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Joint latent class model for longitudinal data and interval‐censored semi‐competing events: Application to dementia

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  • Anaïs Rouanet
  • Pierre Joly
  • Jean‐François Dartigues
  • Cécile Proust‐Lima
  • Hélène Jacqmin‐Gadda

Abstract

Joint models are used in ageing studies to investigate the association between longitudinal markers and a time‐to‐event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time‐to‐dementia is interval‐censored since dementia is assessed intermittently. So subjects can develop dementia and die between two visits without being diagnosed. To study predementia cognitive decline, we propose a joint latent class model combining a (possibly multivariate) mixed model and an illness–death model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi‐competing risks. Parameters are estimated by maximum‐likelihood handling interval censoring. The correlation between the marker and the times‐to‐events is captured by latent classes, homogeneous sub‐groups with specific risks of death, dementia, and profiles of cognitive decline. We propose Markovian and semi‐Markovian versions. Both approaches are compared to a joint latent‐class model for competing risks through a simulation study, and applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among subjects with dementia, mortality depends more on age than on duration of dementia. This model distinguishes the so‐called terminal predeath decline (among healthy subjects) from the predementia decline.

Suggested Citation

  • Anaïs Rouanet & Pierre Joly & Jean‐François Dartigues & Cécile Proust‐Lima & Hélène Jacqmin‐Gadda, 2016. "Joint latent class model for longitudinal data and interval‐censored semi‐competing events: Application to dementia," Biometrics, The International Biometric Society, vol. 72(4), pages 1123-1135, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1123-1135
    DOI: 10.1111/biom.12530
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    References listed on IDEAS

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    1. Hélène Jacqmin-Gadda & Cécile Proust-Lima & Jeremy M.G. Taylor & Daniel Commenges, 2010. "Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model," Biometrics, The International Biometric Society, vol. 66(1), pages 11-19, March.
    2. Hawkins, Dollena S. & Allen, David M. & Stromberg, Arnold J., 2001. "Determining the number of components in mixtures of linear models," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 15-48, November.
    3. Robert M. Elashoff & Gang Li & Ning Li, 2008. "A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types," Biometrics, The International Biometric Society, vol. 64(3), pages 762-771, September.
    4. Ralitza Gueorguieva & Robert Rosenheck & Haiqun Lin, 2012. "Joint modelling of longitudinal outcome and interval‐censored competing risk dropout in a schizophrenia clinical trial," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 417-433, April.
    5. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
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    Cited by:

    1. Chen, Chyong-Mei & Shen, Pao-sheng & Tseng, Yi-Kuan, 2018. "Semiparametric transformation joint models for longitudinal covariates and interval-censored failure time," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 116-127.
    2. Daniel Commenges, 2019. "Dealing with death when studying disease or physiological marker: the stochastic system approach to causality," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 381-405, July.
    3. Ardo Hout & Graciela Muniz-Terrera, 2019. "Hidden three-state survival model for bivariate longitudinal count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 529-545, July.

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