Affiliation:
1. Department of Economics University of Perugia Perugia Italy
2. Department of Statistics and Quantitative Methods University of Milano‐Bicocca Milan Italy
Abstract
AbstractWe propose a hidden Markov model for multivariate continuous longitudinal responses with covariates that accounts for three different types of missing pattern: (I) partially missing outcomes at a given time occasion, (II) completely missing outcomes at a given time occasion (intermittent pattern), and (III) dropout before the end of the period of observation (monotone pattern). The missing‐at‐random (MAR) assumption is formulated to deal with the first two types of missingness, while to account for the informative dropout, we rely on an extra absorbing state. Estimation of the model parameters is based on the maximum likelihood method that is implemented by an expectation‐maximization (EM) algorithm relying on suitable recursions. The proposal is illustrated by a Monte Carlo simulation study and an application based on historical data on primary biliary cholangitis.
Funder
Università degli Studi di Perugia
Università degli Studi di Milano-Bicocca
Subject
Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability
Cited by
1 articles.
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