Literature DB >> 18215000

Supplementary data collection with case-cohort analysis to address potential confounding in a cohort study of thromboembolism in oral contraceptive initiators matched on claims-based propensity scores.

P Mona Eng1, John D Seeger, Jeanne Loughlin, C Robin Clifford, Sherry Mentor, Alexander M Walker.   

Abstract

PURPOSE: Residual confounding is a potential limitation of pharmacoepidemiologic studies, and in particular, studies based on administrative claims data that do not capture lifestyle and clinical confounders. We describe an application of the case-cohort design to assess residual confounding by thromboembolic risk factors (e.g., smoking and obesity) not captured in claims data in a claims-based cohort study of thromboembolism among matched oral contraceptive (OC) initiators.
METHODS: This study was conducted using the Ingenix Research Data Mart, a database containing medical claims for approximately 12 million members of a large health plan of the United States. We randomly sampled 701 OC initiators from cohorts of ethinyl estradiol/drospirenone (n = 22,429) and other OC initiators (n = 44,858) identified in the years 2001-2004 and matched by propensity score in a claims-based cohort study. Supplementary data on risk factors not measured in the cohort study were collected from medical records for the sample. We estimated the risk ratio of thromboembolism adjusted for the supplementary variables using Cox regression modified for a case-cohort design, and compared it to the rate ratio from the cohort study.
RESULTS: The risk ratio adjusted for the supplementary variables was 0.90 (95 per cent (%) confidence interval (CI): 0.49, 1.68) which was similar to the rate ratio (0.92; 95%CI: 0.50, 1.63), indicating negligible confounding by the supplementary variables in the cohort study.
CONCLUSIONS: Case-cohort methods were used to assess residual confounding in a claims-based cohort study. This approach adds to a growing number of methods to evaluate residual confounding in cohort studies. Copyright 2008 John Wiley & Sons, Ltd.

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Year:  2008        PMID: 18215000     DOI: 10.1002/pds.1554

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  6 in total

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Authors:  Elisabetta Patorno; Chandrasekar Gopalakrishnan; Jessica M Franklin; Kimberly G Brodovicz; Elvira Masso-Gonzalez; Dorothee B Bartels; Jun Liu; Sebastian Schneeweiss
Journal:  Diabetes Obes Metab       Date:  2018-01-12       Impact factor: 6.577

2.  Claims Data Studies of Direct Oral Anticoagulants Can Achieve Balance in Important Clinical Parameters Only Observable in Electronic Health Records.

Authors:  Krista F Huybrechts; Chandrasekar Gopalakrishnan; Jessica M Franklin; Kristina Zint; Lionel Riou Franca; Dorothee B Bartels; Joan Landon; Sebastian Schneeweiss
Journal:  Clin Pharmacol Ther       Date:  2018-11-11       Impact factor: 6.875

Review 3.  Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects.

Authors:  Sebastian Schneeweiss
Journal:  Clin Epidemiol       Date:  2018-07-06       Impact factor: 4.790

4.  Post-approval Safety Surveillance Study of Golimumab in the Treatment of Rheumatic Disease Using a United States Healthcare Claims Database.

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Journal:  Clin Drug Investig       Date:  2020-11       Impact factor: 2.859

5.  Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.

Authors:  Sebastian Schneeweiss; Elisabetta Patorno
Journal:  Endocr Rev       Date:  2021-09-28       Impact factor: 19.871

6.  Risk of Thyroid Cancer Associated with Use of Liraglutide and Other Antidiabetic Drugs in a US Commercially Insured Population.

Authors:  Donnie Funch; Kathleen Mortimer; Najat J Ziyadeh; John D Seeger; Li Zhou; Eva Ng; Douglas Ross; Atheline Major-Pedersen; Heidrun Bosch-Traberg; Helge Gydesen; David D Dore
Journal:  Diabetes Metab Syndr Obes       Date:  2021-06-10       Impact factor: 3.168

  6 in total

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