Includes functions for data transformations, estimation, evaluation and simulation of discrete survival analysis. Also discrete life table estimates are available. 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.
concorIndex: Calculates the concordance index for discrete survival models (independent measure of time).
simCompRisk: Simulates responses and covariates of discrete competing risk models.
dataLongSubDist: Converts the data to long format suitable for applying discrete subdistribution hazard modelling (competing risks).
| Package: | discSurv |
| Type: | Package |
| Version: | 1.4.2 |
| Date: | 2022-02-14 |
| License: | GPL-3 |
Main references:
Moritz Berger, Thomas Welchowski, Steffen Schmitz-Valckenberg and Matthias Schmid, (2019), A classification tree approach for the modeling of competing risks in discrete time, Advances in Data Analysis and Classification, vol. 13, issue 4, pages 965-990
Moritz Berger, Matthias Schmid, Thomas Welchowski, Steffen Schmitz-Valckenberg and Jan Beyersmann, (2018), Subdistribution Hazard Models for Competing Risks in Discrete Time, Biostatistics, Doi: 10.1093/biostatistics/kxy069
Matthias Schmid, Gerhard Tutz and Thomas Welchowski, (2017), Discrimination Measures for Discrete Time-to-Event Predictions, Econometrics and Statistics, Elsevier, Doi: 10.1016/j.ecosta.2017.03.008
Gerhard Tutz and Matthias Schmid, (2016), Modeling discrete time-to-event data, Springer series in statistics, Doi: 10.1007/978-3-319-28158-2
Further references:
Gerhard Tutz, (2012), Regression for Categorical Data, Cambridge University Press
Hajime Uno and Tianxi Cai and Lu Tian and L. J. Wei, (2007), Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models, Journal of the American Statistical Association
Gerds T. A. and M. Schumacher, (2006), Consistent estimation of the expected Brier score in general survival models with right-censored event times, Biometrical Journal, Vol. 48, No. 6, pages 1029-1040
Roger B. Nelsen, (2006), An introduction to Copulas, Springer Science+Business Media, Inc.
Patrick J. Heagerty and Yingye Zheng, (2005), Survival Model Predictive Accuracy and ROC Curves, Biometrics 61, 92-105
Steele Fiona and Goldstein Harvey and Browne William, (2004), A general multilevel multistate competing risks model for event history data Statistical Modelling, volume 4, pages 145-159
Jerald F. Lawless, (2000), Statistical Models and Methods for Lifetime Data, 2nd edition, Wiley series in probability and statistics
Jason P. Fine and Robert J. Gray, (1999), A proportional hazards model for the subdistribution of a competing risk, Journal of the American statistical association, Vol. 94, No. 446, pages 496-509
Ludwig Fahrmeir, (1997), Discrete failure time models, LMU Sonderforschungsbereich 386, Paper 91, https://epub.ub.uni-muenchen.de/
Tilmann Gneiting and Adrian E. Raftery, (2007), Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association 102 (477), 359-376
M. J. van der Laan and J. M. Robins, (2003), Unified Methods for Censored Longitudinal Data and Causality, Springer, New York
Wiji Narendranathan and Mark B. Stewart, (1993), Modelling the probability of leaving unemployment: competing risks models with flexible base-line hazards, Applied Statistics, pages 63-83
W. A. Thompson Jr., (1977), On the Treatment of Grouped Observations in Life Studies, Biometrics, Vol. 33, No. 3
William H. Kruskal, (1958), Ordinal Measures of Association, Journal of the American Statistical Association, Vol. 53, No. 284, pages 814-861