We 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.

Pandolfi, S., Bartolucci, F., Pennoni, F. (2023). A hidden Markov model for continuous longitudinal data with missing responses and dropout. BIOMETRICAL JOURNAL, 65(5 (June 2023)), 1-28 [10.1002/bimj.202200016].

A hidden Markov model for continuous longitudinal data with missing responses and dropout

Pennoni, F
2023

Abstract

We 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.
Articolo in rivista - Articolo scientifico
expectation-maximization algorithm; forward–backward recursion; latent Markov model; missing values; prediction;
English
10-apr-2023
2023
65
5 (June 2023)
1
28
2200016
open
Pandolfi, S., Bartolucci, F., Pennoni, F. (2023). A hidden Markov model for continuous longitudinal data with missing responses and dropout. BIOMETRICAL JOURNAL, 65(5 (June 2023)), 1-28 [10.1002/bimj.202200016].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/409097
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