Subgroup analysis bibliography

Category: Subgroup analysis.

Confirmatory subgroup analysis

Multi-population clinical trials

Freidlin, B., McShane, L.M., Korn, E.L. (2010). Randomized clinical trials with biomarkers: Design issues. Journal of National Cancer Institute. 102, 152-160.

Freidlin, B., McShane, L.M., Polley, M.C., Korn, E.L. (2012). Randomized Phase II trial designs with biomarkers. Journal of Clinical Oncology. 30, 3304-3309.

Millen, B., Dmitrienko, A., Ruberg, S., Shen, L. (2012). A statistical framework for decision making in confirmatory multipopulation tailoring clinical trials. Drug Information Journal. 46, 647-656.

Rothmann, M.D., Zhang, J.J., Lu, L., Fleming, T.R. (2012). Testing in a pre-specified subgroup and the intent-to-treat population. Drug Information Journal. 46, 175-179.

Simon, R., Wang, S.J. (2006). Use of genomic signatures in therapeutics development in oncology and other diseases. Pharmacogenomics Journal. 6, 166-173.

Adaptive population selection designs

Brannath, W., Zuber, E., Branson, M., Bretz, F., Gallo, P., Posch, M., Racine-Poon, A. (2009). Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology. Statistics in Medicine. 28, 1445-1463.

Freidlin, B., Simon, R. (2005). Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clinical Cancer Research. 11, 7872-7878.

Freidlin, B., Jiang, W., Simon, R. (2010). Adaptive signature design: The cross-validated adaptive signature design. Clinical Cancer Research. 16, 691-698.

Friede, T., Parsons, N., Stallard, N. (2012). A conditional error function approach for subgroup selection in adaptive clinical trials. Statistics in Medicine. 31, 4309-4320.

Jiang, W., Freidlin, B., Simon, R. (2007). Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarkerdefined subset effect. Journal of National Cancer Institute. 99, 1036-1043.

Mehta, C.R., Gao, P. (2011). Population enrichment designs: case study of a large multinational trial. Journal of Biopharmaceutical Statistics. 21, 831-845.

Wang, S.J., O’Neill, R.T., Hung, H.M.J. (2007). Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics. 6, 227-244.

Wang, S.J., Hung, H.M.J., O’Neill, R.T. (2007). Adaptive patient enrichment designs in therapeutic trials. Biometrical Journal. 51, 358-374.

Multiplicity issues

Alosh, M., Huque, M.F. (2009). A flexible strategy for testing subgroups and overall population. Statistics in Medicine. 28, 3-23.

Alosh, M., Huque, M.F. (2010). A consistency-adjusted alpha-adaptive strategy for sequential testing. Statistics in Medicine. 29, 1559-1571.

Song, Y., Chi, GY. (2007). A method for testing a prespecified subgroup in clinical trials. Statistics in Medicine. 26, 3535-3549.

Zhao, Y.D., Dmitrienko, A., Tamura R. (2010). Design and analysis considerations in clinical trials with a sensitive subpopulation. Statistics in Biopharmaceutical Research. 2, 72-83.

Exploratory subgroup analysis and subgroup search

Berger, J., Wang, X., Shen, L. (2014). A Bayesian approach to subgroup identification. Journal of Biopharmaceutical Statistics. 24, 110-129.

Bonetti, M., Zahrieh, D., Cole, B.F., Gelber, R.D. (2009). A small sample study of the STEPP approach to assessing treatment covariate interactions in survival data. Statistics in Medicine. 2, 1255-1268.

Cai, T., Tian, L., Wong, P., Wei, L.J. (2011). Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics. 12, 270-282.

Chen, G., Zhong, H, Belousov, A. Viswanath, D. (2015). PRIM Approach to Predictive-signature Development for Patient Stratification. Statistics in medicine. 34, 317-342.

Dmitrienko, A., Millen, B., Lipkovich, I. (2017). Multiplicity considerations in subgroup analysis. Statistics in Medicine. 36, 4446-4454.

Dusseldorp, E., Mechelen, I.V. (2014). Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interaction. Statistics in medicine. 33, 219-237.

