|
Abstract:
|
Dynamic risk prediction has recently attracted attention because of its ability to incorporate time-varying information such as repeatedly measured covariates and intermediate event status into the estimation of the probability of failure. Using a landmark data set, the prediction is updated by subsetting the data with left-truncation at the landmark time and enforcing administrative censoring at the prediction horizon time. The landmark Cox model provides a valid estimation of the probability of failure at the horizon time. Risk difference, defined by the difference in conditional probabilities of failure, serves as an accessible, easily interpreted measurement of effect size when comparing two treatment groups.
In this study we proposed a test statistic that could be used to compare two conditional probabilities of failure. We derived an analytic formula to calculate the sample size needed to reach the desired risk difference, significance level, and power. We also investigated factors that can affect the power and sample size of the test and conducted simulation studies under various settings to investigate their impact.
|
