Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

Effective interventions for lowering HbA1C in African Americans with type 2 diabetes: A review

This synthesis of the literature provides descriptive analysis and outlines current self-management interventions for African Americans with type 2 diabetes. Specifically, this study describes and explores the design of those studies whose interventions have been shown to lower HbA1C levels in this population by at least 0.5% points, an improvement that provides approximately 10% reduction in long term complications from this disease.^ Results. In total, 37 articles were reviewed and 17 articles met inclusion criteria for analysis. Analysis of each study's methodology and results was performed and selected studies with interventions that resulted in improvements in HbA1C outcomes equal to 0.5% or greater for both group 1 and 2 were summarized by intervention type in table format. Descriptive analysis, outlining the number and characteristics of proximal and distal mediating components addressed in Group 1 studies, was performed in order to determine whether mediating components may have had some relation to effectiveness of intervention on outcome HbA1C. Descriptive analysis revealed that no particular design is substantially more effective than another among Behavioral studies although, there may be an advantage in using culturally sensitive, group interventions that address greater numbers of distal mediating components. Among Process studies, structured approaches (i.e. algorithm care and scheduled follow up), as well as utilization of specialty and group care are represented as effective for African American populations. ^ Conclusions. It may be summarized that by targeting behavior and addressing provider delivery (i.e. algorithm use, group care, home care, and provider follow up) in this population, a greater yield in outcome improvements may be accomplished. However, many gaps exist in a review process that stratifies results and focuses on identifying group specific intervention successes and failures. Further research in different populations will aid researchers and practitioners in discovering the best evidence, and identifying models that could be utilized in practice to achieve the best diabetes management for at risk groups.^