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. View journal article

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. View journal article

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. View journal article

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 … Continue reading The Structure of Perfect Optimal Models with a Two-Category Class Variable and Four or Fewer Endpoints

Paul R. Yarnold Optimal Data Analysis, LLC Two bivariate data sets and four regression models provide the solution. View journal article

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 … Continue reading Value-Added by ODA vs. Chi-Square

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) … Continue reading Maximum-Precision Markov Transition Table: Successive Daily Change in Closing Price of a Utility Stock

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. View journal article

Paul R. Yarnold Optimal Data Analysis, LLC This note compiles initial research exploring the use of ODA in Markov process modelling. View journal article

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. View journal article

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 … Continue reading Comparing Exact Discrete 95% CIs for Model vs. Chance ESS to Evaluate Statistical Significance

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 … Continue reading Optimal Analyses for Cohort Tables

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. View journal article

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. View journal article

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 … Continue reading Using ODA to Confirm a First Order Markov Steady State Process

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 … Continue reading Comparative Accuracy of a Diagnostic Index Modeled Using (Optimized) Regression vs. Novometrics

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 … Continue reading Identifying Maximum-Accuracy Cut-Points for Diagnostic Indexes via ODA

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 the implementation of various non-experimental methods for drawing causal inferences about treatment effects. In the present study we reanalyze the … Continue reading Reanalysis of the National Supported Work Experiment Using ODA

Ariel Linden & Paul R. Yarnold Linden Consulting Group, LLC & Optimal Data Analysis, LLC In a recent series of papers, ODA has been applied to observational data to draw causal inferences about treatment effects. Presently, ODA is applied to survival outcomes from a randomized controlled trial, with a reanalysis of a study by Linden … Continue reading Using ODA in the Evaluation of Randomized Controlled Trials: Application to Survival Outcomes

Ariel Linden & Paul R. Yarnold Linden Consulting Group, LLC & Optimal Data Analysis, LLC In a recent series of papers, ODA has been applied to observational data to draw causal inferences about treatment effects. In this article ODA is applied to data from a randomized controlled trial, with a reanalysis of a study by … Continue reading Using ODA in the Evaluation of Randomized Controlled Trials