Year: 2018
Type: dissertation
Abstract: We discuss methods for comparing effects of two treatments A and B. We investigate \nthe performance of response-adaptive (RA) and covariate-adjusted response-adaptive \n(CARA) designs in multi-center clinical trials. First, we discuss applying RA designs \nto maximize the well-being of participating patients in multi-center clinical trials. We \nassume that the centers are selected from a large population of centers and develop a \ngeneralized linear mixed model (GLMM) to examine the treatment effect. The asymptotic \nproperties of the maximum likelihood (ML) estimators of model parameters are \nderived using the in \nuence function method. We verified their theoretical properties \nthrough simulation studies. The techniques are then applied to a real data that were \nobtained from a multi-center clinical trial designed to compare two cream preparations \n(active drug/control) for treating an infection. Secondly, we investigate the efficiency for estimates of model parameters and ethics for participating patients among \nRA, CARA, and completely randomized (CR) designs for a generalized linear model \n(GLM). We consider the logit model to measure efficiency and ethics. Furthermore, \nwe showed that ML estimators of GLM parameters are consistent and asymptotically \nfollow multivariate normal distribution for adaptive designs. A simulation study was \nconducted to verify these theoretical results. Finally, we provide a justification of why \nasymptotic results for Wald-type tests for adaptive designs can be used. We proved \nthat the choice of adaptive designs affects the statistical power of hypothesis testing \nvia these quantities: the target allocation proportion, the bias of the randomization \nprocedure from the target, and the variability induced by the randomization process. \nMoreover, we showed that the statistical power increases when the design variability \ndecreases for a covariate in a logit model. Our theoretical findings are verified by \nsimulation results.
Author: Selvakkadunko Selvaratnam
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2808141285