Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials

Cited by (290)

Items 101–200 of 290

1. Exploration of heterogeneity of treatment effects across exercise-based interventions for knee osteoarthritis
   Paul A. Dennis, Livia Anderson, Cynthia J. Coffman, Sara Webb, Kelli D. Allen · 2025 · Osteoarthritis and Cartilage Open
2. Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study
   Eleanor Van Vogt, Anthony C. Gordon, Karla Diaz‐Ordaz, Suzie Cro · 2025 · BMC Medical Research Methodology
3. Survival analysis using a 5‐step stratified testing and amalgamation routine (5‐STAR) in randomized clinical trials
   Devan V. Mehrotra, Rachel Marceau West · 2020 · Statistics in Medicine
4. CAPITAL: Optimal subgroup identification via constrained policy tree search
   Hengrui Cai, Wenbin Lu, Rachel Marceau West, Devan V. Mehrotra, Lingkang Huang · 2022 · Statistics in Medicine
5. Markers of muscarinic deficit for individualized treatment in schizophrenia
   Heiner Stuke · 2023 · Frontiers in Psychiatry
6. Confident inference for SNP effects on treatment efficacy
   Ying Ding, Ying Grace Li, Yushi Liu, Stephen J. Ruberg, Jason C. Hsu · 2018 · The Annals of Applied Statistics
7. Methodological Insights on Biomarker‐Based Patient Selection: A Review of Scientific Advice Procedures at the European Medicines Agency
   Cynthia Huber, Joerg Zinserling, Norbert Benda, Thorsten Vetter, Marcia Viviane Rückbeil · 2025 · Clinical Pharmacology & Therapeutics
8. Explainable AI-driven precision clinical trial enrichment: demonstration of the NetraAI platform with a phase II depression trial
   Joseph Geraci, Bessi Qorri, Mike J. Tsay, Christian A. Cumbaa, Paul Leonczyk, Larry D Alphs, Elizabeth D. Ballard, Carlos A. Zarate, Luca Pani · 2025 · npj Digital Medicine
9. Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification
   Yang Zhao, Wei Zheng, Ying Daisy Zhuo, Yuefeng Lu, Xiwen Ma, Hengchang Liu, Zhen Zeng, Glen Laird · 2017 · Journal of Biopharmaceutical Statistics
10. Estimating heterogeneous treatment effects for latent subgroups in observational studies
    Hang J. Kim, Bo Lu, E. Nehus, Mi-Ok Kim · 2018 · Statistics in Medicine
11. Application of Heterogeneity of Treatment‐Effects Methods: Exploratory Analyses of a Trial of Exercise‐Based Interventions for Knee Osteoarthritis
    Cynthia J. Coffman, Liubov S Arbeeva, Todd A. Schwartz, Leigh F. Callahan, Yvonne M. Golightly, Adam P. Goode, Kim M. Huffman, Kelli D. Allen · 2021 · Arthritis Care & Research
12. Change plane model averaging for subgroup identification
    Pan Liu, Jialiang Li, Michael R. Kosorok · 2023 · Statistical Methods in Medical Research
13. Inference on tree‐structured subgroups with subgroup size and subgroup effect relationship in clinical trials
    Yuanhui Luo, Xinzhou Guo · 2023 · Statistics in Medicine
14. An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies
    David Dejardin, Anton Kraxner, Emilie Schindler, Nicolas Städler, Marcel Wolbers · 2024 · Frontiers in Immunology
15. High-dimensional iterative causal forest (hdiCF) for subgroup identification using health care claims data
    Tiansheng Wang, Virginia Pate, Richard Wyss, John B. Buse, Michael R. Kosorok, Til Stürmer‎ · 2024 · American Journal of Epidemiology
16. Subgroup identification using covariate‐adjusted interaction trees
    Jon Arni Steingrimsson, Jiabei Yang · 2019 · Statistics in Medicine
17. Optimal individualized decision rules from a multi-arm trial: A comparison of methods and an application to tailoring inter-donation intervals among blood donors in the UK
