Adaptability of High Dimensional Propensity Score Procedure in the Transition from ICD-9 to ICD-10 in the US Healthcare System.

HDPS algorithm ICD-10 ICD-9 comparative effectiveness research confounding propensity score real-world evidence

Journal

Clinical epidemiology
ISSN: 1179-1349
Titre abrégé: Clin Epidemiol
Pays: New Zealand
ID NLM: 101531700

Informations de publication

Date de publication:
2023
Historique:
received: 14 02 2023
accepted: 20 04 2023
medline: 5 6 2023
pubmed: 5 6 2023
entrez: 5 6 2023
Statut: epublish

Résumé

High-Dimensional Propensity Score procedure (HDPS) is a data-driven approach to assist control for confounding in pharmacoepidemiologic research. The transition to the International Classification of Disease (ICD-9/10) in the US health system may pose uncertainty in applying the HDPS procedure. We assembled a base cohort of patients in MarketScan A similar bias reduction was observed in cohorts where patient selection pattern from each ICD era was comparable between the exposure groups. In the presence of considerable disparity in patient selection, we observed a bimodal distribution of propensity scores in the data dimensions strategy, indicating instrument-like covariates. Moreover, the CCS mapping strategy resulted in at least 30% less bias than pooled RR and data dimensions strategies (RMSE: 0.14, 0.19, 0.21, respectively) in this scenario. Mapping ICD codes to a stable terminology like CCS serves as a helpful strategy to reduce residual bias when deploying HDPS in pharmacoepidemiologic studies spanning both ICD eras.

Sections du résumé

Background UNASSIGNED
High-Dimensional Propensity Score procedure (HDPS) is a data-driven approach to assist control for confounding in pharmacoepidemiologic research. The transition to the International Classification of Disease (ICD-9/10) in the US health system may pose uncertainty in applying the HDPS procedure.
Methods UNASSIGNED
We assembled a base cohort of patients in MarketScan
Results UNASSIGNED
A similar bias reduction was observed in cohorts where patient selection pattern from each ICD era was comparable between the exposure groups. In the presence of considerable disparity in patient selection, we observed a bimodal distribution of propensity scores in the data dimensions strategy, indicating instrument-like covariates. Moreover, the CCS mapping strategy resulted in at least 30% less bias than pooled RR and data dimensions strategies (RMSE: 0.14, 0.19, 0.21, respectively) in this scenario.
Conclusion UNASSIGNED
Mapping ICD codes to a stable terminology like CCS serves as a helpful strategy to reduce residual bias when deploying HDPS in pharmacoepidemiologic studies spanning both ICD eras.

Identifiants

pubmed: 37274833
doi: 10.2147/CLEP.S405165
pii: 405165
pmc: PMC10237200
doi:

Types de publication

Journal Article

Langues

eng

Pagination

645-660

Informations de copyright

© 2023 Sarayani et al.

Déclaration de conflit d'intérêts

Almut Winterstein has received funding from the FDA, NIH, PCORI, AHRQ, the Bill and Melinda Gates Foundation, Merck & Co. and the state of Florida. She has received consulting honoraria from Arbor Pharmaceuticals and Genentech. She is a special government employee of the FDA and has served as the Chair of the Drug Safety and Risk Management (DSaRM) Advisory Committee from 2012 to 2018. None is related to this project or poses a conflict of interest. Christian Hampp is employed by Regeneron Pharmaceuticals, Inc., and owns company stock. Regeneron did not provide study funding, does not hold a marketing license for any of the study drugs, and had no role in manuscript development and decision to publish. Amir Sarayani is currently affiliated with Janssen Research & Development LLC. The study conceptualization, conduct, and manuscript writing were part of his doctoral dissertation and completed before his new role. Dr Joshua D Brown reports employment from Pfizer, outside the submitted work. The authors report no other conflicts of interest in this work.

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Auteurs

Amir Sarayani (A)

Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Center for Drug Safety and Evaluation, University of Florida, Gainesville, FL, USA.

Joshua D Brown (JD)

Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Center for Drug Safety and Evaluation, University of Florida, Gainesville, FL, USA.

Christian Hampp (C)

Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA.

William T Donahoo (WT)

Division of Endocrinology, Diabetes, & Metabolism, College of Medicine, University of Florida, Gainesville, FL, USA.
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.

Almut G Winterstein (AG)

Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Center for Drug Safety and Evaluation, University of Florida, Gainesville, FL, USA.

Classifications MeSH