1
|
Johnson D, Lu W, Davidian M. A general framework for subgroup detection via one-step value difference estimation. Biometrics 2023; 79:2116-2126. [PMID: 35793474 PMCID: PMC10694635 DOI: 10.1111/biom.13711] [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: 08/04/2021] [Accepted: 06/15/2022] [Indexed: 11/29/2022]
Abstract
Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time-to-event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.
Collapse
Affiliation(s)
- Dana Johnson
- United Therapeutics Corp., Research Triangle Park, Durham, North Carolina, USA
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Marie Davidian
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| |
Collapse
|
2
|
Jin P, Lu W, Chen Y, Liu M. Change-plane analysis for subgroup detection with a continuous treatment. Biometrics 2023; 79:1920-1933. [PMID: 36134534 PMCID: PMC10030385 DOI: 10.1111/biom.13762] [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: 11/29/2021] [Accepted: 09/14/2022] [Indexed: 11/30/2022]
Abstract
Detecting and characterizing subgroups with differential effects of a binary treatment has been widely studied and led to improvements in patient outcomes and population risk management. Under the setting of a continuous treatment, however, such investigations remain scarce. We propose a semiparametric change-plane model and consequently a doubly robust test statistic for assessing the existence of two subgroups with differential treatment effects under a continuous treatment. The proposed testing procedure is valid when either the baseline function for the covariate effects or the generalized propensity score function for the continuous treatment is correctly specified. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established. When the null hypothesis of no subgroup is rejected, the change-plane parameters that define the subgroups can be estimated. This paper provides a unified framework of the change-plane method to handle various types of outcomes, including the exponential family of distributions and time-to-event outcomes. Additional extensions with nonparametric estimation approaches are also provided. We evaluate the performance of our proposed methods through extensive simulation studies under various scenarios. An application to the Health Effects of Arsenic Longitudinal Study with a continuous environmental exposure of arsenic is presented.
Collapse
Affiliation(s)
- Peng Jin
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, U.S.A
| | - Yu Chen
- Division of Epidemiplogy, Department of Population Health, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
| | - Mengling Liu
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York 10016, U.S.A
| |
Collapse
|
3
|
Wang W, Sun Y, Wang HJ. Latent group detection in functional partially linear regression models. Biometrics 2023; 79:280-291. [PMID: 34482542 DOI: 10.1111/biom.13557] [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: 06/27/2020] [Revised: 08/06/2021] [Accepted: 08/19/2021] [Indexed: 11/28/2022]
Abstract
In this paper, we propose a functional partially linear regression model with latent group structures to accommodate the heterogeneous relationship between a scalar response and functional covariates. The proposed model is motivated by a salinity tolerance study of barley families, whose main objective is to detect salinity tolerant barley plants. Our model is flexible, allowing for heterogeneous functional coefficients while being efficient by pooling information within a group for estimation. We develop an algorithm in the spirit of the K-means clustering to identify latent groups of the subjects under study. We establish the consistency of the proposed estimator, derive the convergence rate and the asymptotic distribution, and develop inference procedures. We show by simulation studies that the proposed method has higher accuracy for recovering latent groups and for estimating the functional coefficients than existing methods. The analysis of the barley data shows that the proposed method can help identify groups of barley families with different salinity tolerant abilities.
Collapse
Affiliation(s)
- Wu Wang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China
| | - Ying Sun
- Statistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Huixia Judy Wang
- Department of Statistics, The George Washington University, Washington, DC, USA
| |
Collapse
|
4
|
Liu P, Li J, Kosorok MR. Change plane model averaging for subgroup identification. Stat Methods Med Res 2023; 32:773-788. [PMID: 36775991 DOI: 10.1177/09622802231154327] [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: 02/14/2023]
Abstract
Central to personalized medicine and tailored therapies is discovering the subpopulations that account for treatment effect heterogeneity and are likely to benefit more from given interventions. In this article, we introduce a change plane model averaging method to identify subgroups characterized by linear combinations of predictive variables and multiple cut-offs. We first fit a sequence of statistical models, each incorporating the thresholding effect of one particular covariate. The estimation of submodels is accomplished through an iterative integration of a change point detection method and numerical optimization algorithms. A frequentist model averaging approach is then employed to linearly combine the submodels with optimal weights. Our approach can deal with high-dimensional settings involving enormous potential grouping variables by adopting the sparsity-inducing penalties. Simulation studies are conducted to investigate the prediction and subgrouping performance of the proposed method, with a comparison to various competing subgroup detection methods. Our method is applied to a dataset from a warfarin pharmacogenetics study, producing some new findings.
