Online Program

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Friday, September 14
Fri, Sep 14, 9:15 AM - 9:55 AM
Atrium
Poster Session

Issues in Nonparametric Maximum Likelihood Estimation Method for Semi-Competing Risks Data (300686)

Giorgos Bakoyannis, Indiana University 
*Jing Li, Indiana University 
Ying Zhang, Indiana University 

Keywords: dementia, EM algorithm, frailty, illness-death model, non-parametric; semi-competing risks

Semi-competing risks data are a variant of competing risks data. They occur when non-terminal events can be censored by well-defined terminal events, but not vice versa. The so-called illness-death model has been widely studied, which can be used to characterize semi-competing risks data. We utilize the shared gamma frailty Markov-model for non-parametric maximum likelihood estimation (NPMLE) of three hazards, namely healthy status to the non-terminal event, and healthy status to the terminal event with and without experiencing the non-terminal event. Particularly, we investigate when the maximum likelihood approach fails due to ill-behaved likelihood with simulation studies, and conclude that the conventional NPMLE for semi-competing risks data may not always be valid in practice. Practical guidelines based on plotting the profile likelihood are provided to determine whether semi-competing risks data can be analyzed with the NPMLE method under the shared gamma frailty Markov-Model. It is applied to the Indianapolis Ibadan Dementia Project data to illustrate how the incidence of dementia impacts mortality.