Paul R. Yarnold Optimal Data Analysis, LLC Novometric theory originally consisted of four axioms and applied to designs with a binary class variable and an unrestricted attribute. This note adds a fifth axiom generalizing novometrics to designs involving unrestricted class variables. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC Correlation matrices are commonly used to summarize the linear associations for all possible pairings in a set of ordered variables. This paper exports this methodology, creating the conceptual analogue of a correlation matrix in the optimal data analysis (ODA) statistical paradigm. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC Ordered dependent (class) variables are ordinarily modeled by Pearson correlation (r) in univariable applications with one ordered independent variable (attribute), and by multiple regression analysis (MRA) in multivariable applications involving more than one attribute. Prior research demonstrated the use of ODA to maximize predictive accuracy of r and … Continue reading Novometrics vs. Regression Analysis: Modeling Patient Satisfaction with Care Received in the Emergency Room

Paul R. Yarnold Optimal Data Analysis, LLC A convenience sample of 293 ambulatory women patients, all older than 65 years of age, were surveyed in a general medicine clinic. Correlation (r), multiple regression analysis (MRA), and novometric analysis were used to model the relationship of scores (even integers) on the TOFHLA literacy measure (the dependent … Continue reading Novometrics vs. Regression Analysis: Literacy, and Age and Income, of Ambulatory Geriatric Patients

Paul R. Yarnold & Charles L. Bennett Optimal Data Analysis, LLC The application of ODA to maximize the predictive accuracy achieved by a linear model that was developed using multiple regression analysis (MRA) was previously considered. The present paper illustrates the maximum-accuracy alternative to MRA. View journal article

Paul R. Yarnold & Charles L. Bennett Optimal Data Analysis, LLC The use of ODA to maximize the predictive accuracy of linear Pearson correlation (r) models was previously considered. The present paper discusses and illustrates the maximum-accuracy alternative to r. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis, LLC Employed to model an ordered dependent (class) variable, Pearson correlation (r) is used in univariable applications featuring one ordered independent variable (attribute), and multiple regression analysis (MRA) is utilized in multivariable applications featuring two or more attributes. Prior research demonstrated how to maximize the predictive … Continue reading Novometric Analysis with Ordered Class Variables: The Optimal Alternative to Linear Regression Analysis

Paul R. Yarnold Optimal Data Analysis, LLC As concerns the existence of statistically reliable enumerated-optimal classification tree analysis (EO-CTA) model(s) for a given application, possible alternative analytic outcomes are: no EO-CTA model exists; one model exists; or a descendant family (DF) that consists of two or more models exists. Models in a DF maximize ESS … Continue reading How Many EO-CTA Models Exist in My Sample and Which is the Best Model?

Paul R. Yarnold Optimal Data Analysis, LLC In CTA weighting by prior odds is used if a model is sought to maximize ESS, which is explicitly optimized by a pruning algorithm that deconstructs a fully-grown model into all nested sub-branches and then reassembles all possible combinations of sub-branches to identify the configuration with greatest ESS. … Continue reading Pruning CTA Models to Maximize PAC

Paul R. Yarnold Optimal Data Analysis, LLC Usually it is possible to identify numerous different hierarchically-optimal classification tree analysis (HO-CTA) models in applications having an adequate sample size and involving multiple attributes. The models differ in complexity—defined as the number of endpoints representing distinct patient strata: the fewer the number of strata, the more parsimonious … Continue reading Identifying the Descendant Family of HO-CTA Models by using the Minimum Denominator Selection Algorithm: Maximizing ESS versus PAC

Paul R. Yarnold & Ariel Linden Optimal Data Analysis, LLC A maximum-accuracy machine-learning method for predicting dose of exposure based on distribution of the response variable was recently introduced. Herein we demonstrate the advantages of eliminating baseline variation in the response variable via transformation by ipsative standardization. Using data measuring forearm blood flow responses to … Continue reading Using Machine Learning to Model Dose-Response Relationships via ODA: Eliminating Response Variable Baseline Variation by Ipsative Standardization

Paul R. Yarnold Optimal Data Analysis, LLC As a high-ranking cause of human mortality, adverse drug reactions (ADRs) are the focus of an enormous literature, and optimal statistical methods have proven undaunted by the analysis-challenging geometry of multi-site longitudinal medical data sets. Two broadly-used causality assessment algorithms for identifying ADRs are the Naranjo and World … Continue reading Causality of Adverse Drug Reactions: The Upper-Bound of Arbitrated Expert Agreement for Ratings Obtained by WHO and Naranjo Algorithms

Paul R. Yarnold Optimal Data Analysis, LLC UniODA is used to study the effect of interruptions on the course of behavior normally seen in longitudinal (temporal) series, for cases or for groups, in applications such as modeling of mortality rates after exposure to environmental toxins, evaluating symptom reduction in chronic disease after human or artificial … Continue reading Ascertaining Intervention Efficacy

Paul R. Yarnold Optimal Data Analysis, LLC UniODA may be used to identify two different types of (weighted) maximum-accuracy models. First, ODA can identify models that explicitly maximize overall percentage accuracy in classification or PAC—that is, the percentage of the total sample that is correctly classified by the model. Second, ODA can identify models that … Continue reading Maximizing Overall Percentage Accuracy in Classification: Discriminating Study Groups in the National Pressure Ulcer Long-Term Care Study (NPULS)

Paul R. Yarnold Optimal Data Analysis, LLC Chi-square analysis is often used to analyze data in contingency tables created by crossing two categorical variables, with at least one having three or more categories. Researchers report the associated omnibus (overall) p value to indicate the statistical reliability (not the strength) of the association between the variables. … Continue reading ODA vs. Chi-Square: Describing Baseline Data from the National Pressure Ulcer Long-Term Care Study (NPULS)

Paul R. Yarnold Optimal Data Analysis, LLC Data are from a contingency table used to determine the relationship between years of schooling arbitrarily parsed into six ordered categories, and ethnic group measured on a categorical variable with seven levels. Although ordinal data are inappropriate for analysis via chi-square-based methods, log-linear analysis was used to investigate … Continue reading Pairwise Comparisons using UniODA vs. Not Log-Linear Model: Ethnic Group and Schooling in the 1980 Census

Paul R. Yarnold Optimal Data Analysis, LLC Data are from a classic 4 x 6 contingency table used to determine the relationship (if any) between mental health status measured using four ordered categories, and socioeconomic status (SES) measured using six ordered categories. Although ordinal data are inappropriate for analysis via chi-square-based methods, log-linear analysis was … Continue reading UniODA vs. Not Log-Linear Model: The Relationship of Mental Health Status and Socioeconomic Status

Paul R. Yarnold Optimal Data Analysis, LLC CTA models are developed using one of three different strategies as concerns “leave-one-out” (LOO) analysis: (a) ignore LOO analysis; (b) only include attributes having identical ESS in training and LOO analysis in the model (the “LOO stable” criterion); or (c) only include attributes having the highest ESS in … Continue reading Determining Jackknife ESS for a CTA Model with Chaotic Instability

Paul R. Yarnold Optimal Data Analysis, LLC CTA models may be constructed using three different strategies with respect to consideration of “leave-one-out” (LOO) jackknife validity analysis: (1) ignore LOO validity analysis; (2) only include attributes yielding the same ESS in training and LOO analysis in the model (the “LOO stable” criterion); or (3) include attributes … Continue reading Using UniODA to Determine the ESS of a CTA Model in LOO Analysis