Implementing ODA from Within Stata: Confirmatory Hypothesis, Binary Class Variable, and 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 an ordinal (quintiles) attribute is evaluated using MegaODA software using the new Stata package implementing ODA analysis. View journal article

Optimal Weighted Markov Analysis: Predicting Serial RG-Score

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 … Continue reading Optimal Weighted Markov Analysis: Predicting Serial RG-Score

Modeling an Individual’s Weekly Change in RG-Score via Novometric Single-Case Analysis

Paul R. Yarnold Optimal Data Analysis, LLC Research Gate (RG) weekly summary statistics—including RG-score (the class or “dependent variable”), and number of citations, recommendations, and article views and downloads (the attributes or “independent variables”), were obtained for a single user. Single-case novometric classification tree analysis (CTA) was used to predict RG-score as a function of … Continue reading Modeling an Individual’s Weekly Change in RG-Score via Novometric Single-Case Analysis

Implementing ODA from Within Stata: Exploratory Hypothesis, Binary Class Variable, and Ordinal (Rank) Attribute

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

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

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (i.e., post hoc, nondirectional, two-tailed) hypothesis involving a binary (i.e., dichotomous) class variable and a multicategorical attribute with three or more categorical levels is evaluated using MegaODA software vis-à-vis the new Stata package implementing … Continue reading Implementing ODA from Within Stata: Exploratory Hypothesis, Binary Class Variable, and Multicategorical Attribute

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

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how a confirmatory (i.e., a priori, directional, or one-tailed) hypothesis involving a binary (i.e., dichotomous) class variable and a binary attribute can be evaluated using MegaODA software vis-à-vis the new Stata package for implementing ODA analysis. View … Continue reading Implementing ODA from Within Stata: Confirmatory Hypothesis, Binary Class Variable, and Binary Attribute

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

Paul R. Yarnold & Ariel Linden Optimal Data Analysis LLC & Linden Consulting Group LLC This paper describes how an exploratory (i.e., post hoc, nondirectional, or two-tailed) hypothesis involving a binary (i.e., dichotomous) class variable and a binary attribute can be evaluated using MegaODA software vis-à-vis the new Stata package for implementing ODA analysis. View … Continue reading Implementing ODA from Within Stata: Exploratory Hypothesis, Binary Class Variable, and Binary Attribute

Implementing CTA from Within Stata: Minimizing Imbalances on Patient Characteristics Between Treatment Groups in Randomized Trials (Invited)

Ariel Linden Linden Consulting Group, LLC Randomization ensures that treatment groups do not differ systematically in their characteristics, thereby reducing threats to validity that may otherwise explain differences in outcomes. Large observed imbalances in patient characteristics may indicate that selection bias is being introduced into the treatment allocation process. In this paper, I describe how … Continue reading Implementing CTA from Within Stata: Minimizing Imbalances on Patient Characteristics Between Treatment Groups in Randomized Trials (Invited)

Implementing CTA from Within Stata: Identifying Causal Mechanisms in Interventions (Invited)

Ariel Linden Linden Consulting Group, LLC Mediation analysis identifies causal pathways by testing the relationships between the treatment, the outcome, and an intermediate variable that mediates the relationship between the treatment and the outcome. In this paper, I describe how the new Stata package for implementing CTA can be used to assess mediation effects. View … Continue reading Implementing CTA from Within Stata: Identifying Causal Mechanisms in Interventions (Invited)

Implementing CTA from Within Stata: Modeling Time‐to‐Event (Survival) Data (Invited)

Ariel Linden Linden Consulting Group, LLC Health researchers frequently generate predictive models of time-to-event outcomes (e.g., death, onset of disease, hospital readmission)to assist clinicians to better understand the disease process and manage their patients. In this paper, I describe how the new Stata package for implementing CTA can be used to generate predictive models with … Continue reading Implementing CTA from Within Stata: Modeling Time‐to‐Event (Survival) Data (Invited)

Implementing ODA from Within Stata: Finding the Optimal Cut-Point of a Diagnostic Test or Index (Invited)

Ariel Linden Linden Consulting Group, LLC Maximizing the discriminatory accuracy of a diagnostic or screening test is paramount to ensuring that individuals with or without the disease (or disease marker) are correctly identified as such. In this paper, I describe how the new Stata package for implementing ODA can be used to determine the optimal … Continue reading Implementing ODA from Within Stata: Finding the Optimal Cut-Point of a Diagnostic Test or Index (Invited)

Implementing CTA from Within Stata: Using CTA to Generate Propensity Score Weights (Invited)

Ariel Linden Linden Consulting Group, LLC In contrast to randomized studies in which individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and are therefore likely to differ in their characteristics compared to those who elect not to participate. Analytic approaches using the propensity score to adjust … Continue reading Implementing CTA from Within Stata: Using CTA to Generate Propensity Score Weights (Invited)

Implementing CTA from Within Stata: Characterizing Participation in Observational Studies (Invited)

Ariel Linden Linden Consulting Group, LLC In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and are therefore likely to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference … Continue reading Implementing CTA from Within Stata: Characterizing Participation in Observational Studies (Invited)

Implementing CTA from Within Stata: Assessing the Quality of the Randomization Process in Randomized Controlled Trials (Invited)

Ariel Linden Linden Consulting Group, LLC In randomized controlled trials (RCT) of sufficient size, we expect the treatment and control groups to be balanced on both observed and unobserved characteristics, and any imbalances are considered to be due to chance. CTA can be used to determine whether treatment assignment can be predicted by observed pre-intervention … Continue reading Implementing CTA from Within Stata: Assessing the Quality of the Randomization Process in Randomized Controlled Trials (Invited)

Implementing ODA from Within Stata: Identifying Structural Breaks in Single-Group Interrupted Time Series Designs (Invited)

Ariel Linden Linden Consulting Group, LLC In this paper, I describe how to determine if any structural breaks exist in a time series prior to the introduction of an intervention using the new Stata package for implementing ODA. Given that the internal validity of the design rests on the premise that the interruption in the … Continue reading Implementing ODA from Within Stata: Identifying Structural Breaks in Single-Group Interrupted Time Series Designs (Invited)