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Partial Likelihood Estimation of IRT Models with Censored Lifetime Data: An Application to Mental Disorders in the ESEMeD Surveys

Published online by Cambridge University Press:  01 January 2025

Carlos G. Forero ,
Josué Almansa ,
Núria D. Adroher ,
Jeroen K. Vermunt ,
Gemma Vilagut ,
Ron De Graaf ,
Josep-Maria Haro  and
Jordi Alonso Caballero
Carlos G. Forero
Affiliation:
CIBER en Epidemiología y Salud Pública (CIBERESP) and Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques
Josué Almansa
Affiliation:
Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques
Núria D. Adroher
Affiliation:
CIBER en Epidemiología y Salud Pública (CIBERESP) and Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques
Jeroen K. Vermunt
Affiliation:
Tilburg University
Gemma Vilagut
Affiliation:
CIBER en Epidemiología y Salud Pública (CIBERESP) and Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques
Ron De Graaf
Affiliation:
Netherlands Institute of Mental Health and Addiction
Josep-Maria Haro
Affiliation:
Fundació Sant Joan de Déu, CIBERSAM
Jordi Alonso Caballero*
Affiliation:
CIBER en Epidemiología y Salud Pública (CIBERESP) and Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques
*
Requests for reprints should be sent to Jordi Alonso Caballero, Health Services Research Unit, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques, Doctor Aiguader 88, 08003 Barcelona, Spain. E-mail: jalonso@imim.es
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Abstract

Developmental studies of mental disorders based on epidemiological data are often based on cross-sectional retrospective surveys. Under such designs, observations are right-censored, causing underestimation of lifetime prevalences and correlations, and inducing bias in latent trait models on the observations. In this paper we propose a Partial Likelihood (PL) method to estimate unbiased IRT models of lifetime predisposition to develop a certain outcome. A two-step estimation procedure corrects the IRT likelihood of outcome appearance with a function depending on (a) projected outcome frequencies at the end of the risk period, and (b) outcome censoring status at the time of the observation. Simulation results showed that the PL method yielded good recovery of true frequencies and intercepts. Slopes were best estimated when events were sufficiently correlated. When PL is applied to lifetime mental health disorders (assessed in the ESEMeD project surveys), estimated univariate prevalences were, on average, 1.4 times above raw estimates, and 2.06 higher in the case of bivariate prevalences.

Keywords

data censorshipsurvival analysismental disordersinternalising psychiatric disorderspsychiatric epidemiologypsychometric epidemiology
Type
Original Paper
Information
Psychometrika , Volume 79 , Issue 3 , July 2014 , pp. 470 - 488
Copyright
Copyright © 2013 The Psychometric Society

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Footnotes

The first two authors contributed equally in the writing of this paper.

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