AN ANALYSIS OF THE GENERAL WELL-BEING OF BLACKS AND WHITES

The importance of race as a factor in mental health status has been a topic of controversy. This study reviews the history of research in this area and examines racial variances in the relationship between selected socio-demographic variables and general well-being. The study also examines the appropriateness of an additive versus an interactive statistical model for this investigation.^ The sample consists of 6,913 persons who completed the General Well-Being Schedules as administered in the detailed component of the first National Health and Nutrition Examination Survey (NHANES I) conducted by the National Center for Health Statistics between April, 1971 and October, 1975. The sampling design is a multistage, probability sample of clusters of persons in area based segments. Of the 6,913 persons, 873 are Black.^ Unlike other recent community based mental health studies, this study revealed significant differences between the general well-being of Blacks and Whites. Blacks continued to exhibit significantly lower levels of well-being even after adjustments were made for income, education, marital status, sex, age and place of residence. Statistical interaction was found between race and sex with Black females reporting lower levels of well-being than either Black or White males or their White female counterparts.^ The study includes a detailed review of the NHANES I sample design. It is shown that selected aspects of the design make it difficult to render appropriate national comparisons of Black-White differences. As a result conclusions pertaining to these differences based on NHANES I may be of questionable validity. ^

Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^