No abstract

Missing data methods, maximum likelihood estimation (MLE) and multiple imputation (MI), for longitudinal questionnaire data were investigated via simulation. Predictive mean matching (PMM) was applied at both item and scale levels, logistic regression at item level and multivariate normal imputation at scale level. We investigated a hybrid approach which is combination of MLE and MI, i.e. scales from the imputed data are eliminated if all underlying items were originally missing. Bias and mean square error (MSE) for parameter estimates were examined. ML seemed to provide occasionally the best results in terms of bias, but hardly ever on MSE. All imputation methods at the scale level and logistic regression at item level hardly ever showed the best performance. The hybrid approach is similar or better than its original MI. The PMM-hybrid approach at item level demonstrated the best MSE for most settings and in some cases also the smallest bias.

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Subject-specific and marginal models have been developed for the analysis of longitudinal ordinal data. Subject-specific models often lack a population-average interpretation of the model parameters due to the conditional formulation of random intercepts and slopes. Marginal models frequently lack an underlying distribution for ordinal data, in particular when generalized estimating equations are applied. To overcome these issues, latent variable models underneath the ordinal outcomes with a multivariate logistic distribution can be applied. In this article, we extend the work of O'Brien and Dunson (2004), who studied the multivariate t-distribution with marginal logistic distributions. We use maximum likelihood, instead of a Bayesian approach, and incorporated covariates in the correlation structure, in addition to the mean model. We compared our method with GEE and demonstrated that it performs better than GEE with respect to the fixed effect parameter estimation when the latent variables have an approximately elliptical distribution, and at least as good as GEE for other types of latent variable distributions.