title: Causal nonresponse models for repeated categorical measurements.
abstract: 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.
authors.affiliation: Department of Community and Family Medicine, Duke University Medical Center, Durham, North Carolina 27710, USA.
authors.forename: M R
authors.initials: MR
authors.lastname: Conaway
mesh_terms: D000293:Adolescent; D001699:Biometry / Q000379:methods*; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D003327:Coronary Disease / Q000453:epidemiology; D005260:Female; D006801:Humans; D008433:Mathematics; D015233:Models, Statistical; D011336:Probability; D012107:Research Design; D012307:Risk Factors; D012984:Software