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Improved sample size re-estimation in adaptive clinical trials without unblinding

Teel, Chen (2011) Improved sample size re-estimation in adaptive clinical trials without unblinding. Doctoral Dissertation, University of Pittsburgh.

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    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.


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    Item Type: University of Pittsburgh ETD
    ETD Committee:
    ETD Committee TypeCommittee MemberEmail
    Committee ChairSampson, Allanasampson@pitt.edu
    Committee MemberWahed, AbdusWahedA@edc.pitt.edu
    Committee MemberGleser, Leongleser@pitt.edu
    Committee MemberCheng, Yuyucheng@pitt.edu
    Title: Improved sample size re-estimation in adaptive clinical trials without unblinding
    Status: Unpublished
    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.
    Date: 30 June 2011
    Date Type: Completion
    Defense Date: 14 December 2010
    Approval Date: 30 June 2011
    Submission Date: 27 November 2010
    Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
    Patent pending: No
    Institution: University of Pittsburgh
    Thesis Type: Doctoral Dissertation
    Refereed: Yes
    Degree: PhD - Doctor of Philosophy
    URN: etd-11272010-150936
    Uncontrolled Keywords: Sample size re-estimation; Type I error rate; Conditional Bernoulli distribution; EM algorithm; Adaptive design; Blinded estimation
    Schools and Programs: Dietrich School of Arts and Sciences > Statistics
    Date Deposited: 10 Nov 2011 15:06
    Last Modified: 15 May 2012 10:36
    Other ID: http://etd.library.pitt.edu/ETD/available/etd-11272010-150936/, etd-11272010-150936

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