TL;DR
This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercur-rent events (ICE) under the five strategies outlined in the ICH E9 (R1) adden-dum.
Evaluating treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events (ICEs), e.g., treatment discontinuation or competing events. While the ICH E9 (R1) addendum outlines five strategies to address these challenges, accessible software implementing these methods is limited. T...
Background Parametric time-to-event (TTE) modeling is widely used in clinical trials to evaluate treatment response, patient prognosis, and drug-related adverse events (AEs). As follow-up assessments increasingly capture recurrent AEs, there is a growing need for analytical approaches that extend beyond traditional sin...
Composite endpoints are common in clinical trials for binary, time-to-event or recurrent outcomes. Recently, there has been interest in estimands for continuous endpoints that employ a composite variable strategy for handling intercurrent events, whereby a value on the continuous scale is defined as the outcome. In thi...
Two-sample survival probability curves (TSSPCs) provide a concise way to visualize treatment differences in randomized controlled trials (RCTs). In this paper, we propose various area under the curve (AUC) metrics for TSSPCs as numerical measures for treatment effects. First, in the same spirit as the restricted mean s...
In randomized trials, repeated interim analyses of accumulating data must keep the Type I error controlled. Sequential analyses that use e-values allow continuous assessment of evidence while controlling their own Type I error. We present e-RT, a randomization-based e-value method for continuous trial monitoring,...
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