Suchergebnisse

This Monte Carlo study compares performance of the linear and the logistic mixed-model analyses of simulated community trials having event rates of 37%, 13%, or 5%, intraclass correlations between 0.01 and 0.05, and 17 or 5 denominator degrees of freedom. Type I or Type II error rates showed no essential difference between the two analysis methods. They showed depressed error rates when the event rate or the denominator degrees of freedom were small. The authors conclude that in studies with adequate denominator degrees of freedom, the researcher may use either method of analysis but should accept negative estimates of components of variance to avoid depression of error rates.

Strategies to avoid the penalties of extra variation and reduced degrees offreedom in community trials were compared in Monte Carlo simulations Three conditions were found necessary to ensure nominal Type I and II error rates: (a) Condition variation must be assessed against assignment unit variation, (b) the critical value for the test statistic must be based on the assignment unit degrees offreedom, and (c) estimation of negative intraclass correlations must be allowed in the analysis. Using other test statistics and other degrees of freedom, and fixing negative intraclass correlations at zero often gave Type I and II error rates far from their nominal levels.

Community pluralism predicted both exposure to sources of heart disease prevention information and diversity of source exposure in three Upper Midwest cities (small, medium, large) over a ten-year period. Community differences in both outcomes narrowed, however. The study suggested that during the 1980s, the sheer amount of information about the topic of heart disease prevention and the proliferation of electronic media reduced typical community system and SES-based differences in exposure.

Background: This article revisits an article published in Evaluation Review in 2005 on sample size estimation and power analysis for group-randomized trials. With help from a careful reader, we learned of an important error in the spreadsheet used to perform the calculations and generate the results presented in that article. As we studied the spreadsheet, we discovered other minor errors. When we corrected the errors, we found that the results were substantially different and that the conclusions reported in the original article were not always appropriate. Objective: This article corrects the errors and reports the results as they should have been reported originally. Method: Using a random-effects meta-analytic model, estimates of intraclass correlation were combined from two studies to guide sample size calculations for a new study. Results: The df* method can result in improved power or smaller studies when used a priori to plan future group-randomized trials, though the improvements will be modest in larger studies and will likely be insufficient to provide adequate power to small studies. Conclusion: Smaller group-randomized trials are often desirable, for example, as pilot studies to help plan for a full-scale efficacy trial, as replication studies, or in situations in which resource constraints prohibit a larger trial. We discuss the circumstances under which the df* method will be most helpful and the risks associated with conducting smaller studies.

This study describes a method for incorporating external estimates of intraclass correlation to improve the precision for the analysis of an existing group-randomized trial. The authors use a random-effects meta-analytic approach to pool the information across studies, which takes into account any interstudy heterogeneity that may exist. This approach can be used in several different situations to estimate the degrees of freedom available for an adjusted test of the intervention effect in a study where the challenges of group-randomized trials were not fully considered when the study was planned. The authors discuss the limitations of this approach and the circumstances in which it is likely to be helpful.

Legal standards for liability of commercial sellers & social providers of alcoholic beverages are affected by social norms concerning accountability & responsibility. Using a nationwide probability sample telephone survey of 7,021 US residents, we conducted a randomized experiment in which each subject was asked to respond to multiple vignettes. The vignettes told stories of drinking situations, systematically varying dimensions concerning age of drinker, commercial vs social settings, amount of alcohol consumed, history of previous behavior, & seriousness of damage or injury following drinking. Analyses involved linear mixed (ie, random effects) model regressions, using responses to vignettes as the outcome variable, controlling for a series of sociodemographic, behavioral, & attitudinal measures. Results showed that age of drinker (young), setting (bar), & previous behavior (history of irresponsibility) were most strongly associated with harsher judgments of civil liability. Citizens' multiple standards for assigning legal liability & implications for public policy are discussed. 6 Tables, 3 Figures, 1 Appendix, 27 References. Adapted from the source document.