Topics in Design and Analysis of Clinical Trials (DRAFT)

optimal;cox model;two-stage;group-sequential

Lokhnygina, Yuliya ; Marie Davidian, Committee Member,Dennis Boos, Committee Member,Anastasios A. Tsiatis, Committee Chair,Daowen Zhang, Committee Member,Lokhnygina, Yuliya ; Marie Davidian ; Committee Member ; Dennis Boos ; Committee Member ; Anastasios A. Tsiatis ; Committee Chair ; Daowen Zhang ; Committee Member

University:North Carolina State University

关键词: optimal;    cox model;    two-stage;    group-sequential;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/4597/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

In the first part of this dissertation we derive optimal two-stage adaptive group-sequential designs for normally distributed data which achieve the minimum of a mixture of expected sample sizes at the range of plausible values of a normal mean. Unlike standard group-sequential tests, our method is adaptive in that it allows the group size at the second look to be a function of the observed test statistic at the first look. Using optimality criteria, we construct two-stage designs which we show have advantage over other popular adaptive methods. The employed computational method is a modification of the backward induction algorithm applied to a Bayesian decision problem.Two-stage randomization designs (TSRD) are becoming increasingly common in oncology and AIDS clinical trials as they make more efficient use of study participants to examine therapeutic regimens.In these designs patients are initially randomized to an induction treatment, followed by randomization to a maintenance treatment conditional on their induction response and consent to further study treatment. Broader acceptance of TSRDs in drug development may hinge on the ability to make appropriate intent-to-treat type inference as to whether an experimental induction regimen is better than a standard regimen in the absence of maintenance treatment within this design framework.Lunceford, Davidian, and Tsiatis (2002, Biometrics 58, 48-57) introduced an inverse-probability-weighting based analytical framework for estimating survival distributions and mean restricted survival times, as well as for comparing treatment policies in the TSRD setting.In practice Cox regression is widely used, and in the second part of this dissertation we extend the analytical framework of Lunceford et. al. to derive a consistent estimator for the log hazard in the Cox model and a robust score test to compare treatment policies. Large sample properties of these methods are derived and illustrated via a simulation study. Considerations regarding the application of TSRDs compared to single randomization designs are discussed.