The use of multiple informants (parents, teachers, children, clinicians, peers) is generally regarded as the best approach to obtain information about children's mental health, social functioning, and service use and outcomes. Some of the difficulties encountered in combining data from multiple informants include systematic differences between informants with regard to some outcomes, a relatively low level of informant agreement, and missing information for some informants. A number of strategies are currently employed by researchers in multiple-informant studies, but no single approach has been embraced as a standard, especially for the case where ratings are categorical. This proposal seeks funding to pursue a line of research recently initiated by ourselves and aims to develop methodology for the analysis of repeated (longitudinal) categorical outcomes obtained from multiple informants. For this work, we propose a likelihood-based method (multivariate logistic regression and its extensions for categorical data), which is similar in concept to multivariate analysis of repeated measures used for continuous outcomes. Under a unified framework, this approach will allow us to: 1) test if the effects of risk factors of psychopathology or of predictors of service use vary by informant and obtain a combined estimate across informants if they do not )""""""""marginal"""""""" analyses); 2) assess informant agreement (""""""""agreement"""""""" analyses); 3) assess the effect of time on psychopathology or service use (longitudinal analyses); and 4) fully utilize all the data, even if some children are missing data from an informant or at a time point. We plan to develop the software to apply this methodology to existing data (e.g., a Connecticut survey of children's mental health using parent and teacher ratings and a Boston longitudinal study of mental illness in children and adolescents) that have not been fully analyzed so far. The proposed methods will also be directly applicable to the future analysis of data obtained from several recent NIMH initiatives, including the multi-site UNOCCAP project and other community-based collaborative studies.
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