Speaker: Ariane Bercu from Bordeaux Population Health
Title: Regularized multi-state model for covariate selection with interval-censored survival data
This seminar will be in English
Abstract: The illness-death model for interval-censored data accounts for the uncertainty on the time of dementia and the probability of having dementia until the last visit or death. In this work, we proposed a new regularised estimation procedure for a high dimensional illness-death model with interval censored data, that performs variable selection. Variable selection will allow us to increase predictive abilities and possibly identify new risk factors of dementia. We considered an hybrid algorithm maximising the regularised likelihood with elastic-net penalty on the transitions to dementia and those to death with and without dementia. Our algorithm simultaneously estimates all three transitions' regression parameters while having different penalty parameters on each transition. The optimal penalty parameter is chosen via the bayesian information criteria. The performances of our algorithm were evaluated in simulations and compared to standard competing risk approach that neglects interval censoring. The illness-death model accounting for interval-censored data provided great predictions for the transition to dementia and good performances for variable selection. In comparison, the standard competing risk model showed worse predictive abilities and tended to select irrelevant variables for transition to dementia when associated with death. Neglecting interval censoring of dementia can deteriorate prediction of future cases of dementia and lead to a misleading selection of risk factors. Variable selection in illness-death model handling interval censoring data allows to accurately predict future cases of dementia while identifying risk factors of dementia among a large number of predictors. The regularized illness-death model for interval censored data is implemented in an R package HIDeM.
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Biostatistics seminar series from the Department of Public Health from the University of Bordeaux and the Bordeaux Population Health UMR 1219 research center
Boris Hejblum
