Longitudinal Studies With Outcome-Dependent Follow-up: Models and Bayesian Regression. Academic Article uri icon

abstract

  • 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.

published proceedings

  • J Am Stat Assoc

altmetric score

  • 3

author list (cited authors)

  • Ryu, D., Sinha, D., Mallick, B., Lipsitz, S. L., & Lipshultz, S.

citation count

  • 31

complete list of authors

  • Ryu, Duchwan||Sinha, Debajyoti||Mallick, Bani||Lipsitz, SL||Lipshultz, S

publication date

  • September 2007