Michael J. Maloney, Nathaniel J. Rhodes & Paul R. Yarnold Proof School, Midwestern University & Optimal Data Analysis LLC SARS-CoV-2 is the beta-coronavirus responsible for COVID-19. Facemask use has been qualitatively associated with reduced COVID-19 cases, but no study has quantitatively assessed the impact of government mask mandates (MM) on new COVID-19 cases across multiple … Continue reading Mask Mandates Can Rapidly and Efficiently Limit COVID-19 Spread: Month-Over-Month Effectiveness of Governmental Policies in Reducing the Number of New COVID-19 Cases in 37 US States and the District of Columbia

Michael J. Maloney Proof School As the number of COVID-19 deaths in the US increased, various policies were enacted in an effort to slow the spread of the pandemic. As sufficient data accumulate over time, the impact of policy on public health outcomes may be statistically evaluated. The present paper uses ODA to evaluate the … Continue reading Mask Mandate Prevented COVID-19 Deaths in Minnesota

To run the MegaODA software within R, download the ODA package for R available at GitHub and created by Dr. Nathaniel J. Rhodes and Dr. Paul Yarnold. This package serves as an interface for the MegaODA software suite. To utilize this package obtain a licensed copy of the MegaODA software. Once MegaODA is obtained and … Continue reading MegaODA software within R: Package now available on GitHub

Paul R. Yarnold, Ph.D. Optimal Data Analysis, LLC Statistically unmotivated exploratory parametric analysis reported that the mean number of monthly consults at an academic otolaryngology service in 2014-2015 was significantly lower than in 2017-2018, suggesting a trend involving increasing numbers of consults over time. Evaluating these data, exploratory novometric temporal analysis identified a globally optimal … Continue reading Novometric Temporal Analysis of Monthly Otolaryngology Service Consults Over Five Consecutive Years

Michael J. Maloney Proof School Users running MegaODA and/or CTA software using the Windows-10 operating system may execute programs more efficiently than vis-à-vis the standard procedure of using the command prompt. View journal article

Ariel Linden Linden Consulting Group, LLC Boosted regression (BR) has been recommended as a machine learning alternative to logistic regression for estimating the propensity score because of its greater accuracy. Commonly known as multiple additive regression trees, BR is a general, automated, data-adaptive modelling algorithm which can estimate the non-linear relationship between treatment assignment (the … Continue reading Comparing CTA to Boosted Regression for Estimating the Propensity Score (Invited)

Paul R. Yarnold Optimal Data Analysis LLC Novometric classification tree analysis was used to evaluate Surveillance, Epidemiology, and End Results (SEER) Program data to discover cancer sites moderately or relatively strongly predicted by male vs. female gender. Future research using any of the 13 cancer sites which met this criterion should account for gender using … Continue reading Differing Cancer-Incidence Rates of Male vs. Female Americans

Paul R. Yarnold Optimal Data Analysis LLC Surveillance, Epidemiology and End Results (SEER) Program data were used to find cancer sites with at least moderately different rates for African vs. Caucasian Americans. Future research in ten cancer sites which involves subjects represented by these groups should account for associated cancer-incidence disparity in matching or via … Continue reading Disparate Cancer-Incidence Rates of Caucasian vs. African Americans

Paul R. Yarnold Optimal Data Analysis LLC This note interprets a novometric descendant family (DF) as an optimal “cluster” analysis indicating number of discriminable groups and strength of their differences at every differentiable point identified for the sample. View journal article

Paul R. Yarnold Optimal Data Analysis LLC This note updates current MultiODA research activity in the ODA lab. View journal article

Paul R. Yarnold Optimal Data Analysis LLC Novometric (Latin: new measure) statistical theory is introduced. View journal article

Nathaniel J. Rhodes Chicago College of Pharmacy, and the Pharmacometrics Center of Excellence, Midwestern University I study the role of the random seed number in affecting the reliability of a statistical finding, which in turn determines upper and lower bounds of statistical confidence in expected gain or loss yielded from associated decision-making. Simulation research reveals … Continue reading Assessing Reproducibility of Novometric Bootstrap Confidence Interval Analysis Using Multiple Seed Numbers (Invited)

Ariel Linden Linden Consulting Group, 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. The present paper reassesses the approach used by Dehejia and Wahba (2002) for estimating propensity scores … Continue reading Implementing CTA from Within Stata: Reassessing the Propensity Score Estimation Approach Used in the National Supported Work Experiment (Invited)

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 imple­mentation of various non-experimental methods for drawing causal infer­ences about treatment effects. In this paper we reanalyze these data using the … Continue reading Implementing ODA from Within Stata: A Reanalysis of the National Supported Work Experiment

Nathaniel J. Rhodes and Paul R. Yarnold Chicago College of Pharmacy, and the Pharmacometrics Center of Excellence, Midwestern University & Optimal Data Analysis LLC We introduce a method for evaluating the upper and lower bounds of statistical confidence [e.g., an exact discrete confidence interval (CI)] in the expected effect strength for sensitivity of a decision … Continue reading Generating Novometric Confidence Intervals in R: Bootstrap Analyses to Compare Model and Chance ESS

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how to test a directional (confirmatory) hypothesis for a design relating a three-category class (“dependent”) variable and a four-level categorical ordinal attribute (“Likert-type independent variable”) vis-à-vis the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how to assess a confirmatory (directional) hypothesis for a design involving a multicategorical class (“dependent”) variable and an ordinal attribute (“independent variable”) using the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper demonstrates how to evaluate a confirmatory (directional) hypothesis for a design involving a multicategorical class (“dependent”) variable and a multicategorical attribute (“independent variable”) using the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how to evaluate an exploratory (nondirectional) hypothesis for a design involving a multicategorical class (“dependent”) variable and a multicategorical attribute (“independent variable”) using the new Stata package for implementing ODA. View journal article

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how a confirmatory (a priori, directional, one-tailed) hypothesis involving a binary (dichotomous) class variable and continuous (interval or ratio) attribute is evaluated via MegaODA software using the new Stata package implementing ODA analysis. View journal article