Causal nonresponse models for repeated categorical measurements

This paper uses causal models for nonresponse (Fay, 1986, Journal of the American Statistical Association 81, 354-365) to extend the conditional likelihood procedure for repeated categorical outcome variables to allow for nonrandomly missing data. The extension based on causal models is similar to the log-linear approach presented by Conaway (1992, Journal of the American Statistical Association, 87, 817-824), but has the advantage that the parameters are directly interpretable in terms of the distribution of the outcome variables. As with log-linear model approach, all of the computations can be done with standard statistical software. The methods are first described in terms of a simple example with three binary responses with no covariates and then are applied to a more complicated example. A simulation study evaluates the properties of the estimates based on the proposed method.