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

Lipkovich, Ilya; Dmitrienko, Alex; B., Ralph

doi:10.1002/sim.7064

Cited by 291Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesStatistical Methods and Inference

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

Lipkovich, Ilya, Dmitrienko, Alex, B., Ralph·2017·Statistics in Medicine

DOI 10.1002/sim.7064

Open this paper in CoChat — free account

Abstract

It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data‐driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety. Limitations of ad‐hoc approaches to biomarker exploration and subgroup identification in clinical trials are discussed, and the ad‐hoc approaches are contrasted with principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining. A general framework for evaluating predictive biomarkers and identification of associated subgroups is introduced. The tutorial provides a review of a broad class of statistical methods used in subgroup discovery, including global outcome modeling methods, global treatment effect modeling methods, optimal treatment regimes, and local modeling methods. Commonly used subgroup identification methods are illustrated using two case studies based on clinical trials with binary and survival endpoints. Copyright © 2016 John Wiley & Sons, Ltd.

Integrity signals

Registry

Crossref · 10.1002/sim.7064

Cites

101 of 101 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Penalisation methods such as ridge, LASSO, and elastic net generally lead to smaller absolute values of coefficients (shrink coefficients towards zero) compared with maximum likelihood estimation.
“Penalisation methods such as ridge, LASSO (least absolute shrinkage and selection operator), and elastic net generally lead to smaller absolute values of the coefficients—that is, they shrink coefficients towards zero—compared with maximum likelihood estimation.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [72]

