Analysis with Competing Risks Implementation of the following methods
for event history analysis. Risk regression models for survival
endpoints also in the presence of competing risks are fitted using
binomial regression based on a time sequence of binary event status
variables. A formula interface for the Fine-Gray regression model and an
interface for the combination of cause-specific Cox regression models. A
toolbox for assessing and comparing performance of risk predictions
(risk markers and risk prediction models). Prediction performance is
measured by the Brier score and the area under the ROC curve for binary
possibly time-dependent outcome. Inverse probability of censoring
weighting and pseudo values are used to deal with right censored data.
Lists of risk markers and lists of risk models are assessed
simultaneously. Cross-validation repeatedly splits the data, trains the
risk prediction models on one part of each split and then summarizes and
compares the performance across splits.
Installed Size: 2.2 MB
Architectures: arm64 amd64