FORUM: King Salmon in the Root River

For decades I monitored the start of the fall salmonid spawn run in north-eastern Illinois and south-eastern Wisconsin, along dammed lake-run rivers (and one power-plant discharge channel) spilling into Lake Michigan. When the water flow rate is high these streams propel scents associated with spawning fish out into the Lake, leaving a “trail of crumbs” … Continue reading FORUM: King Salmon in the Root River

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, 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

Novometric Temporal Analysis of Monthly Otolaryngology Service Consults Over Five Consecutive Years

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

Comparing CTA to Boosted Regression for Estimating the Propensity Score (Invited)

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)

Differing Cancer-Incidence Rates of Male vs. Female Americans

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

Disparate Cancer-Incidence Rates of Caucasian vs. African 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

Assessing Reproducibility of Novometric Bootstrap Confidence Interval Analysis Using Multiple Seed Numbers (Invited)

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)

Implementing CTA from Within Stata: Reassessing the Propensity Score Estimation Approach Used in the National Supported Work Experiment (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)

Implementing ODA from Within Stata: A Reanalysis of the National Supported Work Experiment

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

Generating Novometric Confidence Intervals in R: Bootstrap Analyses to Compare Model and Chance ESS

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

Implementing ODA from Within Stata: A Priori Hypothesis, Three-Category Class Variable, Four-Level (Integer) Attribute

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

Implementing ODA from Within Stata: Directional Hypothesis, Multicategorical Class Variable, Ordinal Attribute

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