Managing nonignorable missing data with clustered multinomial responses. (December 2015) - Wang, Mu-Cyun
Clustered multinomial responses are common in public health studies. In this situation, the baseline logit random effects model is usually suggested as a general modelling approach. When nonignorable missing outcomes exist, naïve methods such as complete case analysis or likelihood methods ignoring missing information may distort the conclusions that are drawn. While methods to deal with binary and ordinal outcomes have been proposed, no easily implementable method is specifically available for missing clustered nominal responses. Joint modelling is usually one of the available choices but has high complexity in terms of likelihood. The numerical integration of both missing data and random effects is challenging. In this study, we have derived a closed form of likelihood. A simplified likelihood is also proposed, which is an extension of a previous study. One advantage is that both methods are easily implemented with commonly used software. We illustrate our proposed methods using the Global Youth Tobacco Survey and compare the results obtained by naïve methods that ignore missing data with the results obtained using the proposed methods. Our approaches restore the parameter estimates and predicted probability of each category to an acceptable extent. Analysis guidelines for the use of our methods are provided.