GPW.logrank: Generalized Piecewise Weighted Logrank Test

Description

Compute the p-value based on the generalized piecewise weighted log-rank test when the treatment time-lag effect is present and the lag duration varies heterogeneously from individual to individual or from study to study, within a certain domain and following a uniform pattern.

Usage

GPW.logrank(data, obs.time, time.to.event, event.status, trt.group, tl, tu)

Value

The p-value of the test.

Arguments

data: Data frame
obs.time: Column name in data for the observational time.
time.to.event: Column name in data for the event time.
event.status: Column name in data for the event status, where 0 denotes being censored, and 1 denotes events.
trt.group: Column name in data for the treatment group, where 0 denotes controls, and 1 denotes treated subjects.
tl: Lower bound of delayed duration domain
tu: Upper bound of delayed duration domain

Author

Zhenzhen Xu <Zhenzhen.Xu@fda.hhs.gov> , Boguang Zhen<Boguang.Zhen@fda.hhs.gov>, Yongsoek Park <yongpark@pitt.edu> and Bin Zhu <bin.zhu@nih.gov>

References

Xu, Z., Park, Y., Zhen, B. & Zhu, B. (2017). Achieving optimal power of logrank test with random treatment time-lag effect. Biometrika. Under review.

Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017). Designing therapeutic cancer vaccine trials with delayed treatment effect. Statistics in medicine, 36(4), 592-605.

Examples

Run this code

  data(data, package="DelayedEffect.Design")
  GPW.logrank(data, "X", "time_to_event", "event_status", "Z", 30, 30*11)

Run the code above in your browser using DataLab