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
APACHE II
Knaus, W. A., Draper, E. A., Wagner, D. P., & Zimmerman, J. E. (1985) · Critical Care Medicine · doi:10.1097/00003246-198510000-00009 · Cited by 15,333 · see citing works
APACHE II-A Severity of Disease Classification System
Knaus, W. A., Draper, E. A., Wagner, D. P., & Zimmerman, J. E. (1986) · Critical Care Medicine · doi:10.1097/00003246-198608000-00028 · Cited by 13,379 · see citing works
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648 · see citing works
Least angle regression
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357 · see citing works
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470 · see citing works
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261 · see citing works
Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884 · see citing works
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184 · see citing works
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021 · see citing works
The Bayesian Lasso
Park, T., & Casella, G. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000337 · Cited by 2,941 · see citing works
Sparsity and Smoothness Via the Fused Lasso
Tibshirani, R. J., Saunders, M. A., Rosset, S., Zhu, J., & Knight, K. (2004) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2005.00490.x · Cited by 2,643 · see citing works
Stability Selection
Meinshausen, N., & Bühlmann, P. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00740.x · Cited by 2,314 · see citing works
Estimating the error rate of a prediction rule: improvement on cross-validation
Efron, B. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477973 · Cited by 2,300 · see citing works
A weakly informative default prior distribution for logistic and other regression models
Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y.-S. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas191 · Cited by 2,115 · see citing works
BART: Bayesian additive regression trees
Chipman, H. A., George, E. I., & McCulloch, R. E. (2010) · The Annals of Applied Statistics · doi:10.1214/09-aoas285 · Cited by 1,875 · see citing works
Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling
Royston, P. J., & Altman, D. G. (1994) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2986270 · Cited by 1,843 · see citing works
A WEAKLY INFORMATIVE DEFAULT PRIOR DISTRIBUTION FOR LOGISTIC AND OTHER REGRESSION MODELS
Gelman, A., Jakulin, A., & Grazia, M. (2009) · Cited by 1,352 · see citing works
Why We (Usually) Don't Have to Worry About Multiple Comparisons
Gelman, A., Hill, J., & Yajima, M. (2012) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2011.618213 · Cited by 1,274 · see citing works
Subgroup analysis in randomised controlled trials: importance, indications, and interpretation
Rothwell, P. M. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17709-5 · Cited by 942 · see citing works
SPLIT SELECTION METHODS FOR CLASSIFICATION TREES
Loh, W., & Shih, Y. (1997) · Cited by 933 · see citing works
BART: Bayesian Additive Regression Trees
Chipman, H., George, E. I., & McCulloch, R. E. (2006) · ScholarlyCommons (University of Pennsylvania) · Cited by 889 · see citing works
Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses
Sun, X., Briel, M., Walter, S. D., & Guyatt, G. H. (2010) · BMJ · doi:10.1136/bmj.c117 · Cited by 808 · see citing works
Model-Based Recursive Partitioning
Zeileis, A., Hothorn, T., & Hornik, K. (2008) · Journal of Computational and Graphical Statistics · doi:10.1198/106186008x319331 · Cited by 771 · see citing works
Estimating Individualized Treatment Rules Using Outcome Weighted Learning
Zhao, Y., Zeng, D., Rush, A. J., & Kosorok, M. R. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2012.695674 · Cited by 746 · see citing works
Bayesian CART Model Search
Chipman, H., George, E. I., & McCulloch, R. E. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473750 · Cited by 738 · see citing works
Causal Effects in Clinical and Epidemiological Studies Via Potential Outcomes: Concepts and Analytical Approaches
Little, R. J., & Rubin, D. B. (2000) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.21.1.121 · Cited by 721 · cited 1× · see citing works
“This requires the so-called ‘stable unit treatment value assumption’, which implies that: (i) the treatment status of any patient does not affect the potential outcomes of the other patients; and (ii) there is no hidden variation in the treatment (such as if some patients were taking adjunct medication that may affect…”
Background
The Entire Regularization Path for the Support Vector Machine
Hastie, T. J., Rosset, S., Tibshirani, R. J., & Zhu, J. (2004) · Cited by 617 · see citing works
Bump hunting in high-dimensional data
Friedman, J. H., & Fisher, N. I. (1999) · Statistics and Computing · doi:10.1023/a:1008894516817 · Cited by 605 · see citing works
Performance guarantees for individualized treatment rules
Qian, M., & Murphy, S. A. (2011) · The Annals of Statistics · doi:10.1214/10-aos864 · Cited by 581 · see citing works
Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives
Brookes, S. T., Whitley, E., Peters, T. J., Mulheran, P. A., Egger, M., & Davey Smith, G. (2001) · Health Technology Assessment · doi:10.3310/hta5330 · Cited by 580 · see citing works
Subgroup identification from randomized clinical trial data
Foster, J. C., Taylor, J. M. G., & Ruberg, S. J. (2011) · Statistics in Medicine · doi:10.1002/sim.4322 · Cited by 541 · see citing works
A significance test for the lasso
Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R. (2014) · The Annals of Statistics · doi:10.1214/13-aos1175 · Cited by 541 · see citing works
Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment.
Thomas, G. E., Westfall, P. H., & Young, S. S. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348369 · Cited by 515 · see citing works
A lasso for hierarchical interactions
Bien, J., Taylor, J., & Tibshirani, R. J. (2013) · The Annals of Statistics · doi:10.1214/13-aos1096 · Cited by 500 · see citing works
Estimating treatment effect heterogeneity in randomized program evaluation
Imai, K., & Ratkovic, M. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas593 · Cited by 487 · see citing works
Piecewise linear regularized solution paths
Rosset, S., & Zhu, J. (2007) · The Annals of Statistics · doi:10.1214/009053606000001370 · Cited by 484 · see citing works
p -Values for High-Dimensional Regression
Meinshausen, N., Meier, L., & Bühlmann, P. (2009) · Journal of the American Statistical Association · doi:10.1198/jasa.2009.tm08647 · Cited by 447 · see citing works
A Robust Method for Estimating Optimal Treatment Regimes
Zhang, B., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2012) · Biometrics · doi:10.1111/j.1541-0420.2012.01763.x · Cited by 422 · see citing works
A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
Tian, L., Alizadeh, A. A., Gentles, A. J., & Tibshirani, R. J. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.951443 · Cited by 378 · see citing works
Survival Trees by Goodness of Split
LeBlanc, M. L., & Crowley, J. J. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476296 · Cited by 374 · see citing works
REGRESSION TREES WITH UNBIASED VARIABLE SELECTION AND INTERACTION DETECTION
Loh, W. (2002) · Cited by 370 · see citing works
Adaptive Signature Design: An Adaptive Clinical Trial Design for Generating and Prospectively Testing A Gene Expression Signature for Sensitive Patients
Freidlin, B., & Simon, R. M. (2005) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-05-0605 · Cited by 351 · see citing works
Subgroup Analysis via Recursive Partitioning
Su, X., Tsai, C., Wang, H., Nickerson, D. M., Li, B., & Rosset, S. (2009) · Cited by 315 · see citing works
Subgroup identification based on differential effect search—A recursive partitioning method for establishing response to treatment in patient subpopulations
Lipkovich, I. A., Dmitrienko, A. A., Denne, J. S., & Enas, G. G. (2011) · Statistics in Medicine · doi:10.1002/sim.4289 · Cited by 282 · see citing works
Self-citation
A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials
Royston, P. J., & Sauerbrei, W. (2004) · Statistics in Medicine · doi:10.1002/sim.1815 · Cited by 281 · see citing works
Learning Interactions via Hierarchical Group-Lasso Regularization
Lim, M., & Hastie, T. J. (2014) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.938812 · Cited by 276 · see citing works
Reinforcement learning design for cancer clinical trials
Zhao, Y., Kosorok, M. R., & Zeng, D. (2009) · Statistics in Medicine · doi:10.1002/sim.3720 · Cited by 273 · see citing works
Analysis of randomized comparative clinical trial data for personalized treatment selections
Cai, T., Tian, L., Wong, P. H., & Wei, L. J. (2010) · Biostatistics · doi:10.1093/biostatistics/kxq060 · Cited by 239 · see citing works
Estimating optimal treatment regimes from a classification perspective
Zhang, B., Tsiatis, A. A., Davidian, M., Zhang, M., & Laber, E. B. (2012) · Stat · doi:10.1002/sta.411 · Cited by 234 · see citing works
Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data
Simon, R. M., Subramanian, J., Li, M., & Menezes, S. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr001 · Cited by 228 · see citing works
