A Bayesian approach to estimating the intraclass correlation coefficient

A Bayesian approach to estimating the intraclass correlation coefficient was used for this research project. The background of the intraclass correlation coefficient, a summary of its standard estimators, and a review of basic Bayesian terminology and methodology were presented. The conditional posterior density of the intraclass correlation coefficient was then derived and estimation procedures related to this derivation were shown in detail. Three examples of applications of the conditional posterior density to specific data sets were also included. Two sets of simulation experiments were performed to compare the mean and mode of the conditional posterior density of the intraclass correlation coefficient to more traditional estimators. Non-Bayesian methods of estimation used were: the methods of analysis of variance and maximum likelihood for balanced data; and the methods of MIVQUE (Minimum Variance Quadratic Unbiased Estimation) and maximum likelihood for unbalanced data. The overall conclusion of this research project was that Bayesian estimates of the intraclass correlation coefficient can be appropriate, useful and practical alternatives to traditional methods of estimation. ^

A study of nurses' judgement of and participation in decision-making for nursing home placement for the elderly

This study investigated the effects of patient variables (physical and cognitive disability, significant others' preference and social support) on nurses' nursing home placement decision-making and explored nurses' participation in the decision-making process.^ The study was conducted in a hospital in Texas. A sample of registered nurses on units that refer patients for nursing home placement were asked to review a series of vignettes describing elderly patients that differed in terms of the study variables and indicate the extent to which they agreed with nursing home placement on a five-point Likert scale. The vignettes were judged to have good content validity by a group of five colleagues (expert consultants) and test-retest reliability based on the Pearson correlation coefficient was satisfactory (average of.75) across all vignettes.^ The study tested the following hypotheses: Nurses have more of a propensity to recommend placement when (1) patients have severe physical disabilities; (2) patients have severe cognitive disabilities; (3) it is the significant others' preference; and (4) patients have no social support nor alternative services. Other hypotheses were that (5) a nurse's characteristics and extent of participation will not have a significant effect on their placement decision; and (6) a patient's social support is the most important, single factor, and the combination of factors of severe physical and cognitive disability, significant others' preference, and no social support nor alternative services will be the most important set of predictors of a nurse's placement decision.^ Analysis of Variance (ANOVA) was used to analyze the relationships implied in the hypothesis. A series of one-way ANOVA (bivariate analyses) of the main effects supported hypotheses one-five.^ Overall, the n-way ANOVA (multivariate analyses) of the main effects confirmed that social support was the most important single factor controlling for other variables. The 4-way interaction model confirmed that the most predictive combination of patient characteristics were severe physical and cognitive disability, no social support and the significant others did not desire placement. These analyses provided an understanding of the importance of the influence of specific patient variables on nurses' recommendations regarding placement. ^

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. ^