The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models

• Published in: Computer methods and programs in biomedicine Vol. 99; no. 3; pp. 261 - 274
• Main Authors: de Wreede, Liesbeth C, Fiocco, Marta, Putter, Hein
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.09.2010
• Subjects: Algorithms; Competing risks models; Cox models; Data Interpretation, Statistical; Humans; Internal Medicine; Liver Cirrhosis; Markov Chains; Markov models; Models, Statistical; Multi-state models; Multivariate Analysis; Normal Distribution; Other; Proportional Hazards Models; Regression Analysis; Risk Assessment - methods; Software; Statistics, Nonparametric; Survival Analysis; Survival analysis; Multi-state models; Markov models; Cox models; Software; Competing risks models
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2010.01.001

Abstract In recent years, multi-state models have been studied widely in survival analysis. Despite their clear advantages, their use in biomedical and other applications has been rather limited so far. An important reason for this is the lack of flexible and user-friendly software for multi-state models. This paper introduces a package in R , called ‘ mstate ’, for each of the steps of the analysis of multi-state models. It can be applied to non- and semi-parametric models. The package contains functions to facilitate data preparation and flexible estimation of different types of covariate effects in the context of Cox regression models, functions to estimate patient-specific transition intensities, dynamic prediction probabilities and their associated standard errors (both Greenwood and Aalen-type). Competing risks models can also be analyzed by means of mstate , as they are a special type of multi-state models. The package is available from the R homepage http://cran.r-project.org. We give a self-contained account of the underlying mathematical theory, including a new asymptotic result for the cumulative hazard function and new recursive formulas for the calculation of the estimated standard errors of the estimated transition probabilities, and we illustrate the use of the key functions of the mstate package by the analysis of a reversible multi-state model describing survival of liver cirrhosis patients.