A likely responder approach for the analysis of randomized controlled trials

National Research Council, 2011 Abrams, 2014, National Cancer Institute’s precision medicine initiatives for the new national clinical trials network, Am. Soc. Clin. Oncol. Educ. Book, 71, 10.14694/EdBook_AM.2014.34.71 Ruberg, 2010, The mean does not mean as much anymore: finding sub-groups for tailored therapeutics, Clin Trials, 7, 574, 10.1177/1740774510369350 Neyman, 1923, On the application of probability theory to agricultural experiments. essay on principles. section 9. (translated and edited by DM Dabrowska and TP Speed, Statistical Science (1990), 5, 465-480), Ann. Agric. Sci., 10, 1 Fisher, 1925 Rubin, 1974, Estimating causal effects of treatments in randomized and nonrandomized studies, J Edu Psychol, 66, 688, 10.1037/h0037350 Imbens, 2004, Nonparametric estimation of average treatment effects under exogeneity: a review, Rev. Econ. Stat., 86, 4, 10.1162/003465304323023651 Imbens, 2015 Rubin, 2005, Causal inference using potential outcomes: design, modeling, decisions, J. Am. Stat. Assoc., 100, 322, 10.1198/016214504000001880 Lee, 2018, Comparative effectiveness of extended-release naltrexone versus buprenorphine-naloxone for opioid relapse prevention (X: BOT): a multicentre, open-label, randomised controlled trial, Lancet, 23391, 309, 10.1016/S0140-6736(17)32812-X Gadbury, 2004, Individual treatment effects in randomized trials with binary outcomes, J Stat Plan Inference, 121, 163, 10.1016/S0378-3758(03)00115-0 Lamont, 2018, Identification of predicted individual treatment effects in randomized clinical trials, Stat. Methods Med. Res., 27, 142, 10.1177/0962280215623981 Ballarini, 2018, Subgroup identification in clinical trials via the predicted individual treatment effect, PLoS One, 13, 10.1371/journal.pone.0205971 European Medicines Agency, 2017 Permutt, 2016, A taxonomy of estimands for regulatory clinical trials with discontinuations, Stat. Med., 35, 2865, 10.1002/sim.6841 Permutt, 2019, Defining treatment effects: a regulatory perspective, Clin. Trials, 16, 345, 10.1177/1740774519830358 National Research Council, 2010 Keene, 2021, Why ITT analysis is not always the answer for estimating treatment effects in clinical trials, Contemp. Clin. Trials, 108, 10.1016/j.cct.2021.106494 Frangakis, 2002, Principal stratification in causal inference, Biometrics, 58, 21, 10.1111/j.0006-341X.2002.00021.x Hansen, 2008, The prognostic analogue of the propensity score, Biometrika, 95, 481, 10.1093/biomet/asn004 Rosenbaum, 1983, The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41, 10.1093/biomet/70.1.41 Stuart, 2010, Matching methods for causal inference: a review and a look forward, Stat. Sci. Rev. J. Inst. Math. Stat., 25, 1 Fisher, 1936, “ the coefficient of racial likeness” and the future of craniometry, J. R. Anthropol. Inst. G. B. Irel., 66, 57 Lehmann, 2006 The Food and Drug Administration Austin, 2009, Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples, Stat. Med., 28, 3083, 10.1002/sim.3697 Hansen, 2006 Miettinen, 1976, Stratification by a multivariate confounder score, Am. J. Epidemiol., 104, 609, 10.1093/oxfordjournals.aje.a112339 Arbogast, 2009, Use of disease risk scores in pharmacoepidemiologic studies, Stat. Methods Med. Res., 18, 67, 10.1177/0962280208092347 Wyss, 2017, The “dry-run” analysis: a method for evaluating risk scores for confounding control, Am. J. Epidemiol., 185, 842, 10.1093/aje/kwx032 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Brier, 1950, Verification of forecasts expressed in terms of probability, Mon. Weather Rev., 78, 1, 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 Anscombe, 1963, Sequential medical trials, J. Am. Stat. Assoc., 58, 365, 10.1080/01621459.1963.10500851 Colton, 1963, A model for selecting one of two medical treatments, J. Am. Stat. Assoc., 58, 388, 10.1080/01621459.1963.10500853 