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 class variable) and the attribute which is being assessed, thereby maximizing measurement precision, accuracy of prediction, and statistical power.

View journal article

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 the number of citations, recommendations, and article views and downloads. Two analyses were conducted: one forced the model to have stable classification in leave-one-out (LOO) jackknife analysis; the second permitted jackknife instability so long as the LOO Type I error rate was p<0.05. A single-attribute model which achieved relatively strong LOO accuracy was identified.

View journal article

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 ODA analysis.

View journal article

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 journal article

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 journal article

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 the new Stata package for implementing CTA can be used for identifying potential imbalances in characteristics and their interactions when provisionally assigning each new participant to one or the other treatment group. The participant is then permanently assigned to the treatment group that elicits either no or less imbalance than if assigned to the alternate group.

View journal article

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 journal article

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 time-to-event outcomes.

View journal article

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 cut-point along the continuum of test values that maximally discriminates between those with and without the disease.

View journal article

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 for differences between study groups are thus popular among investigators of observational data. In this paper, I describe how the new Stata package for implementing CTA can be used to generate propensity score weights.

View journal article

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 in the observed outcome. In this paper, I describe how the new Stata package for implementing CTA can identify patterns in the data that distinguish study participants from non-participants, revealing potentially complex relationships among individual characteristics that may bias the outcome analysis.

View journal article

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 covariates–separately or interacted with other covariates. In this paper, I describe how to assess the quality of the randomization process in RCTs using the new Stata package for implementing CTA.

View journal article

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 time series is associated with the introduction of the intervention, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention.

View journal article

Statistical Power Analysis in ODA, CTA and Novometrics (Invited)

Nathaniel J. Rhodes

Chicago College of Pharmacy, and the Pharmacometrics Center of Excellence, Midwestern University

Statistical power analysis simulation results are provided for determining the “worst-case” sample size assuming minimal measurement precision and relatively weak or moderate effect strengths. In simulated trials with relatively weak effects (ESS = 24%), greater than 80% and greater than 90% power is achieved for equal sample sizes for unit-weighted analyses of one comparison at n = 70 and n= 90 subjects per group. For simulated trials with moderate effects (ESS=48%) greater than 80 and greater than 90% power was achieved for the same conditions at n = 20 and n = 25 subjects per group. A simulation-based approach is useful for determining the “worst-case” sample sizes in various applications.

View journal article