Collapse
Affiliation(s)
- Pan Liu
- Department of Statistics and Data Science, 37580National University of Singapore, Singapore, Singapore
| | - Jialiang Li
- Department of Statistics and Data Science, 37580National University of Singapore, Singapore, Singapore.,Duke University NUS Graduate Medical School, Singapore, Singapore
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA
| |
Collapse
|
5
|
Liu P, Li J. Segment regression model average with multiple threshold variables and multiple structural breaks. CAN J STAT 2023. [DOI: 10.1002/cjs.11764] [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: 02/08/2023]
Affiliation(s)
- Pan Liu
- Department of Statistics and Applied Probability National University of Singapore Singapore
| | - Jialiang Li
- Department of Statistics and Applied Probability National University of Singapore Singapore
| |
Collapse
|
6
|
Zhou J, Zhang Y, Tu W. A reference-free R-learner for treatment recommendation. Stat Methods Med Res 2023; 32:404-424. [PMID: 36540907 DOI: 10.1177/09622802221144326] [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: 12/24/2022]
Abstract
Assigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.
Collapse
Affiliation(s)
- Junyi Zhou
- Design and Inovation, 7129Amgen Inc., Thousand Oaks, CA, USA
| | - Ying Zhang
- Department of Biostatistics, 12284University of Nebraska Medical Center, Omaha, NE, USA
| | - Wanzhu Tu
- Department of Biostatistics and Health Data Science, Indiana University-School of Medicine and Fairbanks School of Public Health, Indianapolis, IN, USA
| |
Collapse
|
7
|
Chizuk HM, Cunningham A, Horn EC, Thapar RS, Willer BS, Leddy JJ, Haider MN. Association of Concussion History and Prolonged Recovery in Youth. Clin J Sport Med 2022; 32:e573-e579. [PMID: 35533140 PMCID: PMC9633345 DOI: 10.1097/jsm.0000000000001044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/28/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To determine the number of prior concussions associated with increased incidence of persistent postconcussive symptoms (PPCS) in a cohort of acutely concussed pediatric patients. DESIGN Prospective observational cohort study. SETTING Three university-affiliated concussion clinics. PARTICIPANTS Two hundred seventy participants (14.9 ± 1.9 years, 62% male, 54% with prior concussion) were assessed within 14 days of concussion and followed to clinical recovery. Participants with a second head injury before clinical recovery were excluded. MEASURES AND MAIN OUTCOME Concussion history, current injury characteristics, recovery time, and risk for prolonged recovery from current concussion. RESULTS There was no statistically significant change in PPCS risk for participants with 0, 1 or 2 prior concussions; however, participants with 3 or more prior concussions had a significantly greater risk of PPCS. Twelve participants sustained a subsequent concussion after clinical recovery from their first injury and were treated as a separate cohort. Our secondary analysis found that these participants took longer to recover and had a greater incidence of PPCS during recovery from their latest concussion. CONCLUSION Pediatric patients with a history of 3 or more concussions are at greater risk of PPCS than those with fewer than 3 prior concussions.
Collapse
Affiliation(s)
- Haley M Chizuk
- UBMD Orthopedics and Sports Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
- Department of Rehabilitation Sciences, School of Public Health and Health Professions, University at Buffalo, SUNY, Buffalo, New York; and
| | - Adam Cunningham
- UBMD Orthopedics and Sports Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - Emily C Horn
- UBMD Orthopedics and Sports Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - Raj S Thapar
- UBMD Orthopedics and Sports Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - Barry S Willer
- Department of Psychiatry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - John J Leddy
- UBMD Orthopedics and Sports Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
| | - Mohammad N Haider
- UBMD Orthopedics and Sports Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York
| |
Collapse
|
8
|
Ge X, Peng Y, Tu D. A generalized single‐index linear threshold model for identifying treatment‐sensitive subsets based on multiple covariates and longitudinal measurements. CAN J STAT 2022. [DOI: 10.1002/cjs.11737] [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: 11/07/2022]
Affiliation(s)
- Xinyi Ge
- Department of Mathematics and Statistics Queen's University Kingston Ontario Canada
| | - Yingwei Peng
- Departments of Mathematics and Statistics & Public Health Sciences Queen's University Kingston Ontario Canada
| | - Dongsheng Tu
- Departments of Mathematics and Statistics & Public Health Sciences and Canadian Cancer Trials Group Queen's University Kingston Ontario Canada
| |
Collapse
|
9
|
Wang B, Li J, Wang X. Multi-threshold proportional hazards model and subgroup identification. Stat Med 2022; 41:5715-5737. [PMID: 36198478 DOI: 10.1002/sim.9589] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 07/13/2021] [Revised: 07/22/2022] [Accepted: 09/19/2022] [Indexed: 11/09/2022]
Abstract
We propose a novel two-stage procedure for change point detection and parameter estimation in a multi-threshold proportional hazards model. In the first stage, we estimate the number of thresholds by formulating the threshold detection problem as a variable selection problem and applying the penalized partial likelihood approach. In the second stage, the change point locations are refined by a grid search and the standard inference for segment regression can then follow. The proposed model and estimation procedure could lend support to subgroup identification and personalized treatment recommendation in medical research. We establish the consistency of the threshold estimators and regression coefficient estimators under technical conditions. The finite sample performance of the method is demonstrated via simulation studies and two cancer data examples.