$\mathbf{Q}$- and $\mathbf{A}$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Schulte, P. J., Tsiatis, A. A., Laber, E. B., & Davidian, M. (2014) · Statistical Science · doi:10.1214/13-sts450 · Cited by 214 · see citing works
Adaptive enrichment designs for clinical trials
Simon, N., & Simon, R. (2013) · Biostatistics · doi:10.1093/biostatistics/kxt010 · Cited by 202 · see citing works
Tree-based methods for individualized treatment regimes
Laber, E. B., & Zhao, Y. (2015) · Biometrika · doi:10.1093/biomet/asv028 · Cited by 193 · see citing works
The Cross-Validated Adaptive Signature Design
Freidlin, B., Jiang, W., & Simon, R. M. (2010) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-09-1357 · Cited by 189 · see citing works
A regression tree approach to identifying subgroups with differential treatment effects
Loh, W., He, X., & Man, M. Z. (2015) · Statistics in Medicine · doi:10.1002/sim.6454 · Cited by 187 · see citing works
Effectively Selecting a Target Population for a Future Comparative Study
Zhao, L., Tian, L., Cai, T., Claggett, B. L., & Wei, L. (2013) · Journal of the American Statistical Association · doi:10.1080/01621459.2013.770705 · Cited by 180 · see citing works
Variable selection for optimal treatment decision
Lu, W., Zhang, H. H., & Zeng, D. (2011) · Statistical Methods in Medical Research · doi:10.1177/0962280211428383 · Cited by 170 · see citing works
Traditional multiplicity adjustment methods in clinical trials
Dmitrienko, A. A., & D’Agostino, R. B. (2013) · Statistics in Medicine · doi:10.1002/sim.5990 · Cited by 153 · see citing works
Self-citation
Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model
Shen, J., & He, X. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.894763 · Cited by 146 · see citing works
A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research
Varadhan, R., Segal, J. B., Boyd, C. M., Wu, A. W., & Weiss, C. O. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.02.009 · Cited by 146 · see citing works
Bayesian Subset Analysis
Dixon, D. O., & Simon, R. M. (1991) · Biometrics · doi:10.2307/2532645 · Cited by 143 · see citing works
Subgroup Analysis via Recursive PartitioningPreprint
Su, X., Tsai, C., Wang, H., Nickerson, D. M., & Li, B. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1341380 · Cited by 143 · see citing works
Qualitative interaction trees: a tool to identify qualitative treatment–subgroup interactions
Dusseldorp, E., & Mechelen, I. V. (2013) · Statistics in Medicine · doi:10.1002/sim.5933 · Cited by 130 · see citing works
Interaction Trees with Censored Survival Data
Su, X., Zhou, T., Yan, X., Fan, J., & Song, Y. (2008) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1071 · Cited by 108 · see citing works
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes
Zhang, Y., Laber, E. B., Tsiatis, A. A., & Davidian, M. (2015) · Biometrics · doi:10.1111/biom.12354 · Cited by 105 · see citing works
A Bayesian Approach to Subgroup Identification
Berger, J. O., Wang, X., & Shen, L. (2014) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2013.856026 · Cited by 104 · see citing works
Bayesian models for subgroup analysis in clinical trials
Jones, H. E., Ohlssen, D. I., Neuenschwander, B. E., Racine, A., & Branson, M. (2011) · Clinical Trials · doi:10.1177/1740774510396933 · Cited by 103 · see citing works
Evaluating Markers for Selecting a Patient's Treatment
Song, X., & PEPE, M. S. (2004) · Biometrics · doi:10.1111/j.0006-341x.2004.00242.x · Cited by 94 · see citing works
Strategies for Identifying Predictive Biomarkers and Subgroups with Enhanced Treatment Effect in Clinical Trials Using SIDES
Lipkovich, I. A., & Dmitrienko, A. A. (2014) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2013.856024 · Cited by 93 · see citing works
Self-citation
The mean does not mean as much anymore: finding sub-groups for tailored therapeutics
Ruberg, S. J., Chen, L., & Wang, Y. (2010) · Clinical Trials · doi:10.1177/1740774510369350 · Cited by 80 · see citing works
Combining an Additive and Tree-Based Regression Model Simultaneously: STIMA
Dusseldorp, E., Conversano, C., & van Os, B. J. (2010) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2010.06089 · Cited by 80 · see citing works
Combining biomarkers to optimize patient treatment recommendations
Kang, C., Janes, H. E., & Huang, Y. (2014) · Biometrics · doi:10.1111/biom.12191 · Cited by 78 · see citing works
Regularized outcome weighted subgroup identification for differential treatment effects
Xu, Y., Yu, M., Zhao, Y., Li, Q., Wang, S., & Shao, J. (2015) · Biometrics · doi:10.1111/biom.12322 · Cited by 69 · see citing works
Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria
Negassa, A., Ciampi, A., Abrahamowicz, M., Shapiro, S. H., & Boivin, J. (2005) · Statistics and Computing · doi:10.1007/s11222-005-1311-z · Cited by 66 · see citing works
Personalized Medicine: Four Perspectives of Tailored Medicine
Ruberg, S. J., & Shen, L. (2015) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2015.1059354 · Cited by 61 · see citing works
Assessing Treatment‐Selection Markers using a Potential Outcomes Framework
Huang, Y., Gilbert, P. B., & Janes, H. E. (2012) · Biometrics · doi:10.1111/j.1541-0420.2011.01722.x · Cited by 59 · see citing works
Responder identification in clinical trials with censored data
Kehl, V., & Ulm, K. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.11.015 · Cited by 58 · see citing works
General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials
Dmitrienko, A., Muysers, C., Fritsch, A., & Lipkovich, I. (2016) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2015.1092033 · Cited by 51 · see citing works
Self-citation
A PRIM approach to predictive‐signature development for patient stratification
Chen, G., Zhong, J., Belousov, A., & Devanarayan, V. (2014) · Statistics in Medicine · doi:10.1002/sim.6343 · Cited by 50 · see citing works
Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies
Fu, H., Zhou, J. J., & Faries, D. E. (2016) · Statistics in Medicine · doi:10.1002/sim.6920 · Cited by 45 · see citing works
Bayesian two-step Lasso strategy for biomarker selection in personalized medicine development for time-to-event endpoints
Gu, X., Yin, G., & Lee, J. J. (2013) · Contemporary Clinical Trials · doi:10.1016/j.cct.2013.09.009 · Cited by 42 · see citing works
Variable Selection for Qualitative Interactions in Personalized Medicine While Controlling the Family-Wise Error Rate
Gunter, L., Zhu, J., & Murphy, S. A. (2011) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2011.608052 · Cited by 41 · see citing works
Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials
Xu, Y., Trippa, L., Müller, P., & Ji, Y. (2014) · Statistics in Biosciences · doi:10.1007/s12561-014-9117-1 · Cited by 41 · see citing works
A Bayesian subgroup analysis with a zero‐enriched Polya Urn scheme
Sivaganesan, S., Laud, P. W., & Müller, P. (2010) · Statistics in Medicine · doi:10.1002/sim.4108 · Cited by 39 · see citing works
Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development
Gu, X., Chen, N., Wei, C., Liu, S., Papadimitrakopoulou, V. A., Herbst, R. S., & Lee, J. J. (2014) · Statistics in Biosciences · doi:10.1007/s12561-014-9124-2 · Cited by 37 · see citing works
Identifying optimal biomarker combinations for treatment selection via a robust kernel method
Huang, Y., & Fong, Y. (2014) · Biometrics · doi:10.1111/biom.12204 · Cited by 29 · see citing works
Smoothing Balanced Single-Error-Term Analysis of Variance
Hodges, J. S., Cui, Y., Sargent, D. J., & Carlin, B. P. (2007) · Technometrics · doi:10.1198/004017006000000408 · Cited by 28 · see citing works
Clinical and Statistical Considerations in Personalized Medicine
Carini, C., Menon, S., & Chang, M. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b16749 · Cited by 25 · see citing works
Recent development on statistical methods for personalized medicine discovery
Zhao, Y., & Zeng, D. (2013) · Frontiers of Medicine · doi:10.1007/s11684-013-0245-7 · Cited by 24 · see citing works
Simple subgroup approximations to optimal treatment regimes from randomized clinical trial data
Foster, J. C., Taylor, J. M. G., Kaciroti, N., & Nan, B. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu049 · Cited by 21 · see citing works
Understanding Heterogeneity in Response to Antidiabetes Treatment: A Post Hoc Analysis Using SIDES, a Subgroup Identification Algorithm
Hardin, D. S., Rohwer, R. D., Curtis, B. H., Zagar, A. J., Chen, L., Boye, K. S., Jiang, H. H., & Lipkovich, I. A. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700219 · Cited by 17 · see citing works
Self-citation
Survey Results on Industry Practices and Challenges in Subgroup Analysis in Clinical Trials
Mayer, C., Lipkovich, I. A., & Dmitrienko, A. A. (2015) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2015.1062044 · Cited by 16 · see citing works
Self-citation
Biomarker Evaluation and Subgroup Identification in a Pneumonia Development Program Using SIDES
Dmitrienko, A. A., Lipkovich, I. A., Hopkins, A., Li, Y., & Wang, W. (2015) · ICSA book series in statistics · doi:10.1007/978-3-319-12694-4_26 · Cited by 7 · see citing works
Self-citation

