Inference based on the EM algorithm for the competing risks model with masked causes of failure

• Published in: Biometrika 2004-09, Vol.91 (3), p.543-558
• Main Authors: Craiu, Radu V., Duchesne, Thierry
• Format: Article
• Language: English
• Subjects: Algorithms; Applications; Biology, psychology, social sciences; Data collection; Data masking; Datasets; Estimators; Exact sciences and technology; Grouped data; Inference; Likelihood ratio test; Mathematics; Maximum likelihood estimators; Missing data; Modeling; Musical intervals; Nonparametric inference; Parametric inference; Parametric models; Piecewise‐constant hazard; Probability and statistics; Proportional hazards; Ratio test; Reliability, life testing, quality control; Robustness; Sciences and techniques of general use; SEM algorithm; Standard error; Statistics; Survival analysis; Symmetry assumption; Time‐varying masking probability
• ISSN: 0006-3444; 1464-3510
• DOI: 10.1093/biomet/91.3.543

In this paper we propose inference methods based on the EM algorithm for estimating the parameters of a weakly parameterised competing risks model with masked causes of failure and second‐stage data. With a carefully chosen definition of complete data, the maximum likelihood estimation of the cause‐specific hazard functions and of the masking probabilities is performed via an EM algorithm. Both the E‐ and M‐steps can be solved in closed form under the full model and under some restricted models of interest. We illustrate the flexibility of the method by showing how grouped data and tests of common hypotheses in the literature on missing cause of death can be handled. The method is applied to a real dataset and the asymptotic and robustness properties of the estimators are investigated through simulation.