tteICE: Treatment Effect Estimation for Time-to-Event Data with Intercurrent Events

This package aims to analyze treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICE) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025) doi:10.1002/sim.70091. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, the data involve a primary outcome event and, potentially, an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation are presented.

Details

Main functions:

• tteICE Using formula to fit cumulative incidence functions (CIFs) for competing/semicompeting risk time-to-event data with intercurrent events.

• scr.tteICE Fit CIFs for semicompeting risk time-to-event data with intercurrent events.

• surv.tteICE Fit CIFs for competing risk time-to-event with intercurrent events.

• plot.tteICE Plot results from 'tteICE' objects.

• print.tteICE Print a short summary of results from 'tteICE' objects

• summary.tteICE Summarize results from 'tteICE' objects

• predict.tteICE Predict risks for 'tteICE' objects at specific time points

• tteICEShiny Interactive Shiny app for the 'tteICE' package

Example data:

• bmt Data from Section 1.3 of Klein and Moeschberger (1997)

See also

Useful links:

• https://github.com/mephas/tteICE

• https://mephas.github.io/tteICE/

• Report bugs at https://github.com/mephas/tteICE/issues

Author

Maintainer: Yi Zhou yzhou@pku.edu.cn

Authors:

• Yuhao Deng dengyuhao@pku.edu.cn