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Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies

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  • Janie Coulombe
  • Erica E. M. Moodie
  • Robert W. Platt

Abstract

We address estimation of the marginal effect of a time‐varying binary treatment on a continuous longitudinal outcome in the context of observational studies using electronic health records, when the relationship of interest is confounded, mediated, and further distorted by an informative visit process. We allow the longitudinal outcome to be recorded only sporadically and assume that its monitoring timing is informed by patients' characteristics. We propose two novel estimators based on linear models for the mean outcome that incorporate an adjustment for confounding and informative monitoring process through generalized inverse probability of treatment weights and a proportional intensity model, respectively. We allow for a flexible modeling of the intercept function as a function of time. Our estimators have closed‐form solutions, and their asymptotic distributions can be derived. Extensive simulation studies show that both estimators outperform standard methods such as the ordinary least squares estimator or estimators that only account for informative monitoring or confounders. We illustrate our methods using data from the Add Health study, assessing the effect of depressive mood on weight in adolescents.

Suggested Citation

  • Janie Coulombe & Erica E. M. Moodie & Robert W. Platt, 2021. "Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies," Biometrics, The International Biometric Society, vol. 77(1), pages 162-174, March.
  • Handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:162-174
    DOI: 10.1111/biom.13285
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    References listed on IDEAS

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    1. Xinyuan Song & Xiaoyun Mu & Liuquan Sun, 2012. "Regression Analysis of Longitudinal Data with Time-Dependent Covariates and Informative Observation Times," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(2), pages 248-258, June.
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    3. Liuquan Sun & Xinyuan Song & Jie Zhou & Lei Liu, 2012. "Joint Analysis of Longitudinal Data With Informative Observation Times and a Dependent Terminal Event," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 688-700, June.
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    5. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Richard Gelber & Steven Lipshultz, 2002. "Parameter Estimation in Longitudinal Studies with Outcome-Dependent Follow-Up," Biometrics, The International Biometric Society, vol. 58(3), pages 621-630, September.
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