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Abstract Details
Activity Number:
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89
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #303056 |
Title:
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Assessing Evaluation-Time Bias in Progression-Free Survival Data Analysis
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Author(s):
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Chen Hu and Kalyanee Viraswami-Appanna*+ and Bharani Bharani-Dharan
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Companies:
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University of Michigan and Novartis and Novartis
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Address:
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180 Park Avenue, Florham Park, NJ, 07932,
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Keywords:
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Interval censoring ;
PFS ;
oncology trials ;
right censoring ;
type-I error ;
power
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Abstract:
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In oncology clinical trials, progression-free survival (PFS) is used widely as a primary or a secondary endpoint. While PFS can be naturally viewed as interval-censored data by the latent nature of disease progression, interval-censoring analysis methods have not been widely used in practice for PFS analysis. In addition, different pattern of evaluation time and non-adherence of evaluation time as per protocol between randomized arms may also bias analysis results. In order to understand the performance of statistical methods for interval-censored data in phase II and III oncology trials under various real-world scenarios, we conducted a series of Monte Carlo simulations using both interval-censoring and conventional analysis methods to assess the impacts of sample size, treatment effect, evaluation interval, imbalance of evaluation schedule, protocol adherence and reporting lags in terms of treatment effect estimation and hypothesis testing. We aim to provide practical recommendations on using interval-censoring analysis methods for phase II and III oncology trials.
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