Author ORCID Identifier
Statistics in Medicine
We propose a method for calculating power and sample size for studies involving interval-censored failure time data that only involves standard software required for fitting the appropriate parametric survival model. We use the framework of a longitudinal study where patients are assessed periodically for a response and the only resultant information available to the investigators is the failure window: the time between the last negative and first positive test results. The survival model is fit to an expanded data set using easily computed weights. We illustrate with a Weibull survival model and a two-group comparison. The investigator can specify a group difference in terms of a hazards ratio. Our simulation results demonstrate the merits of these proposed power calculations. We also explore how the number of assessments (visits), and thus the corresponding lengths of the failure intervals, affect study power. The proposed method can be easily extended to more complex study designs and a variety of survival and censoring distributions.
Kim, H., Williamson, J. M., & Lin, H. (2016). Power and Sample Size Calculations for Interval-Censored Survival Analysis. Statistics in Medicine, 35 (8), 1390-1400. https://doi.org/10.1002/sim.6832
This is the peer reviewed version of the following article: Kim, H., Williamson, J. M., & Lin, H. (2016). Power and Sample Size Calculations for Interval-Censored Survival Analysis. Statistics in Medicine, 35 (8), 1390-1400 which has been published in final form at https://doi.org/10.1002/sim.6832
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