Flagged first, then by citations

Sorted by citations

Development of the instrument to assess the credibility of effect modification analyses (ICEMAN) in randomized controlled trials and meta-analyses
Schandelmaier, S., Briel, M., Varadhan, R., Schmid, C. H., Devasenapathy, N., Hayward, R. A., Gagnier, J., Borenstein, M., van der Heijden, G. J. M. G., Dahabreh, I. J., et al. (2020) · Canadian Medical Association Journal · doi:10.1503/cmaj.200077 · Cited by 749 · see citing works
Developing clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
Kent, D. M., Steyerberg, E. W., & Klaveren, D. V. (2018) · BMJ · doi:10.1136/bmj.k4245 · Cited by 487 · see citing works
The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement
Kent, D. M., Paulus, J. K., van Klaveren, D., D'Agostino, R., Goodman, S., Hayward, R., Ioannidis, J. P. A., Patrick-Lake, B., Morton, S., Pencina, M., et al. (2020) · Annals of Internal Medicine · doi:10.7326/m18-3667 · Cited by 469 · see citing works
Self-citation
Quasi-experimental design
Maciejewski, M. L. (2018) · Biostatistics & Epidemiology · doi:10.1080/24709360.2018.1477468 · Cited by 344 · see citing works
The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration
Kent, D. M., Klaveren, D. V., Paulus, J. K., D’Agostino, R. B., Goodman, S. N., Hayward, R. A., Ioannidis, J. P. A., Patrick‐Lake, B., Morton, S. C., Pencina, M. J., et al. (2019) · Annals of Internal Medicine · doi:10.7326/m18-3668 · Cited by 177 · see citing works
Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning
Riley, R. D., Debray, T. P. A., Fisher, D. J., Hattle, M., Marlin, N., Hoogland, J., Gueyffier, F., Staessen, J. A., Wang, J., Moons, K. G. M., et al. (2020) · Statistics in Medicine · doi:10.1002/sim.8516 · Cited by 179 · see citing works
Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review
Al-Tashi, Q., Saad, M. B., Muneer, A., Qureshi, R., Mirjalili, S., Sheshadri, A., Le, X., Vokes, N. I., Zhang, J. J., & Wu, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097781 · Cited by 152 · see citing works
Multiplicity Considerations in Clinical Trials
Dmitrienko, A., & D’Agostino, R. B. (2018) · New England Journal of Medicine · doi:10.1056/nejmra1709701 · Cited by 140 · see citing works
Self-citation
Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder
Luedtke, A., Sadikova, E., & Kessler, R. C. (2019) · Clinical Psychological Science · doi:10.1177/2167702618815466 · Cited by 143 · see citing works
Statistical Considerations for Subgroup Analyses
Xiaofei, W., Piantadosi, S., Le‐Rademacher, J. G., & Mandrekar, S. J. (2020) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2020.12.008 · Cited by 127 · see citing works
Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review
Ondra, T., Dmitrienko, A. A., Friede, T., Graf, A. C., Miller, F., Stallard, N., & Posch, M. (2015) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2015.1092034 · Cited by 122 · see citing works
Self-citation
Distinguishing prognostic and predictive biomarkers: an information theoretic approach
Sechidis, K., Papangelou, K., Metcalfe, P., Svensson, D. J., Weatherall, J., & Gavin Brown, B. (2018) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bty357 · Cited by 107 · see citing works
The potential of predictive analytics to provide clinical decision support in depression treatment planning
Kessler, R. C. (2018) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000377 · Cited by 103 · see citing works
Individualized treatment effects with censored data via fully nonparametric Bayesian accelerated failure time models
Henderson, N. C., Louis, T. A., Rosner, G. L., & Varadhan, R. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy028 · Cited by 81 · see citing works
Machine learning methods for developing precision treatment rules with observational data
Kessler, R. C., Bossarte, R. M., Luedtke, A. R., Zaslavsky, A. M., & Zubizarreta, J. R. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103412 · Cited by 84 · see citing works
Subgroup identification for precision medicine: A comparative review of 13 methods
Loh, W., Cao, L., & Zhou, P. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1326 · Cited by 64 · see citing works
General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials
Dmitrienko, A., Muysers, C., Fritsch, A., & Lipkovich, I. (2016) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2015.1092033 · Cited by 51 · see citing works
Self-citation
Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples
Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93087-x · Cited by 51 · see citing works
Dismantling, personalising and optimising internet cognitive–behavioural therapy for depression: a study protocol for individual participant data component network meta-analysis
Furukawa, T. A., Karyotaki, E., Suganuma, A. M., Pompoli, A., Ostinelli, E. G., Cipriani, A., Cuijpers, P. J., & Efthimiou, O. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-026137 · Cited by 46 · see citing works
Adaptive Enrichment Designs in Clinical Trials
Thall, P. F. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-040720-032818 · Cited by 44 · see citing works
Latent Profile/Class Analysis Identifying Differentiated Intervention Effects
Yang, Q., Zhao, A., Lee, C., Wang, X., Vorderstrasse, A. A., & Wolever, R. Q. (2022) · Nursing Research · doi:10.1097/nnr.0000000000000597 · Cited by 45 · see citing works
Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: a scoping review
Inoue, K., Adomi, M., Efthimiou, O., Komura, T., Omae, K., Ōnishi, A., Tsutsumi, Y., Fujii, T., Kondo, N., & Furukawa, T. A. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111538 · Cited by 44 · see citing works
Subgroup identification in clinical trials via the predicted individual treatment effect
Ballarini, N. M., Rosenkranz, G. K., Jaki, T., König, F., & Posch, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205971 · Cited by 44 · see citing works
Optimized adaptive enrichment designs
Ondra, T., Jobjörnsson, S., Beckman, R. A., Burman, C., König, F., Stallard, N., & Posch, M. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280217747312 · Cited by 42 · see citing works
Random forests of interaction trees for estimating individualized treatment effects in randomized trials
