Intercept Estimation of Semi‐Parametric Joint Models in the Context of Longitudinal Data Subject to Irregular Observations

Longitudinal data are often subject to irregular visiting times, with outcomes and visit times influenced by a latent variable. Semi‐parametric joint models that account for this dependence have been proposed; among these, the Sun model is the most suitable for count data as it employs a multiplicat...

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Bibliographic Details
Published in:Biometrical journal Vol. 67; no. 6; p. e70088
Main Authors: Ledesma, Luis, Pullenayegum, Eleanor
Format: Journal Article
Language:English
Published: Germany 01.12.2025
Subjects:
Biometry - methods
Humans
Longitudinal Studies
Models, Statistical
Urinary Bladder Neoplasms
estimating equations
semi‐parametric models
irregular observation
longitudinal data
ISSN:0323-3847, 1521-4036, 1521-4036
Online Access:Get full text
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Summary:Longitudinal data are often subject to irregular visiting times, with outcomes and visit times influenced by a latent variable. Semi‐parametric joint models that account for this dependence have been proposed; among these, the Sun model is the most suitable for count data as it employs a multiplicative link function. Semi‐parametric joint models define an intercept function as the mean outcome when all covariates are set to zero; this is differenced out in the course of estimation and is consequently not estimated. The Sun estimator thus provides estimates of relative covariate effects, but is unable to provide estimates of absolute effects or of longitudinal prognosis in the absence of covariates. We extend the Sun model by additionally estimating the intercept term, showing that our extended estimator is consistent and asymptotically Normal. In simulations, our estimator outperforms the original Sun estimator in terms of bias and standard error and is also more computationally efficient. We apply our estimator to a longitudinal study of tumor recurrence among bladder cancer patients. Provided the intercept term can be adequately captured using splines, we recommend that our extended Sun estimator be used in place of the original estimator, since it leads to smaller bias, smaller standard errors, and allows estimation of the mean outcome trajectories.
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ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.70088