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Pattern–mixture models with incomplete informative cluster size: application to a repeated pregnancy study

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  • Ashok Chaurasia
  • Danping Liu
  • Paul S. Albert

Abstract

The incomplete informative cluster size problem is motivated by the National Institute of Child Health and Human Development consecutive pregnancies study, aiming to study the relationship between pregnancy outcomes and parity. These pregnancy outcomes are potentially associated with the number of births over a woman's lifetime, resulting in an incomplete informative cluster size (censored at the end of the study window). We develop a pattern–mixture model for informative cluster size by treating the lifetime number of births as a latent variable. We compare this approach with a simple alternative method that approximates the pattern–mixture model. We show that the latent variable approach has good statistical properties for estimating both the mean trajectory of birth weight and the proportion of gestational hypertension with increasing parity.

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  • Ashok Chaurasia & Danping Liu & Paul S. Albert, 2018. "Pattern–mixture models with incomplete informative cluster size: application to a repeated pregnancy study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 255-273, January.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:1:p:255-273
    DOI: 10.1111/rssc.12226
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    Cited by:

    1. A. A. Mitani & E. K. Kaye & K. P. Nelson, 2021. "Marginal analysis of multiple outcomes with informative cluster size," Biometrics, The International Biometric Society, vol. 77(1), pages 271-282, March.

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