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

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

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

Implementing ODA from Within Stata: Nondirectional, Multicategorical Class Variable, Multicategorical Attribute

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

Implementing ODA from Within Stata: Confirmatory Hypothesis, Binary Class Variable, Continuous Attribute

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

Implementing ODA from Within Stata: Nondirectional Hypothesis, Binary Class Variable, Categorical Ordinal Attribute

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (post hoc, nondirectional, two-tailed) hypothesis involving a binary (dichotomous) class variable and a categorical ordinal (three-level) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis. View journal article

Implementing ODA from Within Stata: Exploratory Hypothesis, Binary Class Variable, Categorical Ordinal Attribute

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (post hoc, nondirectional, two-tailed) hypothesis involving a binary (dichotomous) class variable and a categorical ordinal (three-level) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis. View journal article