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A random walk approach for quantifying uncertainty in group sequential survival trials

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  • Gillen, Daniel L.

Abstract

The development of group sequential methods has produced multiple criteria that are used to guide the decision of whether a clinical trial should be stopped early given the data observed at the time of an interim analysis. However, the potential for time-varying treatment effects should be considered when monitoring survival endpoints. In order to quantify uncertainty in future treatment effects it is necessary to consider future alternatives which might reasonably be observed conditional upon data collected up to the time of an interim analysis. A method of imputation of future alternatives using a random walk approach that incorporates a Bayesian conditional hazards model and splits the prior distribution for model parameters across regions of sampled and unsampled support is proposed. By providing this flexibility, noninformative priors can be used over regions of sampled data while providing structure to model parameters over future time intervals. The result is that inference over areas of sampled support remains consistent with commonly used frequentist statistics while a rich class of predictive distributions of treatment effect over the maximal duration of a trial are generated to assess potential treatment effects which may be plausibly observed if the trial were to continue. Selected operating characteristics of the proposed method are investigated via simulation and the approach is applied to survival data stemming from trial 002 of the Community Programs for Clinical Research on AIDS (CPCRA) study.

Suggested Citation

  • Gillen, Daniel L., 2009. "A random walk approach for quantifying uncertainty in group sequential survival trials," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 609-620, January.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:3:p:609-620
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    References listed on IDEAS

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    1. Daniel L. Gillen & Scott S. Emerson, 2005. "A Note on P-Values under Group Sequential Testing and Nonproportional Hazards," Biometrics, The International Biometric Society, vol. 61(2), pages 546-551, June.
    2. Gillen, Daniel L. & Emerson, Scott S., 2007. "Nontransitivity in a class of weighted logrank statistics under nonproportional hazards," Statistics & Probability Letters, Elsevier, vol. 77(2), pages 123-130, January.
    3. Gary L. Rosner, 2005. "Bayesian Monitoring of Clinical Trials with Failure-Time Endpoints," Biometrics, The International Biometric Society, vol. 61(1), pages 239-245, March.
    4. Dean A. Follmann & Paul S. Albert, 1999. "Bayesian Monitoring of Event Rates with Censored Data," Biometrics, The International Biometric Society, vol. 55(2), pages 603-607, June.
    5. Ian W. McKeague & Mourad Tighiouart, 2000. "Bayesian Estimators for Conditional Hazard Functions," Biometrics, The International Biometric Society, vol. 56(4), pages 1007-1015, December.
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