Performance of methods for estimating the effect of covariates on group membership probabilities in group-based trajectory models

Christopher E. Davies, Lynne C. Giles, Gary F.V. Glonek

Research output: Contribution to journalArticle

Abstract

One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this ‘1-step’ approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the ‘3-step’ approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt’s improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.

LanguageEnglish
Number of pages15
JournalStatistical Methods in Medical Research
Early online date18 Jan 2017
DOIs
Publication statusE-pub ahead of print - 18 Jan 2017

Keywords

  • covariates
  • group-based trajectory modelling
  • longitudinal data
  • mixture models
  • simulation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

Cite this

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abstract = "One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this ‘1-step’ approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the ‘3-step’ approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt’s improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.",
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