    Yuejia Xu, Angela Wood, Michael J. Sweeting, David J. Roberts, Brian D. M. Tom · 2020 · Statistical Methods in Medical Research
18. The study protocol: Neuroendocrinology and (epi-) genetics of female reproductive transition phase mood disorder - an observational, longitudinal study from pregnancy to postpartum
    Alexandra Johann, Ulrike Ehlert · 2020 · BMC Pregnancy and Childbirth
19. Subgroup causal effect identification and estimation via matching tree
    Yuyang Zhang, Patrick Schnell, Chi Wook Song, Bin Huang, Bo Lü · 2021 · Computational Statistics & Data Analysis
20. Optimal subgroup selection
    Henry W. J. Reeve, Timothy I. Cannings, Richard J. Samworth · 2023 · The Annals of Statistics
21. A two‐stage adaptive clinical trial design with data‐driven subgroup identification at interim analysis
    Sarah E Johnston, Ilya A. Lipkovich, Alex A. Dmitrienko, Yan Daniel Zhao · 2022 · Pharmaceutical Statistics
22. Inference on subgroups identified based on a heterogeneous treatment effect in a post hoc analysis of a clinical trial
    Beibo Zhao, Anastasia Ivanova, Jason P. Fine · 2023 · Clinical Trials
23. Cytokine Patterns as Predictors of Antibiotic Treatment Effect in Chronic Low Back Pain with Modic Changes: Subgroup Analyses of a Randomized Trial (AIM Study)
    Lars Christian Haugli Bråten, Elisabeth Gjefsen, Kristina Gervin, Are Hugo Pripp, Jan Sture Skouen, Elina Iordanova Schistad, Linda Margareth Pedersen, Monica Wigemyr, Kaja Kristine Selmer, Hans Christian D. Aass et al. · 2023 · Journal of Pain Research
24. Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs
    Alex A. Dmitrienko, Gautier Paux, Erik P. Pulkstenis, Jianliang Zhang · 2015 · Journal of Biopharmaceutical Statistics
25. Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity
    Konstantinos Sechidis, Cong Zhang, Sophie B. Sun, Yao Chen, Asher Spector, Björn Bornkamp · 2025 · Statistics in Medicine
26. Subgroup identification by recursive segmentation
    Alexander Hapfelmeier, Kurt Ulm, Bernhard Haller · 2018 · Journal of Applied Statistics
27. Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity
    Andreas Daniel Meid, Ana Isabel González‐González, Truc Sophia Dinh, Jeanet W. Blom, Marjan van den Akker, Petra J. M. Elders, Ulrich Thiem, Daniela Kuellenberg de Gaudry, Karin M. A. Swart, Henrik Rudolf et al. · 2021 · BMJ Open
28. Subgroup analysis for longitudinal data based on a partial linear varying coefficient model with a change plane
    Kecheng Wei, Guoyou Qin, Zhongyi Zhu · 2023 · Statistics in Medicine
29. Debiased inference on heterogeneous quantile treatment effects with regression rank scores
    Alexander Giessing, Jing-Shen Wang · 2023 · Journal of The Royal Statistical Society Series B-statistical Methodology
30. Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation
    Björn Bornkamp, Silvia Zaoli, Michela Azzarito, Ruvie Martin, Carsten Philipp Müller, Conor Moloney, Giulia Capestro, David I. Ohlssen, Mark Baillie · 2024 · Pharmaceutical Statistics
31. PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
    Oleg Sysoev, Krzysztof Bartoszek, Eva‐Charlotte Ekström, Katarina Ekholm Selling · 2019 · Statistics in Medicine
32. Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning
    Cynthia Huber, Norbert Benda, Tim Friede · 2021 · Advances in Data Analysis and Classification
