Longitudinal studies with outcome-dependent follow-up: Models and bayesian regression

Duchwan Ryu, Debajyoti Sinha, Bani Mallick, Stuart R. Lipsitz, Steven E. Lipshultz

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.

Original languageEnglish (US)
Pages (from-to)952-961
Number of pages10
JournalJournal of the American Statistical Association
Issue number479
StatePublished - Sep 2007


  • Bayesian cubic smoothing spline
  • Latent variable
  • Partially linear model

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability


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