Su, X., Peña, A. T., Liu, L., & Levine, R. A. (2018) · Statistics in Medicine · doi:10.1002/sim.7660 · Cited by 42 · see citing works
Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology
Halabi, S., Li, C., & Luo, S. (2019) · JCO Precision Oncology · doi:10.1200/po.19.00068 · Cited by 42 · see citing works
Multiplicity considerations in subgroup analysis
Dmitrienko, A. A., Millen, B. A., & Lipkovich, I. A. (2017) · Statistics in Medicine · doi:10.1002/sim.7416 · Cited by 39 · see citing works
Self-citation
Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration
Zhang, Z., Chen, L., Sun, B., Ruan, Z., Pan, P., Zhang, W., Jiang, X., Zheng, S., Cheng, S., Xian, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53239-9 · Cited by 37 · see citing works
A Causal Framework for Making Individualized Treatment Decisions in Oncology
Msaouel, P., Lee, J., Karam, J. A., & Thall, P. F. (2022) · Cancers · doi:10.3390/cancers14163923 · Cited by 37 · see citing works
Design and estimation in clinical trials with subpopulation selection
Chiu, Y., König, F., Posch, M., & Jaki, T. (2018) · Statistics in Medicine · doi:10.1002/sim.7925 · Cited by 33 · see citing works
A comparative study of subgroup identification methods for differential treatment effect: Performance metrics and recommendations
Alemayehu, D., Chen, Y., & Markatou, M. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280217710570 · Cited by 32 · see citing works
Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
Lipkovich, I. A., Svensson, D. J., Ratitch, B., & Dmitrienko, A. A. (2024) · Statistics in Medicine · doi:10.1002/sim.10167 · Cited by 33 · see citing works
Self-citation
Multithreshold change plane model: Estimation theory and applications in subgroup identification
Li, J., Li, Y., Jin, B., & Kosorok, M. R. (2021) · Statistics in Medicine · doi:10.1002/sim.8976 · Cited by 32 · see citing works
Nothing wrong about change: the adequate choice of the dependent variable and design in prediction of cognitive training success
Mattes, A., & Roheger, M. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01176-8 · Cited by 31 · see citing works
Use of large-scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery
Gutacker, N., & Street, A. (2017) · Quality of Life Research · doi:10.1007/s11136-017-1599-0 · Cited by 30 · see citing works
Toward an Understanding of Adversarial Examples in Clinical Trials
Papangelou, K., Sechidis, K., Weatherall, J., & Gavin Brown, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_3 · Cited by 27 · see citing works
Generalised linear model trees with global additive effects
Seibold, H., Hothorn, T., & Zeileis, A. (2018) · Advances in Data Analysis and Classification · doi:10.1007/s11634-018-0342-1 · Cited by 29 · see citing works
Subgroup identification in clinical trials: an overview of available methods and their implementations with R
Zhang, Z., Seibold, H., Vettore, M. V., Song, W., & François, V. (2018) · Annals of Translational Medicine · doi:10.21037/atm.2018.03.07 · Cited by 29 · see citing works
Predictive modeling of COPD exacerbation rates using baseline risk factors
Singh, D., Hurst, J. R., Martínez, F. J., Rabe, K. F., Bafadhel, M., Jenkins, M. A., Salazar, D., Dorinsky, P., & Darken, P. F. (2022) · Therapeutic Advances in Respiratory Disease · doi:10.1177/17534666221107314 · Cited by 28 · see citing works
Clinical data mining: challenges, opportunities, and recommendations for translational applications
Qiao, H., Chen, Y., Qian, C., & Guo, Y. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05005-0 · Cited by 27 · see citing works
A model‐based multithreshold method for subgroup identification
Wang, J., Li, J., Li, Y., & Wong, W. K. (2019) · Statistics in Medicine · doi:10.1002/sim.8136 · Cited by 26 · see citing works
Improving Veteran Access to Integrated Management of Back Pain (AIM-Back): Protocol for an Embedded Pragmatic Cluster-Randomized Trial
George, S. Z., Coffman, C. J., Allen, K. D., Lentz, T. A., Choate, A., Goode, A. P., Simon, C. B., Grubber, J. M., King, H. A., Cook, C. E., et al. (2020) · Pain Medicine · doi:10.1093/pm/pnaa348 · Cited by 26 · see citing works
The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer
Ohnmacht, A. J., Stahler, A., Stintzing, S., Modest, D. P., Holch, J. W., Westphalen, C. B., Hölzel, L., Schübel, M. K., Galhoz, A., Farnoud, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41011-4 · Cited by 25 · see citing works
A Review of Causal Inference for External Comparator Arm Studies
Rippin, G., Ballarini, N. M., Sanz, H., Largent, J. A., Quinten, C., & Pignatti, F. (2022) · Drug Safety · doi:10.1007/s40264-022-01206-y · Cited by 25 · see citing works
Multiplicity corrections in life sciences: challenges and consequences
Menyhárt, O., & Győrffy, B. (2025) · International Journal of Epidemiology · doi:10.1093/ije/dyaf098 · Cited by 24 · see citing works
A systematic survey identified 36 criteria for assessing effect modification claims in randomized trials or meta-analyses
Schandelmaier, S., Chang, Y., Devasenapathy, N., Devji, T., Kwong, J. S. W., Colunga‐Lozano, L. E., Lee, Y., Agarwal, A., Bhatnagar, N., Ewald, H., et al. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.05.014 · Cited by 24 · see citing works
A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
Huber, C., Benda, N., & Friede, T. (2019) · Pharmaceutical Statistics · doi:10.1002/pst.1951 · Cited by 24 · see citing works
Multiplicity issues in exploratory subgroup analysis
Lipkovich, I. A., Dmitrienko, A. A., Muysers, C., & Ratitch, B. (2017) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2017.1397009 · Cited by 23 · see citing works
Self-citation
Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab
Terranova, N., French, J. L., Dai, H. I., Wiens, M., Khandelwal, A., Ruiz-Garcı́a, A., Manitz, J., von Heydebreck, A., Ruisi, M., Chin, K. M., et al. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12754 · Cited by 23 · see citing works
Effective Visual Communication for the Quantitative Scientist
Vandemeulebroecke, M., Baillie, M., Margolskee, A., & Magnusson, B. (2019) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12455 · Cited by 22 · see citing works
Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis — An Application to the International Diabetes Management Practice Study
