two-stage methods (except the naïve approach) provided a reliable estimation. When a moderate effect of IOP variability on POAG was imposed, all the two-stage methods underestimated the true association as compared with the joint modeling while the model-based two-stage method (regression calibration) resulted in the least bias.
Conclusion
Regression calibration and joint modelling are the preferred methods in assessing the effect of biomarker variability. Two-stage methods with sample-based measures should be used with caution unless there exists a relatively long series of longitudinal measurements and/or strong effect size (NCT00000125).
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A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-021-00566-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Pietrosanu M, Kong L, Yuan Y, Bell RC, Letourneau N, Jiang B. Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. ENTROPY 2022; 24:e24020232. [PMID: 35205525 PMCID: PMC8871134 DOI: 10.3390/e24020232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/26/2022] [Accepted: 01/29/2022] [Indexed: 11/16/2022]
Abstract
Despite the importance of maternal gestational weight gain, it is not yet conclusively understood how weight gain during different stages of pregnancy influences health outcomes for either mother or child. We partially attribute this to differences in and the validity of statistical methods for the analysis of longitudinal and scalar outcome data. In this paper, we propose a Bayesian joint regression model that estimates and uses trajectory parameters as predictors of a scalar response. Our model remedies notable issues with traditional linear regression approaches found in the clinical literature. In particular, our methodology accommodates nonprospective designs by correcting for bias in self-reported prestudy measures; truly accommodates sparse longitudinal observations and short-term variation without data aggregation or precomputation; and is more robust to the choice of model changepoints. We demonstrate these advantages through a real-world application to the Alberta Pregnancy Outcomes and Nutrition (APrON) dataset and a comparison to a linear regression approach from the clinical literature. Our methods extend naturally to other maternal and infant outcomes as well as to areas of research that employ similarly structured data.
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Affiliation(s)
- Matthew Pietrosanu
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (M.P.); (L.K.)
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (M.P.); (L.K.)
| | - Yan Yuan
- School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada;
| | - Rhonda C. Bell
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, Canada;
| | - Nicole Letourneau
- Faculty of Nursing and Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (M.P.); (L.K.)
- Correspondence:
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Jiang B, Raftery AE, Steele RJ, Wang N. Balancing Inferential Integrity and Disclosure Risk Via Model Targeted Masking and Multiple Imputation. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2021.1909597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | | | - Russell J. Steele
- Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
| | - Naisyin Wang
- Department of Statistics, University of Michigan, Ann Arbor, MI
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Barbieri A, Legrand C. Joint longitudinal and time-to-event cure models for the assessment of being cured. Stat Methods Med Res 2019; 29:1256-1270. [PMID: 31213153 DOI: 10.1177/0962280219853599] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Medical time-to-event studies frequently include two groups of patients: those who will not experience the event of interest and are said to be "cured" and those who will develop the event and are said to be "susceptible". However, the cure status is unobserved in (right-)censored patients. While most of the work on cure models focuses on the time-to-event for the uncured patients (latency) or on the baseline probability of being cured or not (incidence), we focus in this research on the conditional probability of being cured after a medical intervention given survival until a certain time. Assuming the availability of longitudinal measurements collected over time and being informative on the risk to develop the event, we consider joint models for longitudinal and survival data given a cure fraction. These models include a linear mixed model to fit the trajectory of longitudinal measurements and a mixture cure model. In simulation studies, different shared latent structures linking both submodels are compared in order to assess their predictive performance. Finally, an illustration on HIV patient data completes the comparison.
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Affiliation(s)
- Antoine Barbieri
- Institute of Statistics, Biostatistics and Actuarial Sciences, ISBA/LIDAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium.,INSERM, UMR1219, Univ. Bordeaux, Bordeaux, France
| | - Catherine Legrand
- Institute of Statistics, Biostatistics and Actuarial Sciences, ISBA/LIDAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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Jiang B, Petkova E, Tarpey T, Ogden RT. LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS. Ann Appl Stat 2017; 11:1513-1536. [PMID: 29152032 PMCID: PMC5687521 DOI: 10.1214/17-aoas1044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Latent class models are widely used to identify unobserved subgroups (i.e., latent classes) based upon one or more manifest variables. The probability of belonging to each subgroup is typically modeled as a function of a set of measured covariates. In this paper, we extend existing latent class models to incorporate matrix covariates. This research is motivated by a randomized placebo-controlled depression clinical trial. One study goal is to identify a subgroup of subjects who experience symptoms improvement early on during antidepressant treatment, which is considered to be an indication of a placebo rather than a true pharmacological response. We want to relate the likelihood of belonging to this subgroup of early responders to baseline electroencephalography (EEG) measurement that takes the form of a matrix. The proposed method is built upon a low rank Candecomp/Parafac (CP) decomposition of the target coefficient matrix through low-dimensional latent variables, which effectively reduces the model dimensionality. We adopt a Bayesian hierarchical modeling approach to estimate the latent variables, which allows a flexible way to incorporate prior knowledge about covariate effect heterogeneity and offers a data-driven method of regularization. Simulation studies suggest that the proposed method is robust against potentially misspecified rank in the CP decomposition. With the motivating example we show how the proposed method can be applied to extract valuable information from baseline EEG measurements that explains the likelihood of belonging to the early responder subgroup, helping to identify placebo responders and suggesting new targets for the study of placebo response.
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Affiliation(s)
| | - Eva Petkova
- New York University
- Nathan S. Kline Institute for Psychiatric Research
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Jiang B, Wang N, Sammel MD, Elliott MR. Modeling Short- and Long-Term Characteristics of Follicle Stimulating Hormone as Predictors of Severe Hot Flashes in Penn Ovarian Aging Study. J R Stat Soc Ser C Appl Stat 2015; 64:731-753. [PMID: 26538769 DOI: 10.1111/rssc.12102] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
The Penn Ovarian Aging Study tracked a population-based sample of 436 women aged 35-47 years to determine associations between reproductive hormone levels and menopausal symptoms. We develop a joint modeling method that uses the individual-level longitudinal measurements of follicle stimulating hormone (FSH) to predict the risk of severe hot flashes in a manner that distinguishes long-term trends of the mean trajectory, cumulative changes captured by the derivative of mean trajectory, and short-term residual variability. Our method allows the potential effects of longitudinal trajectories on the health risks to vary and accumulate over time. We further utilize the proposed methods to narrow the critical time windows of increased health risks. We find that high residual variation of FSH is a strong predictor of hot flash risk, and that the high cumulative changes of the FSH mean trajectories in the 52.5-55 year age range also provides evidence of increased risk above and beyond that of short-term FSH residual variation by itself.
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Affiliation(s)
- Bei Jiang
- Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Naisyin Wang
- Department of Statistics, University of Michigan, Ann Arbor, USA ; Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Mary D Sammel
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA
| | - Michael R Elliott
- Department of Biostatistics, University of Michigan, Ann Arbor, USA ; Survey Methodology Program, Institute for Social Research, University of Michigan, Ann Arbor, USA
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