Type of Document	Dissertation
Author	Teel, Chen
URN	etd-11272010-150936
Title	Improved sample size re-estimation in adaptive clinical trials without unblinding
Degree	Doctor of Philosophy
Program	Statistics
School	School of Arts and Sciences
Advisory Committee	Advisor Name Title Allan Sampson Committee Chair Abdus Wahed Committee Member Leon Gleser Committee Member Yu Cheng Committee Member
Keywords	Adaptive design EM algorithm Type I error rate Sample size re-estimation Blinded estimation Conditional Bernoulli distribution
Date of Defense	2010-12-14
Availability	restricted
Abstract Sample size calculations in clinical trials depend on good estimates of the standard devotion. Due to the uncertainty in the planning phase, adaptive sample size designs have been used to re-estimate the standard deviation based on interim data and adjust the sample size as necessary. Our research concentrates on carrying out the sample size re-estimation without obtaining the treatment identities. Gould and Shih treated the data at the interim as coming from a mixture of two normal distributions with common standard deviation. In order to adjust the sample size, they used EM algorithm to obtain the MLE of the standard deviation while keeping treatment identities blinded. However, the approach has been criticized in the literature and our simulation studies show that Gould and Shih's EM algorithm sometimes obtains incorrect boundary modes as estimates of the standard deviation. In our research, we establish a new procedure to re-estimate sample size without breaking the blind but using additional information concerning randomization structure at the interim. We enhance Gould and Shih’s EM procedure by utilizing the conditional Bernoulli model to incorporate the available information that equal numbers of subjects are observed at the interim stage. Properties of the proposed enhanced EM estimator are investigated in detail. Furthermore, we use the full information of the blocked randomization schedule in the enhanced EM algorithm that the numbers of subjects are equal across treatment groups within each randomization block. With increased information that occurs with increasing block sizes, the accuracy of the estimation of the standard deviation improves. More specifically, the estimator has quite a small bias when the block size is small which is fairly common the case in clinical trials. Moreover, for the case of two treatment groups, the preservation of the actual type I error rate when using the standard t-test at the end of the trial is verified through a simulation study in many parameter scenarios. We also analytically computed and simulated the actual power and the expected sample size. Finally, we develop the details of sample size re-estimation for multi-center clinical trials, where we have the randomization schedule blocked within center.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access  TEEL04052011.pdf 1.44 Mb 00:06:39 00:03:25 00:02:59 00:01:29 00:00:07 indicates that a file or directory is accessible from the University of Pittsburgh network only.

If you have questions or comments please send mail to ETD-Feedback or view
the University of Pittsburgh Electronic Theses and Dissertations (ETD) Project page.