A Bayesian approach to functional mixed-effects modeling for longitudinal data with binomial outcomes Journal Article

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Authors: Kliethermes, S; Oleson, J.
Article Title: A Bayesian approach to functional mixed-effects modeling for longitudinal data with binomial outcomes
Abstract: Longitudinal growth patterns are routinely seen in medical studies where individual growth and population growth are followed up over a period of time. Many current methods for modeling growth presuppose a parametric relationship between the outcome and time (e.g., linear and quadratic); however, these relationships may not accurately capture growth over time. Functional mixed-effects (FME) models provide flexibility in handling longitudinal data with nonparametric temporal trends. Although FME methods are well developed for continuous, normally distributed outcome measures, nonparametric methods for handling categorical outcomes are limited. We consider the situation with binomially distributed longitudinal outcomes. Although percent correct data can be modeled assuming normality, estimates outside the parameter space are possible, and thus, estimated curves can be unrealistic. We propose a binomial FME model using Bayesian methodology to account for growth curves with binomial (percentage) outcomes. The usefulness of our methods is demonstrated using a longitudinal study of speech perception outcomes from cochlear implant users where we successfully model both the population and individual growth trajectories. Simulation studies also advocate the usefulness of the binomial model particularly when outcomes occur near the boundary of the probability parameter space and in situations with a small number of trials.
Journal Title: Statistics in medicine
Volume: 33
Issue: 18
ISSN: 1097-0258; 0277-6715
Publisher: Wiley Periodicals, Inc  
Journal Place: England
Date Published: 2014
Start Page: 3130
End Page: 3146
Language: eng
DOI/URL:

10.1002/sim.6166
Notes: LR: 20150815; CI: Copyright (c) 2014; GR: 2 P50 DC00242/DC/NIDCD NIH HHS/United States; GR: P50 DC000242/DC/NIDCD NIH HHS/United States; GR: RR00059/RR/NCRR NIH HHS/United States; JID: 8215016; NIHMS581448; OID: NLM: NIHMS581448; OID: NLM: PMC4107023; OTO: NOTNLM; 2013/05/10 [received]; 2014/01/31 [revised]; 2014/03/16 [accepted]; 2014/04/10 [aheadofprint]; ppublish