Paul R. Yarnold & Robert C. Soltysik
Optimal Data Analysis, LLC
This paper uses ODA to weight each event in the transition table by its corresponding absolute change-in-value, thereby maximizing precision of the class variable as well as model accuracy.
Paul R. Yarnold
Optimal Data Analysis, LLC
This paper compares findings obtained using multiple regression analysis vs. novometric analysis to identify the relationship between the type of degree earned by 8th Grade math teachers and the training which they received in cultural and cognitive student diversity methods.
Paul R. Yarnold
Optimal Data Analysis, LLC
Pruning to maximize model accuracy (requiring simple hand computation) is applied to a classification tree model developed via S-PLUS to create propensity scores to improve causal inference in comparing hospitalized vs. ambulatory patients with community-acquired pneumonia. Research reported herein constitutes a thought-provoking example of a striking misalliance between forward analytic thinking and vestige statistical tools—a condition that dominates the empirical literature today. Modifications of ubiquitous methodological practices are suggested.
Paul R. Yarnold
Optimal Data Analysis, LLC
A classification tree pruning methodology that maximizes effect strength for sensitivity is demonstrated for a model developed using classification and regression analysis to identify factors which predict missing data.
Paul R. Yarnold
Optimal Data Analysis, LLC
Growth of classification tree models based upon a secondary a priori performance criterion is demonstrated using a suboptimal classification tree model previously developed using the CHAID algorithm.
Paul R. Yarnold
Optimal Data Analysis, LLC
A classification tree pruning methodology that maximizes effect strength for sensitivity is demonstrated for a model which was developed using CART® soft¬ware to predict influenza among primary care patients.
Paul R. Yarnold
Optimal Data Analysis, LLC
An optimal model has a specific geometric configuration defined by the number of attributes (“independent variables”—schematically illustrated using circles) and endpoints (defined by response on attribute—indicated by rectangles). Branches direct attributes to endpoints via an if/then/else-based decision rule identified by the (ODA/CTA/novometric) algorithm and operationalized vis-à-vis numerical thresholds or categorical rosters which explicitly maximize (weighted) classification accuracy. In hopes of aiding in the visualization, pursuit and discovery of perfectly accurate statistical classification models, this paper presents schematic diagrams which correspond to combinations of number of attributes and endpoints that are possible for a range of optimal models commonly reported.
Paul R. Yarnold
Optimal Data Analysis, LLC
Two bivariate data sets and four regression models provide the solution.
Paul R. Yarnold
Optimal Data Analysis, LLC
Beyond identifying the most accurate classification model which exists for the sample, and estimating cross-generalizability vis-à-vis jackknife, hold-out and/or other validity methods, ODA provides the exact one- or two-tailed P-value, the sensitivity and predictive value for each category of the class variable, and the effect strength corrected for chance.
Paul R. Yarnold
Optimal Data Analysis, LLC
Research seeking to increase the accuracy of traditional Markov analysis-based models, which assess the outcome (class) variable as a two-category variable, studies the use of over-time weighting schemes. This paper demonstrates how to maximize precision of the class variable by using ODA to weight each individual “observation” (event) in the transition table by its corresponding exact absolute change-in-value.
Paul R. Yarnold
Optimal Data Analysis, LLC
This note identifies psychometric research using ODA to assess inter-rater, inter-method and test-retest reliability of scores made using the Naranjo Adverse Drug Reaction Probability (APS) Scale.
Paul R. Yarnold
Optimal Data Analysis, LLC
This note compiles initial research exploring the use of ODA in Markov process modelling.
Paul R. Yarnold
Optimal Data Analysis, LLC
This note illustrates visualizing an ODA optimal cutpoint used to classify observations in training or validity samples, and summarizing resulting accuracy using the confusion matrix and PAC, ESS and D indexes.
Paul R. Yarnold
Optimal Data Analysis, LLC
Satisfaction ratings (1=very dissatisfied; 2=somewhat dissatisfied; 3=neutral; 4=somewhat satisfied; 5=very satisfied) provided by 4,583 hospital patients in three successive cohorts (two consecutive 3-month-long base¬line cohorts and one 3-month-long post-intervention cohort) were compared to evaluate a program which was designed to increase patient-rated satisfaction with in-hospital received care (Table 1).
Paul R. Yarnold
Optimal Data Analysis, LLC
Cross-classification tables may be created for one or more “cohorts”— groups of observations defined by a common event such as the year of one’s birth, graduation, employment, marriage, disease diagnosis or incarceration—and assessed at two or more points in time on one or more variables reflecting the substantive focus of the study. This article demonstrates exploratory maximum-accuracy evaluation of cohort, aging, and time effects for a standard cohort table.
Paul R. Yarnold
Optimal Data Analysis, LLC
When a first-order Markov model cannot be confirmed one approach is subdividing the sample into strata each having a distinct set of transition probabilities. This note demonstrates the use of ODA to assess whether stratification resulted in significantly different transition matrices.
Paul R. Yarnold
Optimal Data Analysis, LLC
This note demonstrates the use of ODA to test the hypothesis of an underlying second-order Markovian process.
Paul R. Yarnold
Optimal Data Analysis, LLC
Sufficiently iterated over time periods a first order Markovian change process defined by a constant transition matrix yields a steady state. Consecutive transition matrices are compared by Goodman’s chi-square test to assess if a steady state has been achieved. This note demonstrates the analogous use of ODA to assess if such transition matrices differ.
Ariel Linden & Paul R. Yarnold
Linden Consulting Group, LLC & Optimal Data Analysis, LLC
Diagnostic screening tests are used to predict an individual’s graduated disease status which is measured on an ordered scale assessing disease progression (severity of illness). Maximizing the predictive accuracy of the diagnostic or screening test is paramount to correctly identifying an individual’s actual score along the ordered continuum. The present study compares two approaches for mapping a statistical model to a diagnostic index in order to make accurate outcome predictions for individuals. The application involves a dataset composed of multiple biomedical voice measurements for 42 individuals with early-stage Parkinson’s disease, who completed a six-month trial of a device for remote symptom progression telemonitoring. For 16 voice measures, each treated as a main effect, ordinary least-squares regression is used to predict baseline motor impairment component score. ODA is used to maximize accuracy of the regression model when it is mapped to the diagnostic index, and results are compared with accuracy achieved by the novometric solution.
Ariel Linden & Paul R. Yarnold
Linden Consulting Group, LLC & Optimal Data Analysis, LLC
Maximizing the discriminatory accuracy of a diagnostic or screening test is paramount to correctly identifying individuals with vs. without the disease or disease marker. In this paper we demonstrate the use of ODA to identify the optimal cut-point which best discriminates between those with vs. without the disease (or marker) under study, for any diagnostic test. We illustrate this methodology using a dataset composed of a range of repeated biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). A logistic regression model was used to estimate the probability that each observation was from a person with vs. without PD as a function of 22 voice measurement variables, entered in the model as main effects only. Five different methods for computing a diagnostic cut-point on estimated probability are compared.
