title: Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes.
abstract: BACKGROUND
  Structural equation models (SEMs) provide a general framework for analyzing mediated longitudinal data. However when interest is in the total effect (i.e. direct plus indirect) of a predictor on the binary outcome, alternative statistical techniques such as non-linear mixed models (NLMM) may be preferable, particularly if specific causal pathways are not hypothesized or specialized SEM software is not readily available. The purpose of this paper is to evaluate the performance of the NLMM in a setting where the SEM is presumed optimal.
  
  
  METHODS
  We performed a simulation study to assess the performance of NLMMs relative to SEMs with respect to bias, coverage probability, and power in the analysis of mediated binary longitudinal outcomes. Both logistic and probit models were evaluated. Models were also applied to data from a longitudinal study assessing the impact of alcohol consumption on HIV disease progression.
  
  
  RESULTS
  For the logistic model, the NLMM adequately estimated the total effect of a repeated predictor on the repeated binary outcome and were similar to the SEM across a variety of scenarios evaluating sample size, effect size, and distributions of direct vs. indirect effects. For the probit model, the NLMM adequately estimated the total effect of the repeated predictor, however, the probit SEM overestimated effects.
  
  
  CONCLUSIONS
  Both logistic and probit NLMMs performed well relative to corresponding SEMs with respect to bias, coverage probability and power. In addition, in the probit setting, the NLMM may produce better estimates of the total effect than the probit SEM, which appeared to overestimate effects.
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. emily.blood@childrens.harvard.edu
authors.forename: Emily A
authors.initials: EA
authors.lastname: Blood
authors.forename: Debbie M
authors.initials: DM
authors.lastname: Cheng
mesh_terms: D000428:Alcohol Drinking / Q000453:epidemiology; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D015658:HIV Infections / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D018401:Sample Size; D015983:Selection Bias