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 hypothesized ability of Minnesota’s limited mask mandate (MM) to reduce the daily number of COVID-19 deaths. Validity sensitivity analysis showed that chance-corrected reduction in the number of daily deaths began the day after the MM, and maximum reduction occurred 20 days after the MM.

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 outcome variable) and a large number of covariates including multiple level interaction terms. However, BR is a “black-box” approach that provides scant information as to how the estimates are derived, and recent research has shown that BR can identify erroneous relationships between outcome and covariates in fabricated random data. The present paper revisits the BR approach used by Linden, et al. (2010) for estimating the propensity score and compares it to a propensity score CTA model which is generated by using the new Stata package for implementing CTA.

View journal article

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 matching or propensity score weighting.

View journal article

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 propensity score weighting methods.

View journal article

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 that obtaining a bootstrap solution consistent with an effect (e.g., the lower exact, discrete 2.5th percentile bound of the model bootstrap exceeds the upper exact, discrete 97.5th percentile bound of the chance bootstrap)—more than once using any two independent seeds—supports the hypothesis that the effect is not attributable to random chance because it was replicated vis-à-vis an independent seed. Based upon this finding I suggest consistent use of a primary seed and confirmation using a secondary seed when undertaking model qualification via simulation. This procedure reduces doubt about the veracity of borderline statistically significant findings, and eliminates false positive results identified by replication failure.

View journal article

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 and compares it to a propensity score CTA model which is generated by using the new Stata package for implementing CTA.

View journal article

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 new Stata package for implementing ODA.

View journal article

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 model. The method presented herein uses bootstrap simulations to determine if a given effect is robust and not attributable to random chance (i.e., the lower bound of the model bootstrap CI is greater than the upper bound of the chance bootstrap CI). We evaluated relatively weak, moderate, and strong effects using the novometric bootstrap method. Our approach should serve to increase confidence in the veracity of decision models.

View journal article

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