Affiliations: [a] Department of Statistics, Durban University of Technology, Durban, South Africa | [b] School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa | [c] School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa
Correspondence:
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Corresponding author: Bonginkosi D. Ndlovu, Department of Statistics, Durban University of Technology, Durban, South Africa. E-mail: [email protected].
Abstract: Larson and Dinse (1985) have introduced the mixture model as an additional competing risks model. In the same article, the authors have suggested that this model can be upscaled to handle the presence of missing failure causes in data. We respond to this proposal in this article and develop a regression model for analysis of data that comes with this complication. We also demonstrate that, with minimal adjustments, the proposed model can be applied in discrete time. This development will be of benefit to discrete time competing risks as analysis of data with this complication is a subject that has not received adequate attention. The mixture model has two components, the incidence and the latency component. It is demonstrated that the parameters related to the model for the latency component as proposed by Larson and Dinse (1985) can be estimated by applying a certain Poisson regression.
Keywords: Missing failure causes, mixture competing risks model, discrete time competing risks, poisson regression model