Collapse
Affiliation(s)
- Bing Wang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Jialiang Li
- Department of Statistics and Data Science, National University of Singapore, Singapore.,Duke University NUS Graduate Medical School, National University of Singapore, Singapore
| | - Xiaoguang Wang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China.,Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian University of Technology, Dalian, Liaoning, China
| |
Collapse
|
10
|
Kulasekera KB, Tholkage S, Kong M. Personalized treatment selection using observational data. J Appl Stat 2022; 50:1115-1127. [PMID: 37009593 PMCID: PMC10062224 DOI: 10.1080/02664763.2021.2019689] [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: 10/19/2022]
Abstract
Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the 'best' outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.
Collapse
Affiliation(s)
- K. B. Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Sudaraka Tholkage
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| | - Maiying Kong
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA
| |
Collapse
|
11
|
Venkatasubramaniam A, Koch B, Erickson L, French S, Vock D, Wolfson J. Assessing effect heterogeneity of a randomized treatment using conditional inference trees. Stat Methods Med Res 2021; 31:549-562. [PMID: 34747281 DOI: 10.1177/09622802211052831] [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: 11/17/2022]
Abstract
Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, that is, the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. Treatment effect heterogeneity trees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population.
Collapse
Affiliation(s)
| | - Brandon Koch
- School of Community Health Sciences, 6851University of Nevada, Reno, USA
| | - Lauren Erickson
- 51441HealthPartners Institute for Education and Research, Minnesota, USA
| | - Simone French
- Division of Epidemiology and Community Health, School of Public Health, 43353University of Minnesota, Minneapolis, USA
| | - David Vock
- Division of Biostatistics, School of Public Health, 43353University of Minnesota, Minneapolis, USA
| | - Julian Wolfson
- Division of Biostatistics, School of Public Health, 43353University of Minnesota, Minneapolis, USA
| |
Collapse
|
12
|
Wang W, Zhu Z. Group structure detection for a high‐dimensional panel data model. CAN J STAT 2021. [DOI: 10.1002/cjs.11646] [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: 11/08/2022]
Affiliation(s)
- Wu Wang
- Center for Applied Statistics and School of Statistics Renmin University of China Beijing China
| | - Zhongyi Zhu
- Department of Statistics Fudan University Shanghai China
| |
Collapse
|
13
|
She Z, Li D, Zhang W, Zhou N, Xi J, Ju K. Three Versions of the Perceived Stress Scale: Psychometric Evaluation in a Nationally Representative Sample of Chinese Adults during the COVID-19 Pandemic. Int J Environ Res Public Health 2021; 18:ijerph18168312. [PMID: 34444061 PMCID: PMC8391348 DOI: 10.3390/ijerph18168312] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 01/05/2023]
Abstract
(1) Background: The COVID-19 outbreak has created pressure in people’s daily lives, further threatening public health. Thus, it is important to assess people’s perception of stress during COVID-19 for both research and practical purposes. The Perceived Stress Scale (PSS) is one of the most widely used instruments to measure perceived stress; however, previous validation studies focused on specific populations, possibly limiting the generalization of results. (2) Methods: This study tested the psychometric properties of three versions of the Chinese Perceived Stress Scale (CPSS-14, CPSS-10, and CPSS-4) in the Chinese general population during the COVID-19 pandemic. A commercial online survey was employed to construct a nationally representative sample of 1133 adults in Mainland China (548 males and 585 females) during a one-week period. (3) Results: The two-factor (positivity and negativity) solution for the three versions of the CPSS showed a good fit with the data. The CPSS-14 and CPSS-10 had very good reliability and the CPSS-4 showed acceptable reliability, supporting the concurrent validity of the CPSS. (4) Conclusions: All three versions of the CPSS appear to be appropriate for use in research with samples of adults in the Chinese general population under the COVID-19 crisis. The CPSS-10 and CPSS-14 both have strong psychometric properties, but the CPSS-10 would have more utility because it is shorter than the CPSS-14. However, the CPSS-4 is an acceptable alternative when administration time is limited.
Collapse
Affiliation(s)
- Zhuang She
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; (Z.S.); (D.L.); (N.Z.); (K.J.)
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| | - Dan Li
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; (Z.S.); (D.L.); (N.Z.); (K.J.)
