Software Application Profile: dynamicLM—a tool for performing dynamic risk prediction using a landmark supermodel for survival data under competing risks

Abstract

Abstract Motivation Providing a dynamic assessment of prognosis is essential for improved personalized medicine. The landmark model for survival data provides a potentially powerful solution to the dynamic prediction of disease progression. However, a general framework and a flexible implementation of the model that incorporates various outcomes, such as competing events, have been lacking. We present an R package, dynamicLM, a user-friendly tool for the landmark model for the dynamic prediction of survival data under competing risks, which includes various functions for data preparation, model development, prediction and evaluation of predictive performance. Implementation dynamicLM as an R package. General features The package includes options for incorporating time-varying covariates, capturing time-dependent effects of predictors and fitting a cause-specific landmark model for time-to-event data with or without competing risks. Tools for evaluating the prediction performance include time-dependent area under the ROC curve, Brier Score and calibration. Availability Available on GitHub [https://github.com/thehanlab/dynamicLM].