Multivariate matching and bias reduction in the surgical outcomes study

Abstract

Background: Outcomes studies often need a level of detail that is not present in administrative data, therefore requiring abstraction of medical charts. Case-control methods may be used to improve statistical power and reduce abstraction costs, but limitations of exact matching often preclude the use of many covariates. Unlike exact matching, multivariate matching may allow cases to be matched simultaneously on hundreds of covariates.

Objectives: To develop multivariate matched case-control pairs in a study of death after surgery in the Medicare population.

Research design: Using 830 randomly selected index cases of patients who died within 60 days from admission, controls were found who did not die within that time period, matching on risk for death and other patient characteristics with up to 173 variables used simultaneously in the matching algorithms.

Subjects: General and orthopedic Medicare surgical cases in Pennsylvania from 1995 to 1996. Controls were either selected from across the entire state (108,765 possible subjects), or from within the same hospital as the case.

Measures: Percent bias reduction and the average difference between cases and controls in units of standard deviations.

Results: Matched controls were far more similar to cases (deaths) upon admission to the hospital than typical patients, both in statewide and within hospital matches. Bias reduction was usually greater than 50% and often approached 100%. The difference between cases and matched controls for most variables was usually below 0.2 SD.

Conclusions: Multivariate matching methods may aid in conducting studies with Medicare claims records by improving the quality of matches, thereby achieving a better understanding of the etiology of outcomes.