@article{Information:4793,
      recid = {4793},
      author = {Schwartz, Daniel Evan},
      title = {Maximizing and Borrowing Information in Randomized Trials},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2022-08},
      pages = {188},
      abstract = {To learn efficiently from randomized experiments, it is  critical to understand how they may be designed and  analyzed to best accumulate and interpret the statistical  information that their data provide. To that end, this  dissertation includes research on three important problems.  In the first paper, we develop promising Bayesian  uncertainty-directed (BUD) designs for faster and more  informative dose-ranging clinical trials. The basic  principle is to randomize new patients more often to doses  that are expected to generate the most added information  about the optimal dose, averaged over the posterior  predictive distribution of their still unknown outcomes.  This typically means assigning new patients to doses that  are understudied relative to how strongly the data suggest  they are optimal. We also use Bayesian model averaging of  dose-response curves to robustly accelerate learning by  letting each dose’s effectiveness partially inform those of  nearby doses. This butts against a computational challenge  that has made BUDs with nontrivial data models impractical,  so we develop an efficient Sequential Monte Carlo strategy  to enable this appealing approach to multi-arm trial  design. In the second paper, we propose a new model for  borrowing from historical controls in efficacy trials. This  model is called SPx ("synthetic prior with covariates") and  uses carefully posed Bayesian model averaging to balance  between competing philosophies about how the historical and  new data are related. In simulations and a case study we  show how SPx quickly distinguishes between historical data  that are helpful and historical data that are misleading,  leading to a smaller control group in the new trial to the  extent reasonable. In the third paper, we consider the  often overlooked problem that in multi-site efficacy trials  there are often substantive grounds to believe that the  effectiveness of each site may be related to its size or  randomization ratio. We call this phenomenon endogeneity of  design. We re-evaluate treatment effect estimators commonly  used in practice and derive asymptotic and finite-sample  results as well as run extensive simulations to  characterize their performance under this more realistic  assumption. In a detailed case study of a landmark trial in  education, we take a Bayesian viewpoint to evaluate the  likely performance of the popular estimators in this  specific setting. The implication is that endogeneity of  design can significantly complicate analysis of multi-site  trials, and existing methods are not well-equipped to  handle this situation. For all three papers, code to  reproduce the main analyses and simulations is included as  supplementary files.},
      url = {http://knowledge.uchicago.edu/record/4793},
      doi = {https://doi.org/10.6082/uchicago.4793},
}