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Activity Number: 343 - SPEED: Tests, Trials, Biomarkers, and Other Topics in Biometrics
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #329031
Title: Statistical Precision of Time-to-Event Endpoint in Single Arm Observational Study Using Monte Carlo Simulation
Author(s): Meijing Wu* and Hongwei Wang and Yabing Mai and Dajun Tian
Companies: AbbVie and AbbVie Inc and AbbVie, Inc and Chiltern
Keywords: time-to-event; statistical precision; Monte Carlo simulation; single arm

Time-to-event endpoints are widely used in oncology studies. Various statistical methods have been developed for the sample size and power considerations of randomized clinical trial with time-to-event endpoints. With more observational studies being conducted for real world evidence generation, it is important to obtain some insight on the statistical precision provided by a given sample size in the observational studies using real world data. In this presentation we evaluated the statistical precision of time-to-event endpoints in single-arm observational studies via practical and applicable parametric and nonparametric methods. A Monte Carlo simulation process was conducted under the survival analysis framework incorporating both events and censored observations in multiple scenarios. Different distributions (e.g., Exponential, Weibull, and log-normal) were implemented to simulate the time-to-event and time-to-censoring data with different median survival time, drop-out rates, sample sizes and study duration. Practical guidance on the assessment of statistical precision and robustness were demonstrated with the simulated data.

Authors who are presenting talks have a * after their name.

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