One-Stop Source for Risk Adjustment Models

STATinMED Research’s cutting edge algorithm applies propensity score matching techniques to your data. The ProbChoice™ algorithm:

• tests the assumptions behind propensity score matching
• statistically chooses the sets of variables to match on
• uses up to 25 different matching techniques
• suggests the best technique for your data applying statistical guidelines published by a STATinMED consultant
• does sensitivity analysis against the unmeasurable factors
• allows you to match more than two categories, such as multi-level treatment choices
• increases the efficiency of your estimator when there is a lack of overlap in the groups

Feel free to download our ProbChoice™ flyer.

The merits of using propensity score matching have been increasingly recognized over the years. However, different propensity score matching techniques may produce different results in finite samples.

Unlike Randomized Clinical Trials, with its long and well-documented history, the application of design processes in propensity score matching is not well established. Moreover, there are numerous factors to consider in implementing propensity score matching in general, a process further complicated by the number of matching routines available. Despite its frequent use in observational research, no coherent, rule-based decision matrix currently exists in the literature. The potential for misapplication of these techniques is high and contributes to the controversy as to the value of the methodology.

Sensitivity analysis of the matching techniques is especially important because none of the proposed methods in the literature is a priori superior to the others.

The ProbChoice™ algorithm is a one-stop source for your projects tailored to your propensity score techniques.