Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

Maximizing the discriminatory accuracy of a diagnostic or screening test is paramount to correctly identifying individuals with vs. without the disease or disease marker. In this paper we demonstrate the use of ODA to identify the optimal cut-point which best discriminates between those with vs. without the disease (or marker) under study, for any diagnostic test. We illustrate this methodology using a dataset composed of a range of repeated biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). A logistic regression model was used to estimate the probability that each observation was from a person with vs. without PD as a function of 22 voice measurement variables, entered in the model as main effects only. Five different methods for computing a diagnostic cut-point on estimated probability are compared.

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Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

Data from the National Supported Work (NSW) randomized experiment have been used frequently over the past 30 years to demonstrate the implementation of various non-experimental methods for drawing causal inferences about treatment effects. In the present study we reanalyze the NSW data using ODA and compare results with those estimated using t-tests. Statistical results were largely consistent between methods, however ODA found 22.2% (2 of 9) preintervention characteristics to be imbalanced. Given that ODA avoids assumptions required of parametric methods, and is insensitive to skewed data and outliers, ODA should be considered the preferred approach when evaluating data from randomized experiments.

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Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

In a recent series of papers, ODA has been applied to observational data to draw causal inferences about treatment effects. Presently, ODA is applied to survival outcomes from a randomized controlled trial, with a reanalysis of a study by Linden and Butterworth [2014] that investigated (as a secondary outcome) the effect of a comprehensive hospital-based intervention in reducing mortality at 90 days for chronically ill patients. In the original analysis, differences in mortality rates between treatment and control groups were estimated using logistic regression and calculated as both risk differences and risk ratios, and a treatment effect was found in the subgroup of patients with chronic obstructive pulmonary disease (COPD), but not in the other subgroup of patients with congestive heart failure (CHF). In the present study, we reanalyze these results using both Cox regression, and weighted ODA, wherein the weight of every subject is their follow-up time (i.e., number of days of follow-up).

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Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

In a recent series of papers, ODA has been applied to observational data to draw causal inferences about treatment effects. In this article ODA is applied to data from a randomized controlled trial, with a reanalysis of 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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Paul R. Yarnold

Optimal Data Analysis, LLC

Two prediction-interval scaling strategies are illustrated for a 10-point ordinal attribute: one strategy compresses the most extreme scores, and the other strategy compresses the least extreme scores on the scale.

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Paul R. Yarnold

Optimal Data Analysis, LLC

This first correspondence note addresses the question of how one may obtain a personal copy of ODA, MegaODA and/or CTA software.

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Paul R. Yarnold

Optimal Data Analysis, LLC

For class variables with two categories, ODA and MegaODA software employ Fisher’s one-tailed exact test to assess p associated with LOO classification performance. For class variables having three or more categories, LOO p is not provided. This article discusses how to use ODA and MegaODA software to obtain LOO p in this situation.

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