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.
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.
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.
Paul R. Yarnold
Optimal Data Analysis LLC
This note updates current MultiODA research activity in the ODA lab.
Paul R. Yarnold
Optimal Data Analysis LLC
Novometric (Latin: new measure) statistical theory is introduced.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 an ordinal (quintiles) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis.
Paul R. Yarnold
Optimal Data Analysis, LLC
This study predicts change in magnitude of weekly RG-scores recorded over a year for a single individual. Attributes include weekly numbers of citations, recommendations, reads, and full-text downloads. Events in the transition table are weighted by the absolute product of ipsative standard scores for RG-score (treated as the class variable) and the attribute which is being assessed, thereby maximizing measurement precision, accuracy of prediction, and statistical power.
