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Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random: A high dimensional pattern‐mixture model
Author(s) -
Kaciroti Niko A.,
Little Roderick J.A.
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9083
Subject(s) - missing data , statistics , sensitivity (control systems) , random effects model , bayesian probability , dropout (neural networks) , outcome (game theory) , mathematics , econometrics , computer science , medicine , machine learning , meta analysis , mathematical economics , electronic engineering , engineering
Randomized clinical trials with outcome measured longitudinally are frequently analyzed using either random effect models or generalized estimating equations. Both approaches assume that the dropout mechanism is missing at random (MAR) or missing completely at random (MCAR). We propose a Bayesian pattern‐mixture model to incorporate missingness mechanisms that might be missing not at random (MNAR), where the distribution of the outcome measure at the follow‐up timet k, conditional on the prior history, differs across the patterns of missing data. We then perform sensitivity analysis on estimates of the parameters of interest. The sensitivity parameters relate the distribution of the outcome of interest between subjects from a missing‐data pattern at timet kwith that of the observed subjects at timet k. The large number of the sensitivity parameters is reduced by treating them as random with a prior distribution having some pre‐specified mean and variance, which are varied to explore the sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the proposed model, allowing a sensitivity analysis of deviations from MAR. The proposed approach is applied to data from the Trial of Preventing Hypertension.