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Abstract Details
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Activity Number:
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627
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #301283 |
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Title:
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Impact of Treatment Crossover on Survival Analysis in Clinical Trials
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Author(s):
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Yan Xu*+ and Ying Wan
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Companies:
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University of California at Santa Barbara and Johnson & Johnson Pharmaceutical R&D, LLC
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Address:
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, santa barbara, CA, 93106,
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Keywords:
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Treatment crossover ;
Sample Size ;
Modified Lakatos method ;
Survival analysis
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Abstract:
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Treatment crossover is a common problem for overall survival analysis in clinical trials. Specifically, a proportion of patients initially assigned to control group would cross over to treatment group at the time of disease progression or relapse. The treatment crossover would confound overall survival analysis and cause a substantial loss of power in log rank test. In our study, the theoretical survival distribution adjusted for crossover is derived under an exponential survival assumption and used to evaluate the impact of crossover on analysis. Adjusted sample size is calculated for log rank test based on Lakato's work (1986, 1988). Simulation results show that power can be preserved in presence of crossovers. The problems associated with estimation of treatment effect by Cox PH and accelerated failure time model are studied. Extension of our work to informative crossover will be discussed.
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Authors who are presenting talks have a * after their name.
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