Implementing ODA From Within Stata: An Application to Data From a Randomized Controlled Trial (Invited)

Ariel Linden

Linden Consulting Group, LLC

In this paper, the new Stata package for implementing ODA is introduced by reanalyzing data from a study by Linden and Butterworth (2014) that investigated the effect of a comprehensive hospital-based intervention in reducing readmissions for chronically ill patients. In the original analysis, negative binomial regression was used to evaluate readmission rates and emergency department visit rates at 30 and 90 days, and no treatment effects were found. However, ODA is a superior analytic approach because of its insensitivity to skewed data, model-free permutation tests to derive P values, identification of the threshold value which best discriminates intervention and control groups, use of a chance- and complexity-corrected indexes of classification accuracy, and cross-validation to assess generalizability of the findings.

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Randomized Blocks Designs: Omnibus vs. Pairwise Comparison, Fixed vs. Relative Optimal Discriminant Threshold, and Raw vs. Ipsative z-Score Measures

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis, LLC & Linden Consulting Group, LLC

This study extends recent research assessing the use of relative thresholds in matched-pairs designs, for a randomized blocks design in which four treatments are randomly assigned to blood samples drawn from each of eight people (each person treated as a block). Both raw and ipsatively standardized plasma clotting times are compared between treatments.

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