Dataset Information

Indirect Standardization Matching: Assessing Specific Advantage and Risk Synergy.

ABSTRACT:

Objective

To develop a method to allow a hospital to compare its performance using its entire patient population to the outcomes of very similar patients treated elsewhere.

Data sources/setting

Medicare claims in orthopedics and common general, gynecologic, and urologic surgery from Illinois, New York, and Texas from 2004 to 2006.

Study design

Using two example "focal" hospitals, each hospital's patients were matched to 10 very similar patients selected from 619 other hospitals.

Data collection/extraction methods

All patients were used at each focal hospital, and we found the 10 closest matched patients from control hospitals with exactly the same principal procedure as each focal patient.

Principal findings

We achieved exact matches on all procedures and very close matches for other patient characteristics for both hospitals. There were few to no differences between each hospital's patients and their matched control patients on most patient characteristics, yet large and significant differences were observed for mortality, failure-to-rescue, and cost.

Conclusion

Indirect standardization matching can produce fair audits of quality and cost, allowing for a comprehensive, transparent, and relevant assessment of all patients at a focal hospital. With this approach, hospitals will be better able to benchmark their performance and determine where quality improvement is most needed.

SUBMITTER: Silber JH

PROVIDER: S-EPMC5134157 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Publications

Indirect Standardization Matching: Assessing Specific Advantage and Risk Synergy.

Silber Jeffrey H JH Rosenbaum Paul R PR Ross Richard N RN Ludwig Justin M JM Wang Wei W Niknam Bijan A BA Hill Alexander S AS Even-Shoshan Orit O Kelz Rachel R RR Fleisher Lee A LA

Health services research 20160229 6

<h4>Objective</h4>To develop a method to allow a hospital to compare its performance using its entire patient population to the outcomes of very similar patients treated elsewhere.<h4>Data sources/setting</h4>Medicare claims in orthopedics and common general, gynecologic, and urologic surgery from Illinois, New York, and Texas from 2004 to 2006.<h4>Study design</h4>Using two example "focal" hospitals, each hospital's patients were matched to 10 very similar patients selected from 619 other hospi ...[more]

PMID: 26927625

Similar Datasets

Project description:BackgroundAnchored covariate-adjusted indirect comparisons inform reimbursement decisions where there are no head-to-head trials between the treatments of interest, there is a common comparator arm shared by the studies, and there are patient-level data limitations. Matching-adjusted indirect comparison (MAIC), based on propensity score weighting, is the most widely used covariate-adjusted indirect comparison method in health technology assessment. MAIC has poor precision and is inefficient when the effective sample size after weighting is small.MethodsA modular extension to MAIC, termed two-stage matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two parametric models. One estimates the treatment assignment mechanism in the study with individual patient data (IPD), the other estimates the trial assignment mechanism. The first model produces inverse probability weights that are combined with the odds weights produced by the second model. The resulting weights seek to balance covariates between treatment arms and across studies. A simulation study provides proof-of-principle in an indirect comparison performed across two randomized trials. Nevertheless, 2SMAIC can be applied in situations where the IPD trial is observational, by including potential confounders in the treatment assignment model. The simulation study also explores the use of weight truncation in combination with MAIC for the first time.ResultsDespite enforcing randomization and knowing the true treatment assignment mechanism in the IPD trial, 2SMAIC yields improved precision and efficiency with respect to MAIC in all scenarios, while maintaining similarly low levels of bias. The two-stage approach is effective when sample sizes in the IPD trial are low, as it controls for chance imbalances in prognostic baseline covariates between study arms. It is not as effective when overlap between the trials' target populations is poor and the extremity of the weights is high. In these scenarios, truncation leads to substantial precision and efficiency gains but induces considerable bias. The combination of a two-stage approach with truncation produces the highest precision and efficiency improvements.ConclusionsTwo-stage approaches to MAIC can increase precision and efficiency with respect to the standard approach by adjusting for empirical imbalances in prognostic covariates in the IPD trial. Further modules could be incorporated for additional variance reduction or to account for missingness and non-compliance in the IPD trial.

Dataset Information

Indirect Standardization Matching: Assessing Specific Advantage and Risk Synergy.

Objective

Data sources/setting

Study design

Data collection/extraction methods

Principal findings

Conclusion

Publications

Indirect Standardization Matching: Assessing Specific Advantage and Risk Synergy.

Similar Datasets

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