Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes: J. L. JOHNSONET AL.

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes, and accounts for within cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least 6 clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Since small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach

Parameters in panoramic radiography for differentiation of radiolucent lesions

OBJECTIVE: The aims of this study were to establish parameters in panoramic radiography for interpretation of unilocular radiolucent lesions, and to compare the accuracy of diagnoses given by examiners before and after using these parameters. MATERIAL AND METHODS: In Part I, 12 specialists analyzed 24 images and the diagnostic criteria used by each examiner to make correct diagnoses were used to build a list of basic radiographic parameters for each pathology (ameloblastoma, keratocystic odontogenic tumor, dentigerous cyst, and idiopathic bone cavity). In Part II, this list was used by 6 undergraduate students (Un), 8 recently graduated dentists (D), 3 oral pathologists, 3 stomatologists, 3 oral radiologists, and 3 oral surgeons to diagnose the corresponding pathologies in the other set of 24 panoramic radiographs (T2). The same analysis occurred without using this list (T1). The method of generalized estimating equations (GEE) was used in order to estimate the probability of making a correct diagnosis depending on the specialty of the examiner, type of lesion, and moment of the evaluation, T1 or T2 (before or after they had access to the list of parameters, respectively). RESULTS: Higher values were obtained for the probability (GEE) of making a correct diagnosis on T2; the group Un presented the highest improvement (14.6 %); no differences between the probabilities were observed either between Un and D, or among the different groups of specialists. CONCLUSIONS: The use of panoramic radiographic parameters did allow improving the diagnostic accuracy for all groups of examiners

GLIMMPSE Lite: calculating power and sample size on smartphone devices.

Researchers seeking to develop complex statistical applications for mobile devices face a common set of difficult implementation issues. In this work, we discuss general solutions to the design challenges. We demonstrate the utility of the solutions for a free mobile application designed to provide power and sample size calculations for univariate, one-way analysis of variance (ANOVA), GLIMMPSE Lite. Our design decisions provide a guide for other scientists seeking to produce statistical software for mobile platforms