Building marginal models for multiple ordinal measurements

Biomedical and psychosocial researchers increasingly utilize multiple indicators to assess an outcome of interest. We apply the ordinal estimating equations model for analysing this kind of measurement. We detail the special complexities of using this model to analyse clustered non-identical items and propose a workable model building strategy. Three graphical methods-cumulative log-odds, partial residual and Pearson residual plotting-are developed to diagnose the adequacy of models. The benefit of incorporating interitem associations and the trade-off between simple versus complex models are evaluated. Throughout the paper, an analysis to determine how measured impairments affect visual disability is used for illustration.