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Title page for ETD etd-06132017-150729


Type of Document Master's Thesis
Author Olson, Molly Ann
Author's Email Address molly.a.olson@vanderbilt.edu
URN etd-06132017-150729
Title A Comparison of Approaches for Unplanned Sample Size Changes in Phase II Clinical Trials
Degree Master of Science
Department Biostatistics
Advisory Committee
Advisor Name Title
Tatsuki Koyama Committee Chair
Jeffrey Blume Committee Member
Keywords
  • sample size deviations
  • likelihood
  • frequentist
  • phase II
  • clinical trial
  • two-stage
Date of Defense 2017-06-13
Availability restrictsix
Abstract
Oncology phase II clinical trials are used to evaluate the initial effect of a new regimen to determine if there warrants further study in a phase III clinical trial. Two-stage designs with an early futility stop are commonly used in these phase II trials. It is common for attained sample sizes in these trials to be different from the designed sample sizes due to over- and under- enrollment. Currently, when the attained sample size differs from that planned, common practice is to treat the attained sample sizes as planned, and this practice leads to invalid inference. In this thesis, we examine the problems and solutions in hypothesis testing for two-stage phase II clinical trial designs when attained sample sizes differ from the planned sample sizes. We describe existing methods for redesigning trials when there is over- or under-enrollment in either the first or second stage and introduce a new method for redesigning a two-stage clinical trial when the first stage sample size deviates from planned. We focus our investigation when there is over- or under-enrollment in the first stage. We compare the frequentist methods of Chang et al., Olson and Koyama, and the Likelihood two-stage design by applying these methods to two-stage designs with deviations in the first stage of +/- 10. We examine type I error rates, power, probability of early termination and expected sample size under the null hypothesis in a number of two-stage designs. We also compare error rates in these methods using a Monte Carlo simulation.
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