Pseudo-observations for competing risks with covariate dependent censoring

Regression analysis for competing risks data can be based on generalized estimating equations. For the case with right censored data, pseudo-values were proposed to solve the estimating equations. In this article we investigate robustness of the pseudo-values against violation of the assumption that the probability of not being lost to follow-up (un-censored) is independent of the covariates. Modified pseudo-values are proposed which rely on a correctly specified regression model for the censoring times. Bias and efficiency of these methods are compared in a simulation study. Further illustration of the differences is obtained in an application to bone marrow transplantation data and a corresponding sensitivity analysis.

[1]  Maja Pohar Perme,et al.  Pseudo-observations in survival analysis , 2010, Statistical methods in medical research.

[2]  R. Gill.  Censoring and stochastic integrals , 1980 .

[3]  Odd Aalen,et al.  Nonparametric Estimation of Partial Transition Probabilities in Multiple Decrement Models , 1978 .

[4]  Mei-Jie Zhang,et al.  Predicting cumulative incidence probability by direct binomial regression , 2008 .

[5]  Robert Gray,et al.  A Proportional Hazards Model for the Subdistribution of a Competing Risk , 1999 .

[6]  Thomas A Gerds,et al.  Absolute risk regression for competing risks: interpretation, link functions, and prediction , 2012, Statistics in medicine.

[7]  J. Klein,et al.  Generalised linear models for correlated pseudo‐observations, with applications to multi‐state models , 2003 .

[8]  Niels Keiding,et al.  Statistical Models Based on Counting Processes , 1993 .

[9]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[10]  D. Schoenfeld,et al.  A proportional hazards model for truncated AIDS data. , 1993, Biometrics.

[11]  W. J. Hall,et al.  Information and Asymptotic Efficiency in Parametric-Nonparametric Models , 1983 .

[12]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical , 2002 .

[13]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[14]  M. Schumacher,et al.  On pseudo-values for regression analysis in competing risks models , 2009, Lifetime data analysis.

[15]  John P Klein,et al.  Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function , 2005, Biometrics.

[16]  J P Klein,et al.  Results of allogeneic bone marrow transplants for leukemia using donors other than HLA-identical siblings. , 1997, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.