Poster
in
Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling

A shared parameter model accounting for drop-out not at random in a predictive model for systolic bloodpressure using the HUNT study

Aurora Christine Hofman

Abstract:

This work proposes and evaluates a shared parameter model (SPM) to account for data being missing not at random (MNAR) for a predictive model based on a longitudinal population study. The aim is to model systolic blood pressure ten years ahead based on current observations and is inspired by and evaluated for data from the Nord-Tr√łndelag Health Study (HUNT). The proposed SPM consists of a linear model for the systolic blood pressure and a logistic model for the drop-out process connected through a shared random effect. To evaluate the SPM we compare the parameter estimates and predictions of the SPM with a naive linear Bayesian model using the same explanatory variables while ignoring the drop-out process. This corresponds to assuming data to be missing at random (MAR). In addition, a simulation study is performed in which the naive model and the SPM are tested on data with known parameters when missingness is assumed to be MNAR. The SPM indicates that participants with higher systolic blood pressure than expected from the explanatory variables at the time of the follow-up study have a higher probability of dropping out, suggesting that the data are MNAR. Further, the SPM and the naive model result in different parameter estimates for the explanatory variables. The simulation study validates that the SPM is identifiable for the estimates obtained by the predictive model based on the HUNT study

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