Literature DB >> 30659590

Evaluation of Socioeconomic Status Indicators for Confounding Adjustment in Observational Studies of Medication Use.

Chandrasekar Gopalakrishnan1, Joshua J Gagne1, Ameet Sarpatwari1, Sara Z Dejene1, Sarah K Dutcher2, Raisa Levin1, Jessica M Franklin1, Sebastian Schneeweiss1, Rishi J Desai1.   

Abstract

Methodologic research evaluating confounding due to socioeconomic status (SES) in observational studies of medications is limited. We identified 7,109 patients who initiated brand or generic atorvastatin from Medicare claims (2011-2013) linked to electronic medical records and census data. We created a propensity score (PS) containing only claims-based covariates and augmented it with additional claims-based proxies for SES, ZIP code, and block group level SES. Cox models with PS fine-stratification and weighting were used to compare rates of a cardiovascular end point and emergency department visits. Adjustment with only claims-based variables substantially improved balance on all SES variables compared with the unadjusted. Although inclusion of SES in PS models further improved balance on SES variables compared with models with claims-based covariates only, it did not materially change point estimates for either outcome. Inclusion of claims-based proxies may mitigate confounding by SES when aggregate-level SES information is unavailable.
© 2019 The Authors Clinical Pharmacology & Therapeutics © 2019 American Society for Clinical Pharmacology and Therapeutics.

Entities:  

Year:  2019        PMID: 30659590     DOI: 10.1002/cpt.1348

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  4 in total

1.  Using Real-World Data to Predict Findings of an Ongoing Phase IV Cardiovascular Outcome Trial: Cardiovascular Safety of Linagliptin Versus Glimepiride.

Authors:  Elisabetta Patorno; Sebastian Schneeweiss; Chandrasekar Gopalakrishnan; David Martin; Jessica M Franklin
Journal:  Diabetes Care       Date:  2019-06-25       Impact factor: 19.112

2.  Trends in First-Line Glucose-Lowering Drug Use in Adults With Type 2 Diabetes in Light of Emerging Evidence for SGLT-2i and GLP-1RA.

Authors:  HoJin Shin; Sebastian Schneeweiss; Robert J Glynn; Elisabetta Patorno
Journal:  Diabetes Care       Date:  2021-06-18       Impact factor: 17.152

Review 3.  When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?

Authors:  Jessica M Franklin; Richard Platt; Nancy A Dreyer; Alex John London; Gregory E Simon; Jonathan H Watanabe; Michael Horberg; Adrian Hernandez; Robert M Califf
Journal:  Clin Pharmacol Ther       Date:  2021-05-09       Impact factor: 6.903

4.  Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.

Authors:  Rishi J Desai; Shirley V Wang; Muthiah Vaduganathan; Thomas Evers; Sebastian Schneeweiss
Journal:  JAMA Netw Open       Date:  2020-01-03
  4 in total

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