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Biased and unbiased estimation in longitudinal studies with informative visit processes

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  • Charles E. McCulloch
  • John M. Neuhaus
  • Rebecca L. Olin

Abstract

The availability of data in longitudinal studies is often driven by features of the characteristics being studied. For example, clinical databases are increasingly being used for research to address longitudinal questions. Because visit times in such data are often driven by patient characteristics that may be related to the outcome being studied, the danger is that this will result in biased estimation compared to designed, prospective studies. We study longitudinal data that follow a generalized linear mixed model and use a log link to relate an informative visit process to random effects in the mixed model. This device allows us to elucidate which parameters are biased under the informative visit process and to what degree. We show that the informative visit process can badly bias estimators of parameters of covariates associated with the random effects, while allowing consistent estimation of other parameters.

Suggested Citation

  • Charles E. McCulloch & John M. Neuhaus & Rebecca L. Olin, 2016. "Biased and unbiased estimation in longitudinal studies with informative visit processes," Biometrics, The International Biometric Society, vol. 72(4), pages 1315-1324, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1315-1324
    DOI: 10.1111/biom.12501
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    References listed on IDEAS

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    Cited by:

    1. Alessandro Gasparini & Keith R. Abrams & Jessica K. Barrett & Rupert W. Major & Michael J. Sweeting & Nigel J. Brunskill & Michael J. Crowther, 2020. "Mixed‐effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 5-23, February.

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