Statistical Modelling for Adaptive Design of Clinical Trials: Optimality and Efficiency

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Response adaptive design (RAD) has ethical advantages over traditional methods for clinical trials, but it also introduces dependency into data. Most statistical literature on RAD focused on statistical methodologies for mean response models without considering population heterogeneity. Ignoring such heterogeneity could introduce bias into conclusions. In addition, patients exposed to different environmental factors may respond to treatments very differently. All these are the main reasons why the efficacious treatments recommended by randomized clinical trials do not always work in day-to-day health care.This proposed research will develop new RAD to advocate the ethical advantages of RAD, propose statistical methods to account for the dependency introduced by RAD for various population heterogeneities, and establish efficient inferential methods for the proposed designs. This research will investigate the influence of adaptive randomization on statistical estimation, and employ that influence to improve the efficiency in learning on treatment effect. The population heterogeneities considered in this research include patients' and institutional characteristics and their interactions with treatments as well as patients' responses to a series of treatments.This research will establish statistical methods for RAD with covariates and interaction, especially qualitative interaction, as well as for RAD for dynamic treatment region clinical trials. The results will add to the statistical literature on estimation for dependent data under adaptive randomization as well as optimal designs of clinical trials for heterogeneous populations. The use of RAD to adaptively learn on qualitative interaction may improve the efficiency of detecting subpopulations. Statistical methods developed for clustered responses will add efficient ethical designs for multi-center trials. The integration of adaptive randomization into clinical trials of dynamic treatment region (DTR) will enable adaptive learning on the optimal DTR thus improving the ethical benefit and statistical efficiency.This proposed research addresses the methodological challenges in design and analysis of clinical trials when facing population heterogeneities. The developed methodologies incorporate population heterogeneities in RAD learning to advocate the ethical benefits and to improve statistical power. This research will add innovative designs of clinical trials and efficient statistical methods to statistical literature. The resulting toolbox can be generalized to other clinical settings where practitioners hope to efficiently identify optimal treatments for subpopulations.