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.
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.
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.
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.
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.
Ariel Linden
Linden Consulting Group, LLC
In this paper, I describe how to evaluate treatment effects in interrupted time-series studies in which the treated unit is contrasted with one or more comparable control groups, using the new Stata package for implementing ODA.
Ariel Linden
Linden Consulting Group, LLC
In this paper, I describe how to assess whether treatment groups are comparable on observed baseline covariates (balance) in non-randomized studies using the new Stata package for implementing ODA.
