Case Studies in Business Industry and Government Statistics最新文献

Texts and software that we are currently using for teaching multivariate analysis to non-statisticians lack in the delivery of confirmatory factor analysis (CFA). The purpose of this paper is to provide educators with a complement to these resources that includes CFA and its computation. We focus on how to use CFA to estimate a "composite reliability" of a psychometric instrument. This paper provides guidance for introducing, via a case-study, the non-statistician to CFA. As a complement to our instruction about the more traditional SPSS, we successfully piloted the software R for estimating CFA on nine non-statisticians. This approach can be used with healthcare graduate students taking a multivariate course, as well as modified for community stakeholders of our Center for American Indian Community Health (e.g. community advisory boards, summer interns, & research team members). The placement of CFA at the end of the class is strategic and gives us an opportunity to do some innovative teaching: (1) build ideas for understanding the case study using previous course work (such as ANOVA); (2) incorporate multi-dimensional scaling (that students already learned) into the selection of a factor structure (new concept); (3) use interactive data from the students (active learning); (4) review matrix algebra and its importance to psychometric evaluation; (5) show students how to do the calculation on their own; and (6) give students access to an actual recent research project.

Objective: To demonstrate the utility of a medical terminology-based method for identifying cases of possible mitochondrial dysfunction (MD) in a large cohort of youths with perinatal HIV infection and to describe the scoring algorithms.

Methods: Medical Dictionary for Regulatory Activities (MedDRA)^® version 6 terminology was used to query clinical criteria for mitochondrial dysfunction by two published classifications, the Enquête Périnatale Française (EPF) and the Mitochondrial Disease Classification (MDC). Data from 2,931 participants with perinatal HIV infection on PACTG 219/219C were analyzed. Data were qualified for severity and persistence, after which clinical reviews of MedDRA-coded and other study data were performed.

Results: Of 14,000 data records captured by the EPF MedDRA query, there were 3,331 singular events. Of 18,000 captured by the MDC query, there were 3,841 events. Ten clinicians blindly reviewed non MedDRA-coded supporting data for 15 separate clinical conditions. We used the Statistical Analysis System (SAS) language to code scoring algorithms. 768 participants (26%) met the EPF case definition of possible MD; 694 (24%) met the MDC case definition, and 480 (16%) met both definitions.

Limitations: Subjective application of codes could have affected our results. MedDRA terminology does not include indicators of severity or persistence. Version 6.0 of MedDRA did not include Standard MedDRA Queries, which would have reduced the time needed to map MedDRA terms to EPF and MDC criteria.

Conclusion: Together with a computer-coded scoring algorithm, MedDRA terminology enabled identification of potential MD based on clinical data from almost 3000 children with substantially less effort than a case by case review. The article is accessible to readers with a background in statistical hypothesis testing. An exposure to public health issues is useful but not strictly necessary.