Premium
Pattern–mixture models with incomplete informative cluster size: application to a repeated pregnancy study
Author(s) -
Chaurasia Ashok,
Liu Danping,
Albert Paul S.
Publication year - 2018
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12226
Subject(s) - mixture model , parity (physics) , cluster (spacecraft) , pregnancy , latent variable , statistics , statistical model , gestational age , sample size determination , mathematics , computer science , biology , physics , particle physics , genetics , programming language
Summary 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.