33. On Discovering Treatment-Effect Modifiers Using Virtual Twins and Causal Forest ML in the Presence of Prognostic Biomarkers
    Erik Hermansson, David J. Svensson · 2021 · Lecture notes in computer science
34. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso
    Wencan Zhu, Céline Lévy‐Leduc, Nils Ternès · 2023 · BMC Bioinformatics
35. Practical guidance on modeling choices for the virtual twins method
    Chuyu Deng, Jack M. Wolf, David M. Vock, Dana Mowls Carroll, Jeffrey A. Boatman, Dorothy K. Hatsukami, Ning Leng, Joseph S. Koopmeiners · 2023 · Journal of Biopharmaceutical Statistics
36. Estimating individual benefits of medical or behavioral treatments in severely ill patients
    FRANCISCO JAVIER DÍAZ · 2017 · Statistical Methods in Medical Research
37. A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies
    Christoph Muysers, Alex A. Dmitrienko, Hermann Kulmann, Bodo Kirsch, Susanne Lippert, Thomas Schmelter, Anke Schulz, Nicole Mentenich, Heinz Schmitz, Matthias Schaefers et al. · 2019 · Therapeutic Innovation & Regulatory Science
38. Estimating the quality of optimal treatment regimes
    Aniek Sies, Iven Van Mechelen · 2019 · Statistics in Medicine
39. Change-Plane Analysis for Subgroup Detection with a Continuous Treatment
    Peng Jin, Wenbin Lu, Yu Chen, Mengling Liu · 2022 · Biometrics
40. Heterogeneous treatment effect of dose‐dense paclitaxel plus carboplatin therapy for advanced ovarian cancer
    Ayumi Taguchi, Kosuke Kato, Akiko Furusawa, Konan Hara, Kenbun Sone, Kyosuke Yamada, Hiroaki Kajiyama, Muneaki Shimada, Aikou Okamoto · 2024 · International Journal of Cancer
41. Optimized multiple testing procedures for nested sub-populations based on a continuous biomarker
    Alexandra Christine Graf, Dominic Magirr, Alex A. Dmitrienko, Martin Posch · 2020 · Statistical Methods in Medical Research
42. A platform for comparing subgroup identification methodologies
    Stephen J. Ruberg, Ying Zhang, Hollins Showalter, Lei Shen · 2023 · Biometrical Journal
43. Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects
    Ke Wan, Kensuke Tanioka, Toshio Shimokawa · 2024 · Statistics in Medicine
44. Learning Personalized Treatment Strategies with Predictive and Prognostic Covariates in Adaptive Clinical Trials
    Andres Alban, Stephen E. Chick, Spyros I. Zoumpoulis · 2025 · Management Science
45. Analyse en sous-groupes et Forest Plot : limites et intérêts
    Thomas Filleron, Julia Gilhodes, Jean‐Pierre Delord, Bastien Cabarrou · 2016 · Bulletin du Cancer
46. A new approach of subgroup identification for high-dimensional longitudinal data
    Mu Yue, Lei Huang · 2020 · Journal of Statistical Computation and Simulation
47. Survival analysis using a 5‐step stratified testing and amalgamation routine ( 5‐STAR ) in randomized clinical trials
    Devan V. Mehrotra, Rachel Marceau West · 2021 · Statistics in Medicine
48. Clinical trials, real-world evidence, and digital medicine
    Jim Weatherall, Faisal M. Khan, Mishal N. Patel, Richard Dearden, Khader Shameer, Glynn Dennis, Gabriela Feldberg, Thomas White, Sajan Khosla · 2021 · Elsevier eBooks
49. Selecting predictive biomarkers from genomic data
    Florian Frommlet, Piotr Szulc, Franz König, Małgorzata Bogdan · 2022 · PLoS ONE
50. Auxiliary variable–enriched biomarker‐stratified design
    Ting Wang, Xiaofei Wang, Haibo Zhou, Jianwen Cai, Stephen L. George · 2018 · Statistics in Medicine
51. Subgroup Discovery for Treatment Optimization
    Natalia Korepanova · 2018 · Kalpa publications in computing
