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
Ballarini, N.M., Rosenkranz, G.K., Jaki, T., König, F., Posch, M. (2018). Subgroup identification in clinical trials via the predicted individual treatment effect. PLoS One 13:e0205971.
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.
Bornkamp, B., Ohlssen, D., Magnusson, B.P., Schmidli, H. (2017). Model averaging for treatment effect estimation in subgroups. Pharm Stat. 16, 133-142.
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.
Chen, S., Tian, L., Cai, T., Yu, M. (2017). A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics. 73(4), 1199-1209.
Chernozhukov, V., Demirer, M., Duflo, E., Fernandez-val. (2019). Generic machine learning inference on heterogenous treatment effects in randomized experiments. https://arxiv.org/abs/1712.04802.
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.
Guo, X., He, X. (2021). Inference on selected subgroups in clinical trials. J Am Stat Assoc. 116(535), 1498-1506.
Guo. W., Zhou, X-H., Ma, S. (2021). Estimation of optimal individualized treatment rules using a covariate-specific treatment effect curve with high-dimensional covariates. J Am Stat Assoc. 116(533), 309-321.
Hahn, P.R., Murray, J.S., Carvalho, C.M. (2019). Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects. Bayesian Anal. 15(3). 965-1056.
Han, Y., Tang, S-Y., Lin, H-M., Hsu, J.C. (2021). Exact simultaneous confidence intervals for logical selection of a biomarker cut-point. Biometrical journal. 1-18.
Hodges, J.S., Cui, Y., Sargent, D.J., Carlin, B.P. (2007). Smoothing balanced single-error-term analysis of variance. Technometrics. 49, 12-25.
Huang, X., Sun, Y., Trow, P., Chatterjee, S., Chakravartty, A., Tian, L., & Devanarayan, V. (2017). Patient subgroup identification for clinical drug development. Statistics in medicine. 36(9), 1414-1428.
Huling, J.D., and Yu, M. (2021). Subgroup identification using the personalized package. Journal of statistical software. 98(5), 1-60.
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.
Laber, E.B., Zhao, Y.Q. (2015). Tree-based methods for individualized treatment regimes. Biometrika. 102, 501-514.
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, 1818-1833.
Loh, W.Y., Fu, H., Man, M., Champion, V., Yu, M. (2016). Identification of subgroups with differential treatment effects for longitudinal and multiresponse variables. Stat Med. 35, 4837-4855.
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.
Nie, X. and Wager, S. (2021). Quasi-oracle estimation of heterogeneous treatment effects. Biometrika. 108(2), 299-319.
Qi, Z., Lui, B., Fu, H., Lui, Y. (2020). Multi-armed angle-based direct learning for estimating optimal individualized treatment rules with various outcomes. J Am Stat Assoc. 115(530), 678-691.
Schnell, P.M., Müller, P., Tang, Q., Carlin, B.P. (2018). Multiplicity-adjusted semiparametric benefiting subgroup identification in clinical trials. Clinical trials. 15(1),75-86.
Sechidis, K., Kormaksson, M., Ohlssen, D. (2021). Using knockoffs for controlled predictive biomarker identification. Statistics in Medicine. 40,5453-5473.
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. https://doi.org/10.1177/0962280217693034.
Shen, C., Li, X., and Jeong, J. (2016). Estimation of treatment effect in a sub-population: an Empirical Bayes approach. Journal of Biopharmaceutical Statistics. 26(3), 507-518.
Shen, J., He, X. (2015). Inference for subgroup analysis with a structured logistic-normal mixture model. J Am Stat Assoc. 110, 303-312.
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 http://arxiv.org/abs/1212.2995.
VanderWeelem, T.Y., Luedtke, A.R., van der Laan, M.J., Kessler, R.C. (2019). Selecting optimal subgroups for treatment using many covariates. Epidemiology. 30, 334-341.
Wager, S., Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 113,1228-1242.
Wang, Y., Fu, H., and Zeng, D. (2018). Learning optimal personalized treatment rules in consideration of benefit and risk: with an application to treating type 2 diabetes patients with insulin therapies. J Am Stat Assoc. 113(521), 1-13.
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.
Zhang, Y., Schnell, P., Song, C., Huang, B., Lu B. (2021). Subgroup causal effect identification and estimation via matching tree. Comp Stat and Data Analysis. https://doi.org/10.1016/j.csda.2021.107188.
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.