Literature DB >> 29531406

Pattern-mixture models with incomplete informative cluster size: Application to a repeated pregnancy study.

Ashok Chaurasia1, Danping Liu2, Paul S Albert3.   

Abstract

The incomplete informative cluster size problem is motivated by the NICHD 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 possesses good statistical properties for estimating both the mean trajectory of birthweight and the proportion of gestational hypertension with increasing parity.

Entities:  

Keywords:  Generalized linear mixed model; Incomplete informative cluster size; Latent variable; Pattern mixture model; Repeated Pregnancy; Sensitivity analysis

Year:  2017        PMID: 29531406      PMCID: PMC5844500          DOI: 10.1111/rssc.12226

Source DB:  PubMed          Journal:  J R Stat Soc Ser C Appl Stat        ISSN: 0035-9254            Impact factor:   1.864


  13 in total

1.  Marginal analyses of clustered data when cluster size is informative.

Authors:  John M Williamson; Somnath Datta; Glen A Satten
Journal:  Biometrics       Date:  2003-03       Impact factor: 2.571

2.  Informative cluster sizes for subcluster-level covariates and weighted generalized estimating equations.

Authors:  Ying Huang; Brian Leroux
Journal:  Biometrics       Date:  2011-01-31       Impact factor: 2.571

3.  A joint modeling approach to data with informative cluster size: robustness to the cluster size model.

Authors:  Zhen Chen; Bo Zhang; Paul S Albert
Journal:  Stat Med       Date:  2011-04-15       Impact factor: 2.373

4.  A general class of pattern mixture models for nonignorable dropout with many possible dropout times.

Authors:  Jason Roy; Michael J Daniels
Journal:  Biometrics       Date:  2007-09-26       Impact factor: 2.571

5.  Pattern-mixture models for multivariate incomplete data with covariates.

Authors:  R J Little; Y Wang
Journal:  Biometrics       Date:  1996-03       Impact factor: 2.571

6.  Estimation and comparison of changes in the presence of informative right censoring: conditional linear model.

Authors:  M C Wu; K R Bailey
Journal:  Biometrics       Date:  1989-09       Impact factor: 2.571

7.  A dose-response model incorporating nonlinear kinetics.

Authors:  J Van Ryzin; K Rai
Journal:  Biometrics       Date:  1987-03       Impact factor: 2.571

8.  A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.

Authors:  David B Dunson; Zhen Chen; Jean Harry
Journal:  Biometrics       Date:  2003-09       Impact factor: 2.571

9.  The NICHD Consecutive Pregnancies Study: recurrent preterm delivery by subtype.

Authors:  S Katherine Laughon; Paul S Albert; Kira Leishear; Pauline Mendola
Journal:  Am J Obstet Gynecol       Date:  2013-09-11       Impact factor: 8.661

Review 10.  Methods for observed-cluster inference when cluster size is informative: a review and clarifications.

Authors:  Shaun R Seaman; Menelaos Pavlou; Andrew J Copas
Journal:  Biometrics       Date:  2014-01-30       Impact factor: 2.571

View more
  3 in total

1.  Marginal analysis of ordinal clustered longitudinal data with informative cluster size.

Authors:  Aya A Mitani; Elizabeth K Kaye; Kerrie P Nelson
Journal:  Biometrics       Date:  2019-04-04       Impact factor: 2.571

2.  Estimating recurrence and incidence of preterm birth subject to measurement error in gestational age: A hidden Markov modeling approach.

Authors:  Paul S Albert
Journal:  Stat Med       Date:  2018-02-21       Impact factor: 2.373

3.  Marginal analysis of multiple outcomes with informative cluster size.

Authors:  A A Mitani; E K Kaye; K P Nelson
Journal:  Biometrics       Date:  2020-03-05       Impact factor: 1.701

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.