Journal article
A Bayesian approach to functional mixed-effects modeling for longitudinal data with binomial outcomes
Statistics in medicine, Vol.33(18), pp.3130-3146
08/15/2014
DOI: 10.1002/sim.6166
PMCID: PMC4107023
PMID: 24723495
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.
Details
- Title: Subtitle
- A Bayesian approach to functional mixed-effects modeling for longitudinal data with binomial outcomes
- Creators
- Stephanie Kliethermes - Department of Medicine, Stritch School of Medicine, Loyola University Chicago, 2160 S. First Ave, Maywood, IL 60153, U.S.AJacob Oleson
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.33(18), pp.3130-3146
- DOI
- 10.1002/sim.6166
- PMID
- 24723495
- PMCID
- PMC4107023
- NLM abbreviation
- Stat Med
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Publisher
- Wiley; England
- Grant note
- M01 RR000059 / NCRR NIH HHS P50 DC000242 / NIDCD NIH HHS 2 P50 DC00242 / NIDCD NIH HHS RR00059 / NCRR NIH HHS
- Language
- English
- Date published
- 08/15/2014
- Academic Unit
- Biostatistics
- Record Identifier
- 9983997369802771
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