Cornfield, 1969, An adaptive procedure for sequential clinical trials, J. Am. Stat. Assoc., 64, 759, 10.1080/01621459.1969.10501010 Simon, 2018, Personalized Cancer genomics, Annu. Rev. Stat. Appl., 5, 169, 10.1146/annurev-statistics-031017-100609 Simon, 2004, Evaluating the efficiency of targeted designs for randomized clinical trials, Clin. Cancer Res., 10, 6759, 10.1158/1078-0432.CCR-04-0496 Simon, 2013, Adaptive enrichment designs for clinical trials, Biostatistics, 14, 613, 10.1093/biostatistics/kxt010 Freidlin, 2005, Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients, Clin. Cancer Res., 11, 7872, 10.1158/1078-0432.CCR-05-0605 Freidlin, 2010, The cross-validated adaptive signature design, Clin. Cancer Res., 16, 691, 10.1158/1078-0432.CCR-09-1357 Karuri, 2012, A two-stage Bayesian design for co-development of new drugs and companion diagnostics, Stat. Med., 31, 901, 10.1002/sim.4462 Wang, 2007, Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset, Pharm. Stat. J. Appl. Stat. Pharm. Ind., 6, 227 Wang, 2009, Adaptive patient enrichment designs in therapeutic trials, Biom. J. J. Math. Methods Biosci., 51, 358 Jenkins, 2011, An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints, Pharm. Stat., 10, 347, 10.1002/pst.472 Magnusson, 2013, Group sequential enrichment design incorporating subgroup selection, Stat. Med., 32, 2695, 10.1002/sim.5738 Qian, 2011, Performance guarantees for individualized treatment rules, Ann. Stat., 39, 1180, 10.1214/10-AOS864 Zhang, 2012, Estimating optimal treatment regimens from a classification perspective, Stat., 1, 103, 10.1002/sta.411 Murphy, 2003, Optimal dynamic treatment regimes (with discussion), J. R. Stat. Soc. Ser. B, 65, 331, 10.1111/1467-9868.00389 Chakraborty, 2009, Inference for non-regular parameters in optimal dynamic treatment regimes, Stat. Methods Med. Res., 19, 317, 10.1177/0962280209105013 Laber, 2014, Dynamic treatment regimes: technical challenges and applications, Electron. J. Stat., 8, 1225 Basu, 2014, Estimating person-centered treatment (PeT) effects using instrumental variables: an application to evaluating prostate cancer treatments, J. Appl. Econ., 29, 671, 10.1002/jae.2343 Foster, 2011, Subgroup identification from randomized clinical trial data, Stat. Med., 30, 2867, 10.1002/sim.4322 Doove, 2013, A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions, Adv. Data Anal. Classif., 1 Freidlin, 2012, Randomized phase II trial designs with biomarkers, J. Clin. Oncol., 30, 3304, 10.1200/JCO.2012.43.3946 Imai, 2011, Estimation of heterogeneous treatment effects from randomized experiments, with application to the optimal planning of the get-out-the-vote campaign, Polit. Anal., 19, 1 Zhang, 2013, Assessing the heterogeneity of treatment effects via potential outcomes of individual patients, J. R. Stat. Soc. C, 62, 10.1111/rssc.12012 Shen, 2013, Treatment benefit and treatment harm rate to characterize heterogeneity in treatment effect, Biometrics, 69, 724, 10.1111/biom.12038 Poulson, 2012, Treatment heterogeneity and individual qualitative interaction, Am. Stat., 66, 16, 10.1080/00031305.2012.671724 Cai, 2011, Analysis of randomized comparative clinical trial data for personalized treatment selections, Biostatistics, 12, 270, 10.1093/biostatistics/kxq060 Lipkovich, 2014, Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES, J. Biopharm. Stat., 24, 130, 10.1080/10543406.2013.856024 Zhang, 2016, Use of auxiliary covariates in estimating a biomarker-adjusted treatment effect model with clinical trial data, Stat. Methods Med. Res., 25, 2103, 10.1177/0962280213515572 Wyss, 2016, A review of disease risk scores and their application in pharmacoepidemiology, Curr. Epidemiol. Rep., 3, 277, 10.1007/s40471-016-0088-2 Nguyen, 2019, The use of prognostic scores for causal inference with general treatment regimes, Stat. Med., 38, 2013, 10.1002/sim.8084