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

Author

Listed:
  • Carlos Forero
  • Josué Almansa
  • Núria Adroher
  • Jeroen Vermunt
  • Gemma Vilagut
  • Ron Graaf
  • Josep-Maria Haro
  • Jordi Alonso Caballero

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. Copyright The Psychometric Society 2014

Suggested Citation

  • Carlos Forero & Josué Almansa & Núria Adroher & Jeroen Vermunt & Gemma Vilagut & Ron Graaf & Josep-Maria Haro & Jordi Alonso Caballero, 2014. "Partial Likelihood Estimation of IRT Models with Censored Lifetime Data: An Application to Mental Disorders in the ESEMeD Surveys," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 470-488, July.
    • Handle: RePEc:spr:psycho:v:79:y:2014:i:3:p:470-488
    DOI: 10.1007/s11336-013-9400-y
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    References listed on IDEAS

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    1. Jeroen K. Vermunt, 2004. "An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 220-233, May.
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