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Semiparametric regression models and sensitivity analysis of longitudinal data with non‐random dropouts

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  • David Todem
  • KyungMann Kim
  • Jason Fine
  • Limin Peng

Abstract

We propose a family of regression models to adjust for non‐random dropouts in the analysis of longitudinal outcomes with fully observed covariates. The approach conceptually focuses on generalized linear models with random effects. A novel formulation of a shared random effects model is presented and shown to provide a dropout selection parameter with a meaningful interpretation. The proposed semiparametric and parametric models are made part of a sensitivity analysis to delineate the range of inferences consistent with observed data. Concerns about model identifiability are addressed by fixing some model parameters to construct functional estimators that are used as the basis of a global sensitivity test for parameter contrasts. Our simulation studies demonstrate a large reduction of bias for the semiparametric model relative to the parametric model at times where the dropout rate is high or the dropout model is mis‐specified. The methodology's practical utility is illustrated in a data analysis.

Suggested Citation

  • David Todem & KyungMann Kim & Jason Fine & Limin Peng, 2010. "Semiparametric regression models and sensitivity analysis of longitudinal data with non‐random dropouts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(2), pages 133-156, May.
    • Handle: RePEc:bla:stanee:v:64:y:2010:i:2:p:133-156
    DOI: 10.1111/j.1467-9574.2009.00435.x
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    References listed on IDEAS

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    3. Caroline Beunckens & Geert Molenberghs & Geert Verbeke & Craig Mallinckrodt, 2008. "A Latent-Class Mixture Model for Incomplete Longitudinal Gaussian Data," Biometrics, The International Biometric Society, vol. 64(1), pages 96-105, March.
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    5. Rotnitzky Andrea & Daniel Scharfstein & Ting‐Li Su & James Robins, 2001. "Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of Censoring," Biometrics, The International Biometric Society, vol. 57(1), pages 103-113, March.
    6. John Copas & Shinto Eguchi, 2001. "Local sensitivity approximations for selectivity bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 871-895.
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    Cited by:

    1. Oludare Samuel Ariyo & Matthew Adekunle Adeleke, 2022. "Simultaneous Bayesian modelling of skew-normal longitudinal measurements with non-ignorable dropout," Computational Statistics, Springer, vol. 37(1), pages 303-325, March.

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