Direct likelihood inference and sensitivity analysis for competing risks regression with missing causes of failure

Biometrics
M Moreno-BetancurAurélien Latouche

Abstract

Competing risks arise in the analysis of failure times when there is a distinction between different causes of failure. In many studies, it is difficult to obtain complete cause of failure information for all individuals. Thus, several authors have proposed strategies for semi-parametric modeling of competing risks when some causes of failure are missing under the missing at random (MAR) assumption. As many authors have stressed, while semi-parametric models are convenient, fully-parametric regression modeling of the cause-specific hazards (CSH) and cumulative incidence functions (CIF) may be of interest for prediction and is likely to contribute towards a fuller understanding of the time-dynamics of the competing risks mechanism. We propose a so-called "direct likelihood" approach for fitting fully-parametric regression models for these two functionals under MAR. The MAR assumption not being verifiable from the observed data, we propose an approach for performing sensitivity analyses to assess the robustness of inferences to departures from this assumption. The method relies on so-called "pattern-mixture models" from the missing data literature and was evaluated in a simulation study. This sensitivity analysis approach is appl...Continue Reading

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Citations

Jul 1, 2016·Epidemiology·Margarita Moreno-BetancurGrégoire Rey
Apr 4, 2018·Statistics in Medicine·Daniel Mark TompsettIan R White
Aug 21, 2018·American Journal of Epidemiology·Margarita Moreno-BetancurJohn B Carlin

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