Esnault, C., Gadonna, M.-L., Queyrel, M., Templier, A., & Zucker, J. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.559927 · Cited by 22 · see citing works
A recursive partitioning approach for subgroup identification in individual patient data meta‐analysis
Mistry, D., Stallard, N., & Underwood, M. R. (2018) · Statistics in Medicine · doi:10.1002/sim.7609 · Cited by 21 · see citing works
Overview of modern approaches for identifying and evaluating heterogeneous treatment effects from clinical data
Lipkovich, I. A., Svensson, D. J., Ratitch, B., & Dmitrienko, A. A. (2023) · Clinical Trials · doi:10.1177/17407745231174544 · Cited by 21 · see citing works
Self-citation
Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error
Holmes, C., & Watson, J. A. (2020) · Trials · doi:10.1186/s13063-020-4076-y · Cited by 21 · see citing works
Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data
Yang, J., Dahabreh, I. J., & Steingrimsson, J. A. (2021) · Biometrics · doi:10.1111/biom.13432 · Cited by 21 · see citing works
Subgroup identification in dose‐finding trials via model‐based recursive partitioning
Thomas, M., Bornkamp, B., & Seibold, H. (2018) · Statistics in Medicine · doi:10.1002/sim.7594 · Cited by 20 · see citing works
Precision medicine: Subgroup identification in longitudinal trajectories
Wei, Y., Liu, L., Su, X., Zhao, L., & Jiang, H. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220904114 · Cited by 20 · see citing works
Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis
Brandt, L., Ritter, K., Schneider‐Thoma, J., Siafis, S., Montag, C., Ayrilmaz, H., Bermpohl, F., Hasan, A., Heinz, A., Leucht, S., et al. (2023) · The Lancet Psychiatry · doi:10.1016/s2215-0366(23)00008-1 · Cited by 18 · see citing works
Treatment evaluation for a data‐driven subgroup in adaptive enrichment designs of clinical trials
Zhang, Z., Chen, R., Soon, G. G., & Zhang, H. (2017) · Statistics in Medicine · doi:10.1002/sim.7497 · Cited by 19 · see citing works
Subgroup analysis and interpretation for phase 3 confirmatory trials: White paper of the EFSPI/PSI working group on subgroup analysis
Dane, A. L., Spencer, A. V., Rosenkranz, G., Lipkovich, I. A., Parke, T., & on behalf of the PSI/EFSPI Working Group on Subgroup Analysis (2018) · Pharmaceutical Statistics · doi:10.1002/pst.1919 · Cited by 19 · see citing works
Self-citation
A critical review of graphics for subgroup analyses in clinical trials
Ballarini, N. M., Chiu, Y., König, F., Posch, M., & Jaki, T. (2020) · Pharmaceutical Statistics · doi:10.1002/pst.2012 · Cited by 19 · see citing works
Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report
Montesi, S. B., Gómez, C. R., Beers, M. F., Brown, R., Chattopadhyay, I., Flaherty, K. R., Garcia, C. K., Gomperts, B. N., Hariri, L. P., Hogaboam, C. M., et al. (2023) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202307-1154ws · Cited by 18 · see citing works
The change-plane Cox model
Wei, S. H., & Kosorok, M. R. (2018) · Biometrika · doi:10.1093/biomet/asy050 · Cited by 17 · see citing works
Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies
Linck, E. J. G., Goligher, E. C., Semler, M. W., & Churpek, M. M. (2024) · Critical Care Medicine · doi:10.1097/ccm.0000000000006371 · Cited by 18 · see citing works
Estimating individual treatment effects by gradient boosting trees
Sugasawa, S., & Noma, H. (2019) · Statistics in Medicine · doi:10.1002/sim.8357 · Cited by 17 · see citing works
The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions
Montoya, L., van der Laan, M. J., Luedtke, A. R., Skeem, J. L., Coyle, J., & Petersen, M. L. (2022) · The International Journal of Biostatistics · doi:10.1515/ijb-2020-0127 · Cited by 19 · see citing works
Primer on Health Equity Research in Health Economics and Outcomes Research: An ISPOR Special Interest Group Report
Griffiths, M. J. S., Cookson, R., Avanceña, A. L. V., Espinoza, M., Jacobsen, C. M., Sussell, J. A., & Kowal, S. L. (2025) · Value in Health · doi:10.1016/j.jval.2024.09.012 · Cited by 16 · see citing works
On the Stability of Feature Selection in the Presence of Feature Correlations
Sechidis, K., Papangelou, K., Nogueira, S., Weatherall, J. H., & Gavin Brown, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46150-8_20 · Cited by 16 · see citing works
Subgroup identification in dose-finding trials via model-based recursive partitioning
Thomas, M., Bornkamp, B., & Seibold, H. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-151171 · Cited by 16 · see citing works
Subgroup analyses in randomized controlled trials frequently categorized continuous subgroup information
Williamson, S. F., Grayling, M. J., Mander, A., Noor, N. M., Savage, J. S., Yap, C., & Wason, J. M. S. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.06.017 · Cited by 15 · see citing works
Predicting Treatment Responses in Patients With Osteoarthritis: Results From Two Phase III Tanezumab Randomized Clinical Trials
Colloca, L., Dworkin, R. H., Farrar, J. T., Tive, L. A., Yang, J., Viktrup, L., Dašić, G., West, C. R., Whalen, E., Brown, M., et al. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2842 · Cited by 15 · see citing works
Subgroup identification via homogeneity pursuit for dense longitudinal/spatial data
Li, J., Yue, M., & Zhang, W. (2019) · Statistics in Medicine · doi:10.1002/sim.8192 · Cited by 15 · see citing works
Effects of housing vouchers on the long-term exposure to neighbourhood opportunity among low-income families: the moving to opportunity experiment
Kim, H., Schmidt, N. M., Osypuk, T. L., Thyden, N. H., & Rehkopf, D. H. (2022) · Housing Studies · doi:10.1080/02673037.2022.2112154 · Cited by 15 · see citing works
Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials
Sun, S. B., Sechidis, K., Chen, Y., Lu, J., Ma, C., Mirshani, A., Ohlssen, D. I., Vandemeulebroecke, M., & Bornkamp, B. (2022) · Biometrical Journal · doi:10.1002/bimj.202100337 · Cited by 14 · see citing works
Meclizine in combination with ondansetron for prevention of postoperative nausea and vomiting in a high-risk population.
Forrester, C. M., Benfield, D. A., Matern, C. E., Kelly, J. A., & Pellegrini, J. E. (2007) · PubMed · Cited by 14 · see citing works
Optimal Treatment Regimes: A Review and Empirical Comparison
