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Focused on interpreting data as statistical evidence, the evidential paradigm uses likelihood ratios to measure the strength of statistical evidence. Under this paradigm, re-examination of accumulating evidence is encouraged because (i) the likelihood ratio, unlike a p-value, is unaffected by the number of examinations and (ii) the probability of observing(More)
INTRODUCTION/PURPOSE Although metal baseball bats are widely believed to outperform wood bats, there are few scientific studies which support this. In a batting cage study, Greenwald et al. found that baseballs hit with a metal bat traveled faster than those hit with a wood bat, but the factors responsible for this difference in bat performance remain(More)
We present likelihood methods for defining the non-inferiority margin and measuring the strength of evidence in non-inferiority trials using the 'fixed-margin' framework. Likelihood methods are used to (1) evaluate and combine the evidence from historical trials to define the non-inferiority margin, (2) assess and report the smallest non-inferiority margin(More)
OBJECTIVE To evaluate the effectiveness of a culturally adapted, primary care-based nurse-community health worker (CHW) team intervention to support diabetes self-management on diabetes control and other biologic measures. RESEARCH DESIGN AND METHODS Two hundred sixty-eight Samoan participants with type 2 diabetes were recruited from a community health(More)
The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false(More)
To many, the foundations of statistical inference are cryptic and irrelevant to routine statistical practice. The analysis of 2 x 2 contingency tables, omnipresent in the scientific literature, is a case in point. Fisher's exact test is routinely used even though it has been fraught with controversy for over 70 years. The problem, not widely acknowledged,(More)
comments that led to a substantially improved version of this manuscript. Abstract Current approaches to modeling receiver operating characteristic (ROC) curves ignore the magnitude of the observed test scores and are unable to connect a test score with its estimated diagnostic performance (sensitivity and specificity). We show here how to construct a model(More)
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