Advances in mental health services research are highly dependent on the quality of research procedures, measures, and data analytic strategies available to investigators. As the knowledge base broadens and deepens, questions of increasing subtlety and complexity must be addressed. To do so requires the development or adaptation of increasingly more sophisticated and precise methods, measures, and analytic strategies. In particular because modern methods for handling missing data draw on advanced statistical computing techniques high level statistical training is imperative in order to make progress in this area. Through training provided by the UCLA Department of Biostatistics and the UCLA Neuropsychiatric Health Services Research Center, the recipient of this award will be prepared to enter and contribute to the field of mental health services research as an Investigator and a Biostatistician. ? ?
| Siddique, Juned; Belin, Thomas R (2008) Using an Approximate Bayesian Bootstrap to Multiply Impute Nonignorable Missing Data. Comput Stat Data Anal 53:405-415 |
