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.
Author: paulyarnold
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 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.
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 matching or propensity score weighting.
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 propensity score weighting methods.
The Novometric Descendant Family as a Maximum-Accuracy Cluster Analysis
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.
Status of Current MultiODA Research in the Optimal Data Analysis Laboratory
Paul R. Yarnold
Optimal Data Analysis LLC
This note updates current MultiODA research activity in the ODA lab.
What is Novometric Data Analysis?
Paul R. Yarnold
Optimal Data Analysis LLC
Novometric (Latin: new measure) statistical theory is introduced.
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 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.
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 and compares it to a propensity score CTA model which is generated by using the new Stata package for implementing CTA.
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 new Stata package for implementing ODA.
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 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.
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.
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.
Video 1: Purchasing and Downloading the ODA Suite
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.
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.
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.
Implementing ODA from Within Stata: Directional Hypothesis, Binary Class Variable, Ordinal 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 a five-level ordinal attribute is evaluated using MegaODA software via the new Stata package implementing ODA analysis.
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.
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.