Foster, J.C., Taylor, J.M.C., Ruberg, S.J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine. 30, 2867-2880.

Gu, X., Yin, G., Lee, J.J. (2013). Bayesian two-step Lasso strategy for biomarker selection in personalized medicine development for time-to-event endpoints. Contemporary Clinical Trials. 36, 642–650.

Gunter, L., Zhu, J., Murphy, S. (2011). Variable selection for qualitative interactions in personalized medicine while controlling the familywise error rate. Journal of Biopharmaceutical Statistics. 21, 1063-1078.

Hodges, J.S., Cui, Y., Sargent, D.J., Carlin, B.P. (2007). Smoothing balanced single-error-term analysis of variance. Technometrics. 49, 12-25.

Imai, K., Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. The Annals of Applied Statistics. 7, 443-470.

Jones, H.E., Ohlssen, D.I., Neuenschwander, B., Racine, A., Branson, M. (2011). Bayesian models for subgroup analysis in clinical trials. Clinical Trials. 8, 129-143.

Kehl, V., Ulm, K. (2006). Responder identification in clinical trials with censored data. Computational Statistics & Data Analysis. 50, 1338-1355.

Lipkovich, I., Dmitrienko, A., Denne, J., Enas, G. (2011). Subgroup Identification based on Differential Effect Search (SIDES) – A recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine. 30, 2601-2621.

Lipkovich, I., Dmitrienko, A. (2014). Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES. Journal of Biopharmaceutical Statistics. 24, 130-153.

Lipkovich, I., Dmitrienko, A., D’Agostino, R.B. (2017). Tutorial in Biostatistics: Data-driven subgroup identification and analysis in clinical trials. Statistics in Medicine. 36, 136-196.

Lipkovich, I., Dmitrienko, A., Muysers, C., Ratitch, B. (2018). Multiplicity issues in exploratory subgroup analysis. Journal of Biopharmaceutical Statistics. 28, 63-81.

Loh, W-Y., He, X., Man, M. (2015). A regression tree approach to identifying subgroups with differential treatment effects, Statistics in medicine. 34, 1818-

Negassa, A., Ciampi, A., Abrahamowicz, M., Shapiro, S., Boivin, J.F. (2005). Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria. Statistics and Computing. 15, 231-239.

Seibold,  H.,  Zeileis, A.,  Hothorn, T. (2016). Model-based Recursive Partitioning for Subgroup Analyses. International Journal of Biostatistics. 12(1):45-63

Seibold Н., Zeileis, A. Hothorn, T. (2017). Individual Treatment Effect Prediction for amyotrophic lateral sclerosis Patients. Statistical Methods in Medical Research.

Sivaganesan, S., Laudm P.W., Muller, P. (2011). A Bayesian subgroup analysis with a zero-enriched Polya urn scheme. Statistics in Medicine. 30, 312-323.

Su, X., Tsai, C.L., Wang, H., Nickerson, D.M., Li, B. (2009). Subgroup analysis via recursive partitioning. Journal of Machine Learning Research. 10, 141-158.

Su, X.G., Zhou, T., Yan, X., Fan, J., Yang, S. (2008). Interaction trees with censored survival data. The International Journal of Biostatistics. 4, Article 2.

Thomas, M., Bornkamp, B., Seibold, H. (2018). Subgroup identification in dose‐finding trials via model‐based recursive partitioning. Statistics in Medicine. 37:1608–1624.

Tian, L., Alizaden, A.A., Gentles, A.J., Tibshirani, R. (2012). A simple method for detecting interactions between a treatment and a large number of covariates. Available at

Xu, Y., Yu, M., Zhao, Y-Q., Li, Q., Wang, S., Shao, J. (2015). Regularized outcome weighted subgroup identification for differential treatment effects. Biometrics. 71(3):645-53.

Zhang, B., Tsiatis, A. A., Davidian, M., Zhang, M., Laber, E.B. (2012). Estimating optimal treatment regimes from a classification perspective. Statistics. 1, 103-114.

Zhao, Y., Zheng, D., Rush, A.J., Kosorok, M.R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association. 107, 1106-1118.