| discSurv-package {discSurv} | R Documentation |
Discrete Survival Analysis
Description
Includes functions for data transformations, estimation, evaluation and simulation of discrete survival analysis. The most important functions are listed below:
contToDisc: Discretizes continuous time variable into a specified grid of censored data for discrete survival analysis.dataLong: Transform data from short format into long format for discrete survival analysis and right censoring.dataLongCompRisks: Transforms short data format to long format for discrete survival modelling in the case of competing risks with right censoring.dataLongTimeDep: Transforms short data format to long format for discrete survival modelling of single event analysis with right censoring.cIndex: Calculates the concordance index for discrete survival models (independent measure of time).dataLongSubDist: Converts the data to long format suitable for applying discrete subdistribution hazard modelling (competing risks).
Details
"DataShort" format is defined as data without repeated measurements. "DataSemiLong" format consists of repeated measurements, but there are gaps between the discrete time intervals. "DataLong" format is expanded to include all time intervals up to the last observation per individual.
| Package: | discSurv |
| Type: | Package |
| Version: | 2.0.0 |
| Date: | 2022-03-02 |
| License: | GPL-3 |
Author(s)
Thomas Welchowski welchow@imbie.meb.uni-bonn.de
Moritz Berger moritz.berger@imbie.uni-bonn.de
David Koehler koehler@imbie.uni-bonn.de
Matthias Schmid matthias.schmid@imbie.uni-bonn.de
References
Berger M, Schmid M (2018).
“Semiparametric regression for discrete time-to-event data.”
Statistical Modelling, 18, 322–345.
Berger M, Welchowski T, Schmitz-Valckenberg S, Schmid M (2019).
“A classification tree approach for the modeling of competing risks in discrete time.”
Advances in Data Analysis and Classification, 13, 965-990.
Berger M, Schmid M, Welchowski T, Schmitz-Valckenberg S, Beyersmann J (2020).
“Subdistribution Hazard Models for Competing Risks in Discrete Time.”
Biostatistics, 21, 449-466.
Schmid M, Tutz G, Welchowski T (2018).
“Discrimination Measures for Discrete Time-to-Event Predictions.”
Econometrics and Statistics, 7, 153-164.
Tutz G, Schmid M (2016).
Modeling discrete time-to-event data.
Springer Series in Statistics.