Marginal Structural Illness-Death Models for Semi-competing Risks Data

Author:

Zhang Yiran,Ying Andrew,Edland Steve,White Lon,Xu RonghuiORCID

Funder

National Institutes of Health

Publisher

Springer Science and Business Media LLC

Subject

Biochemistry, Genetics and Molecular Biology (miscellaneous),Statistics and Probability

Reference48 articles.

1. Alvares D, Haneuse S, Lee C, Lee KH (2019) Semicomprisks: an R package for the analysis of independent and cluster-correlated semi-competing risks data. R J 11(1):376

2. Andersen PK, Hansen LS, Keiding N (1991) Non-and semi-parametric estimation of transition probabilities from censored observation of a non-homogeneous Markov process. Scand J Stat 18:153–167

3. Breslow NE, Wellner JA (2007) Weighted likelihood for semiparametric models and two-phase stratified samples, with application to Cox regression. Scand J Stat 34(1):86–102

4. Chastang C, Byar D, Piantadosi S (1988) A quantitative study of the bias in estimating the treatment effect caused by omitting a balanced covariate in survival models. Stat Med 7(12):1243–1255

5. Comment L, Mealli F, Haneuse S, Zigler C (2019) Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicompeting risks. arXiv preprint arXiv:1902.09304

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