- Shanghai Changning Mental Health Center, Shanghai 200335, China
- Mental Health Center, Hainan Medical University, Haikou 571199, China
| | - Wei Zhang
- Mental Health Center, Wuhan Polytechnic University, Wuhan 430074, China;
| | - Ningning Zhou
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; (Z.S.); (D.L.); (N.Z.); (K.J.)
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| | - Juzhe Xi
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; (Z.S.); (D.L.); (N.Z.); (K.J.)
- Shanghai Changning Mental Health Center, Shanghai 200335, China
- Correspondence:
| | - Kang Ju
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China; (Z.S.); (D.L.); (N.Z.); (K.J.)
- Shanghai Changning Mental Health Center, Shanghai 200335, China
| |
Collapse
|
14
|
Lin ZM, Chen JF, Xu FT, Liu CM, Chen JS, Wang Y, Zhang C, Huang PT. Principal component regression-based contrast-enhanced ultrasound evaluation system for the management of BI-RADS US 4A breast masses: objective assistance for radiologists. Ultrasound Med Biol 2021; 47:1737-1746. [PMID: 33838937 DOI: 10.1016/j.ultrasmedbio.2021.02.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 02/23/2021] [Accepted: 02/26/2021] [Indexed: 06/12/2023]
Abstract
A portion of detected breast masses might be overrated by using the Breast Imaging-Reporting and Data System ultrasonography (BI-RADS US) lexicon. A principal component regression-based contrast-enhanced ultrasound (PCR-CEUS) evaluation system was built to quantitatively illustrate whether CEUS could help radiologists to differentiate 4A masses. The PCR-CEUS evaluation system, based on principal component analysis (PCA) and logistic regression, was verified by random assignment into training and test sets and shown to reduce the data dimension and avoid collinearity in CEUS variables. This prospective study consecutively collected 238 patients with 238 4A masses confirmed pathologically. All enrolled patients accepted CEUS examination. The diagnostic performance of senior and junior radiologists, PCR-CEUS and combined methods was compared. The PCR-CEUS system had consistent diagnostic performance in both the training and test sets, with an area under the curve (AUC) of 0.831 (0.765-0.897), 0.798 (0.7034-0.892) and 0.854 (0.765-0.943) (all P > 0.05). The AUC of the combined diagnostic model (PCR-CEUS + Senior radiologists) was higher than that of senior radiologists, and the combined model had higher sensitivity (0.875 (0.781-0.969) vs. 0.729 (0.603-0.855)) without compromising specificity. Furthermore, the AUC and specificity of the combined model (PCR-CEUS + Junior radiologists) (0.852 (0.787-0.916)) was higher than that of junior radiologists (0.665 (0.592-0.737) (P < 0.00001)). PCR-CEUS demonstrated good ability in differentiating malignant BI-RADS-US 4A masses and was helpful for both senior and junior radiologists.
Collapse
Affiliation(s)
- Zi-Mei Lin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Ji-Fan Chen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Fang-Ting Xu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Chun-Mei Liu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Jian-She Chen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yao Wang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Chao Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Pin-Tong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China; Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
| |
Collapse
|
15
|
Wan K, Tanioka K, Minami H, Mizuta M, Shimokawa T. Hybrid adaptive index model for binary response data. Jpn J Stat Data Sci 2021; 4:299-315. [DOI: 10.1007/s42081-020-00097-6] [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: 10/23/2022]
|
16
|
Li J, Li Y, Jin B, Kosorok MR. Multithreshold change plane model: Estimation theory and applications in subgroup identification. Stat Med 2021; 40:3440-3459. [PMID: 33843100 DOI: 10.1002/sim.8976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 06/22/2020] [Revised: 01/06/2021] [Accepted: 03/21/2021] [Indexed: 11/05/2022]
Abstract
We propose a multithreshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping variable is a linear function of observed covariates and thus multiple thresholds produce change planes in the covariate space. We contribute a novel two-stage estimation approach to determine the number of subgroups, the location of thresholds, and all other regression parameters. In the first stage we adopt a group selection principle to consistently identify the number of subgroups, while in the second stage change point locations and model parameter estimates are refined by a penalized induced smoothing technique. Our procedure allows sparse solutions for relatively moderate- or high-dimensional covariates. We further establish the asymptotic properties of our proposed estimators under appropriate technical conditions. We evaluate the performance of the proposed methods by simulation studies and provide illustrations using two medical data examples. Our proposal for subgroup identification may lead to an immediate application in personalized medicine.
Collapse
Affiliation(s)
- Jialiang Li
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.,Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore.,Singapore Eye Research Institute, Singapore, Singapore
| | - Yaguang Li
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Baisuo Jin
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Michael R Kosorok
- Department of Biotatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| |
Collapse
|