52. WATCH : A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors
    Konstantinos Sechidis, Sophie B. Sun, Yao Chen, Jiarui Lu, Cong Zhang, Mark Baillie, David I. Ohlssen, Marc Vandemeulebroecke, Rob Hemmings, Stephen J. Ruberg et al. · 2024 · Pharmaceutical Statistics
53. A reference-free R-learner for treatment recommendation
    Junyi Zhou, Ying Zhang, Wanzhu Tu · 2022 · Statistical Methods in Medical Research
54. Risk Prediction Models for Frailty in Adult Maintenance Haemodialysis Patients: A Systematic Review and Methodological Appraisal
    Hong Zhang, Jing Li, Shi‐Yao Hu, Chan Liu, Meixun Liu, Yuanliang Xie, Shan Deng · 2025 · Journal of Advanced Nursing
55. Statistical Data Mining of Clinical Data
    Ilya A. Lipkovich, Bohdana Ratitch, Cristina Ivanescu · 2020 · Springer eBooks
56. Learning and confirming a class of treatment responders in clinical trials
    Victor B. Talisa, Chung‐Chou H. Chang · 2021 · Statistics in Medicine
57. Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing
    Yue Wei, Jason C. Hsu, Wei Chen, Emily Y. Chew, Ying Ding · 2021 · Statistics in Medicine
58. Heterogeneity of Treatment Effect in a Randomized Trial of a Communication Intervention
    Ann L. Jennerich, Lois Downey, Ruth A. Engelberg, Jared Randall Curtis · 2022 · Journal of Pain and Symptom Management
59. Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes
    Lara Maleyeff, Rui Wang, Sebastien Haneuse, Fan Li · 2023 · Statistics in Medicine
60. The Optimal Dynamic Treatment Rule SuperLearner: Considerations, Performance, and Application
    Lina Montoya, Mark J. van der Laan, Alex R. Luedtke, Jennifer L. Skeem, Jeremy Coyle, Maya L Petersen · 2021 · arXiv (Cornell University)
61. Preoperative nutritional support in children with Hirschsprung disease: a prospective multicenter open-label randomized controlled trial
    Hong-yi Zhang, Ke Gong Chen, Yanmin Zhang, Tingting Gao, Yuanyuan Huang, Liang Ge, Yuhang Yuan, Guofeng Zhang, Heying Yang, Hongxia Ren et al. · 2025 · Nature Communications
62. Heterogeneous treatment effect of immune checkpoint inhibitors by pretreatment prognosis in randomized controlled trials
    Lee Xingzhuo Li, Adel Shahnam, Ganessan Kichenadasse, Lewis Murray, Richard John Woodman, Ahmad Y. Abuhelwa, Natansh Deepak Modi, Andrew Rowland, Ashley Mark Hopkins, Michael Joseph Sorich · 2026 · JNCI Cancer Spectrum
63. Understanding and applying statistical methods in surgical research: a comprehensive guide for clinicians
    Jasmin Zeindler, Anas Taha, Florian Ponholzer, Vincent Ochs, Khoshimov Rakhmatillokhon, Savas Deniz Soysal, Otto Kollmar, Robert Rosenberg · 2026 · European surgery. Supplement/European surgery
64. Value Function Guided Subgroup Identification via Gradient Tree Boosting: A Framework to Handle Multiple Outcomes for Optimal Treatment Recommendation
    Pingye Zhang, Peng Liu, Junshui Ma, Yue Shentu · 2021 · Statistics in Biopharmaceutical Research
65. Heterogeneity of Treatment Effects Among Patients With Type 2 Diabetes and Elevated Body Mass Index in a Study Comparing Group Medical Visits Focused on Weight Management and Medication Intensification
    Elizabeth A. Kobe, Matthew J. Crowley, Amy S. Jeffreys, William S. Yancy, Jennifer Zervakis, David E. Edelman, Corrine I. Voils, Matthew L. Maciejewski, Cynthia J. Coffman · 2021 · Medical Care
66. Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials
    Wenxuan Xiong, Jason Roy, Hao Liu, Liangyuan Hu · 2024 · Contemporary Clinical Trials
67. Design and analysis considerations for investigating patient subgroups of interest within cancer clinical trials
    Gina L. Mazza, Eva Culakova, Danielle M. Enserro, James J. Dignam, Joseph M. Unger · 2025 · JNCI Monographs
68. Moderator Effects in Intervention Studies
    Heather Becker, Alexa M. Stuifbergen, Wenhui Zhang, Adam Sales · 2019 · Nursing Research
69. Assessing effect heterogeneity of a randomized treatment using conditional inference trees
    A. Venkatasubramaniam, Brandon Koch, Lauren Erickson, S. French, D. Vock, Julian A. Wolfson · 2019 · Statistical Methods in Medical Research
70. On evaluating how well a biomarker can predict treatment response with survival data
    Bassirou Mboup, Paul Blanche, Aurélien Latouche · 2020 · Pharmaceutical Statistics
71. REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction
    Zaheer Ahmed, Alberto Cassese, Gerard van Breukelen, Jan Schepers · 2021 · Advances in Data Analysis and Classification
72. Identifying Treatment Responders to Varenicline for Alcohol Use Disorder Using Two Machine-Learning Approaches
    Erica N. Grodin, Amanda Kay Montoya, Alondra Cruz, Suzanna Donato, Wave‐Ananda Baskerville, Lara A. Ray · 2023 · Clinical Psychological Science
73. All that Glitters Is not Gold: Type‐I Error Controlled Variable Selection from Clinical Trial Data
    Manuela R. Zimmermann, Mark Baillie, Matthías Kormáksson, David I. Ohlssen, Konstantinos Sechidis · 2024 · Clinical Pharmacology & Therapeutics
74. Protocols for Observational Studies: Methods and Open Problems
    Dylan S. Small · 2024 · Statistical Science
75. Practical and statistical aspects of subgroup analyses in surgical neuro-oncology: A comprehensive review from the PIONEER consortium
    Jasper K W Gerritsen, Philipp Karschnia, Jacob S. Young, Martin J. van den Bent, Susan Chang, Timothy R. Smith, Brian Vala Nahed, Jordina Rincón-Torroella, Chetan Bettegowda, Nader Sanai et al. · 2024 · Neuro-Oncology
76. Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data
    Pan Liu, Yaguang Li, Jialiang Li · 2025 · Biometrics
77. Key Aspects of Modern, Quantitative Drug Development
    Eric W. Gibson, Frank Bretz, Michael Looby, Björn Bornkamp · 2018 · Statistics in Biosciences
78. Qualitative treatment-subgroup interactions in the antidepressant treatment of major depression: Application of QUINT to individual participant data from seven placebo-controlled randomized controlled trials
    Kazushi Maruo, Toshi A. Furukawa, Hisashi Noma, Hissei Imai, Kazutaka Ikeda, Shigeto Yamawaki · 2020 · Personalized Medicine in Psychiatry
79. Efficient Screening of Predictive Biomarkers for Individual Treatment Selection
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80. Which patients benefit most from completing health risk assessments: comparing methods to identify heterogeneity of treatment effects
    Maren K. Olsen, Karen M. Stechuchak, Eugene Ž. Oddone, Laura J. Damschroder, Matthew L. Maciejewski · 2021 · Health Services and Outcomes Research Methodology
81. Data-Driven Subgroup Identification in Confirmatory Clinical Trials
    Pierre Bunouf, M. Groc, Alex A. Dmitrienko, Ilya A. Lipkovich · 2021 · Therapeutic Innovation & Regulatory Science
82. CauchyCP: A powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions
    Hong Zhang, Qing Li, Devan V. Mehrotra, Judong Shen · 2021 · Statistical Methods in Medical Research
83. [Subgroup identification based on accelerated failure time model combined with adaptive elastic net].
    Hongxia Wei, Pil Sung Kang, Yi Liu, Fangjun Huang, Zheng Chen, Shengli An · 2021 · PubMed Central
84. A Permutation Test for Assessing the Presence of Individual Differences in Treatment Effects
    Chi Chang, Thomas Jaki, Muhammad Saad Sadiq, Alena Kuhlemeier, Daniel J. Feaster, Cole, Nathan, Andrea E. Lamont, Daniel Leonard Oberski, Yasin Desai, Van Horn, M. Lee · 2019 · arXiv (Cornell University)
85. Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example
    Robin Anno Wester, Julian A. Rubel, Axel Mayer · 2021
86. LIFT - XAI: Leveraging Important Features in Treatment Effects to Inform Clinical Decision-Making via Explainable AI
    Mingyu Lu, Chanwoo Kim, Ian Covert, Nathan J. White, Su‐In Lee · 2024 · medRxiv
87. Best (but often forgotten) Statistical Practices: Measuring Real-World Intervention Effectiveness using Electronic Health Data
    Julian A. Wolfson, Ashwini Venkatasubramaniam · 2023 · American Journal of Clinical Nutrition
88. Treatment Durability of Injection Augmentation of Vocal Fold Atrophy
    Anumitha Venkatraman, Ruth J. Davis, Susan L. Thibeault · 2025 · Laryngoscope Investigative Otolaryngology
89. Common reporting errors in subgroup analysis: a comparison of interaction and stratified regression models
    Chingkwong Tsoi, Ran Sun · 2026 · BMC Medical Research Methodology
90. Sustainable Development of Leisure Tourism Agriculture in Guangzhou Based on Data Hierarchical Modeling
    Ning Wang, Jinyu Zhou, Liming Liu · 2021 · Journal of Physics Conference Series
91. Sparse Boosting Based Machine Learning Methods for High-Dimensional Data
    Mu Yue · 2021 · IntechOpen eBooks
92. Design Considerations and Analytical Framework for Reliably Identifying a Beneficial Individualized Treatment Rule
    Charles Cain, Thomas A. Murray, Kyle D. Rudser, Alexander J. Rothman, Anne C. Melzer, Anne Marie Joseph, David M. Vock · 2022 · Contemporary Clinical Trials
93. Estimating Optimal Individualized Treatment Rules with Multistate Processes
    Giorgos Bakoyannis · 2023 · Biometrics
94. Interactive identification of individuals with positive treatment effect while controlling false discoveries
    Boyan Duan, Larry Wasserman, Aaditya Ramdas · 2024 · Journal of Causal Inference
95. Identifying the Relationship Structure Among Multiple Datasets Using Independent Vector Analysis: Application to Multi-Task fMRI Data
    Isabell Lehmann, Tanuj Hasija, Ben Gabrielson, M. A. B. S. Akhonda, Vince D. Calhoun, Tülay Adalı · 2024 · IEEE Access
96. A novel high dimensional approach to assess heterogeneous treatment effect in claims data
    Tiansheng Wang, Virginia Pate, Richard Wyss, John B. Buse, Michael R. Kosorok, Til Stürmer‎ · 2025 · American Journal of Epidemiology
97. iGroup Learning and iDetect for Dynamic Anomaly Detection with Applications in Maritime Threat Detection
    Chencheng Cai, Rong Chen, Alexander D. Liu, Fred S. Roberts, Minge Xie · 2018 · IEEE Conference on Technologies for Homeland Security (HST)
98. Comparing internal and external validation in the discovery of qualitative treatment-subgroup effects using two small clinical trials
    Maren K. Olsen, Karen M. Stechuchak, Karen E. Steinhauser · 2019 · Contemporary Clinical Trials Communications
99. Biostatistics in Clinical Trials
    Olga Marchenko, Lisa Morrissey LaVange, Natallia Katenka · 2020 · Springer eBooks
100. Identifying individual predictive factors for treatment efficacy
     Ariel Alonso, Wim Van der Elst, Lizet Sánchez, Patricia Lorenzo-Luaces, Geert Molenberghs · 2020 · Biometrics