Li, Z., Chen, J., Laber, E. B., Liu, F., & Baumgartner, R. (2023) · International Statistical Review · doi:10.1111/insr.12536 · Cited by 13 · see citing works
A Simulation Study Comparing Different Statistical Approaches for the Identification of Predictive Biomarkers
Haller, B., Ulm, K., & Hapfelmeier, A. (2019) · Comput. Math. Methods Medicine · doi:10.1155/2019/7037230 · Cited by 13 · see citing works
Application of structured statistical analyses to identify a biomarker predictive of enhanced tralokinumab efficacy in phase III clinical trials for severe, uncontrolled asthma
Gottlow, M., Svensson, D. J., Lipkovich, I. A., Huhn, M., Bowen, K., Wessman, P., & Colice, G. (2019) · BMC Pulmonary Medicine · doi:10.1186/s12890-019-0889-4 · Cited by 13 · see citing works
Self-citation
Using knockoffs for controlled predictive biomarker identification
Sechidis, K., Kormáksson, M., & Ohlssen, D. I. (2021) · Statistics in Medicine · doi:10.1002/sim.9134 · Cited by 13 · see citing works
A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate
Ge, X., Peng, Y., & Tu, D. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220912772 · Cited by 13 · see citing works
Covariate Selection for Estimating Individual Treatment Effects in Psychotherapy Research: A Simulation Study and Empirical Example
Wester, R. A., Rubel, J. A., & Mayer, A. (2022) · Clinical Psychological Science · doi:10.1177/21677026211071043 · Cited by 13 · see citing works
Iterative Causal Forest: A Novel Algorithm for Subgroup Identification
Wang, T., Keil, A. P., Kim, S., Wyss, R., Htoo, P. T., Funk, M. J., Buse, J. B., Kosorok, M. R., & Stürmer‎, T. (2023) · American Journal of Epidemiology · doi:10.1093/aje/kwad219 · Cited by 12 · see citing works
Development, Validation, and Clinical Utility of a Nomogram for Urological Tumors: How to Build the Best Predictive Model
Utsumi, T., Ishitsuka, N., Noro, T., Suzuki, Y., Iijima, S., Sugizaki, Y., Somoto, T., Oka, R., Endo, T., Kamiya, N., et al. (2025) · International Journal of Urology · doi:10.1111/iju.70075 · Cited by 12 · see citing works
Studying treatment-effect heterogeneity in precision medicine through induced subgroups
Sies, A., Demyttenaere, K., & Mechelen, I. V. (2019) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2019.1579220 · Cited by 12 · see citing works
Look before you leap: systematic evaluation of tree-based statistical methods in subgroup identification
Liu, Y., Ma, X., Zhang, D., Geng, L., Wang, X., Zheng, W., & Chen, M. (2019) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2019.1584204 · Cited by 12 · see citing works
Multi-stage adaptive enrichment trial design with subgroup estimation
Joshi, N., Nguyen, C. T., & Ivanova, A. (2020) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2020.1832109 · Cited by 12 · see citing works
Exploratory identification of predictive biomarkers in randomized trials with normal endpoints
Krzykalla, J., Benner, A., & Kopp‐Schneider, A. (2019) · Statistics in Medicine · doi:10.1002/sim.8452 · Cited by 12 · see citing works
Added value of Mindfulness-Based Cognitive Therapy for Depression: A Tree-based Qualitative Interaction Analysis.
ter Avest, M. J., Dusseldorp, E., Huijbers, M., van Aalderen, J. V., Cladder-Micus, M. B., Spinhoven, P., Greven, C., & Speckens, A. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103467 · Cited by 13 · see citing works
A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data
Zhang, P., Ma, J., Chen, X., & Shentu, Y. (2020) · Statistics in Medicine · doi:10.1002/sim.8714 · Cited by 11 · see citing works
Assessing and communicating heterogeneity of treatment effects for patient subpopulations: The hardest problem there is
Ruberg, S. J. (2021) · Pharmaceutical Statistics · doi:10.1002/pst.2110 · Cited by 11 · see citing works
Considerations for Analyzing and Interpreting Data from Biometric Monitoring Technologies in Clinical Trials
Ratitch, B., Rodriguez-Chavez, I. R., Dabral, A., Fontanari, A., Vega, J., Onorati, F., Vandendriessche, B., Morton, S., Damestani, Y., & Damestani, Y. (2022) · Digital Biomarkers · doi:10.1159/000525897 · Cited by 10 · see citing works
Identifying heterogeneity of treatment effect for antibiotic duration in bloodstream infection: an exploratory post-hoc analysis of the BALANCE randomised clinical trial
Ong, S. W. X., Pinto, R. L., Rishu, A. H., Tong, S. Y. C., Commons, R. J., Conly, J. M., Evans, G. A., Fralick, M., Kandel, C. E., Lagacé-Wiens, P. R. S., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103195 · Cited by 10 · see citing works
A permutation test for assessing the presence of individual differences in treatment effects
Chang, C., Jaki, T., Sadiq, M. S., Kuhlemeier, A., Feaster, D. J., Cole, N., Lamont, A. E., Oberski, D. L., Desai, Y., & Horn, M. L. V. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211033640 · Cited by 10 · see citing works
Multi‐threshold proportional hazards model and subgroup identification
Wang, B., Li, J., & Wang, X. (2022) · Statistics in Medicine · doi:10.1002/sim.9589 · Cited by 10 · see citing works
Subgroup Identification in Clinical Trials by Stochastic SIDEScreen Methods
Lipkovich, I. A., Dmitrienko, A. A., Patra, K., Ratitch, B., & Pulkstenis, E. P. (2017) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2017.1371069 · Cited by 10 · see citing works
Self-citation
A Bayesian Method for the Detection of Proof of Concept in Early Phase Oncology Studies with a Basket Design
Jin, J., Liu, Q., Zheng, W., Shun, Z., Lin, T. T., Gao, L., & Dong, Y. (2020) · Statistics in Biosciences · doi:10.1007/s12561-020-09267-2 · Cited by 10 · see citing works
Multiply robust subgroup analysis based on a single‐index threshold linear marginal model for longitudinal data with dropouts
Wei, K., Zhu, H., Qin, G., Zhu, Z., & Tu, D. (2022) · Statistics in Medicine · doi:10.1002/sim.9386 · Cited by 10 · see citing works
Data-Driven and Confirmatory Subgroup Analysis in Clinical Trials
Dmitrienko, A. A., Lipkovich, I. A., Dane, A. L., & Muysers, C. (2020) · Emerging topics in statistics and biostatistics · doi:10.1007/978-3-030-40105-4_3 · Cited by 10 · see citing works
Self-citation
New insights into adaptive enrichment designs
Antognini, A. B., Frieri, R., & Zagoraiou, M. (2023) · Statistical Papers · doi:10.1007/s00362-023-01433-0 · Cited by 9 · see citing works

Statistical Methods for Biomarker and Subgroup Evaluation in Oncology Trials
Lipkovich, I. A., Dmitrienko, A. A., & Ratitch, B. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315112084-16
Self-citation
Exploratory Subgroup Identification for Biopharmaceutical Development
Huang, X., Gu, Y., Sun, Y., & Chan, I. S. F. (2020) · Emerging topics in statistics and biostatistics · doi:10.1007/978-3-030-40105-4_12 · Cited by 1 · see citing works
Combining machine learning methods for subgroup identification in time-to-event data with approximate Bayesian computation for bias correction
Stahl, H., Klein, L., Grieser, G., Jahn, A., & Götte, H. (2025) · German Medical Science (German Research Foundation) · doi:10.3205/25gmds067
Estimating the subgroup and testing for treatment effect in a post-hoc analysis of a clinical trial with a biomarker
Joshi, N., Fine, J. P., Chu, R., & Ivanova, A. (2019) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2019.1633655 · Cited by 6 · see citing works
Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation
Bornkamp, B., Zaoli, S., Azzarito, M., Martin, R., Müller, C. P., Moloney, C., Capestro, G., Ohlssen, D. I., & Baillie, M. (2024) · Pharmaceutical Statistics · doi:10.1002/pst.2368 · Cited by 6 · see citing works

Authors: Lipkovich, I., Dmitrienko, A., & B., R.
Affiliations: IQVIA (United States) (US)
Mediana, Inc. Overland Park KS U.S.A.
Boston University Boston MA U.S.A.
Corresponding author: Lipkovich, Ilya
ORCID: Lipkovich, Ilya
Dmitrienko, Alex
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2016-08-03
Volume: 36
Issue: 1
Pages: 136-196
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.7064
OpenAlex: W2476265589
Cited by: 291
References: 101
Citations by year: 2026: 19 · 2025: 26 · 2024: 27 · 2023: 36 · 2022: 29 · 2021: 38 · 2020: 39 · 2019: 36 · 2018: 26 · 2017: 10 · 2016: 1 · 2015: 3
FWCI: 24.61
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
doi.wiley.com/10.1002/tdm_license_1 (tdm)
onlinelibrary.wiley.com/termsAndConditions (vor)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Keywords: subgroup identification, predictive biomarkers, treatment effect heterogeneity, personalized medicine, clinical trials, machine learning, data mining, optimal treatment regimes, binary endpoints, survival endpoints
MeSH terms: Clinical Trials as Topic, Humans, Research Design, Biomarkers, Biostatistics, Data Mining, Precision Medicine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks