Making fair comparisons in pregnancy medication safety studies: An overview of advanced methods for confounding control

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Abstract

Understanding the safety of medication use during pregnancy relies on observational studies: However, confounding in observational studies poses a threat to the validity of estimates obtained from observational data. Newer methods, such as marginal structural models and propensity calibration, have emerged to deal with complex confounding problems, but these methods have seen limited uptake in the pregnancy medication literature. In this article, we provide an overview of newer advanced methods for confounding control and show how these methods are relevant for pregnancy medication safety studies.

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Most cited references 36

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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

Sebastian Schneeweiss, Jeremy A Rassen, Robert J Glynn … (2009)

Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared with nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91-1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94 [0.78-1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88 [0.73-1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02 [0.85-1.21]). In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.

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Identification of Causal Effects Using Instrumental Variables

Joshua Angrist, Guido Imbens, Donald B Rubin (1996)

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Medication use during pregnancy, with particular focus on prescription drugs: 1976-2008.

Allen A Mitchell, Suzanne M. Gilboa, Martha M. Werler … (2011)

The objective of the study was to provide information on overall medication use throughout pregnancy, with particular focus on the first trimester and specific prescription medications. The study design included the Slone Epidemiology Center Birth Defects Study, 1976-2008, and the National Birth Defects Prevention Study, 1997-2003, which together interviewed more than 30,000 women about their antenatal medication use. Over the last 3 decades, first-trimester use of prescription medication increased by more than 60%, and the use of 4 or more medications more than tripled. By 2008, approximately 50% of women reported taking at least 1 medication. Use of some specific medications markedly decreased or increased. Prescription medication use increased with maternal age and education, was highest for non-Hispanic whites, and varied by state. These data reflect the widespread and growing use of medications by pregnant women and reinforce the need to study their respective fetal risks and safety. Copyright © 2011 Mosby, Inc. All rights reserved.

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Author and article information

Contributors

Mollie E. Wood:

ORCID: http://orcid.org/0000-0002-9302-2641

mollie.wood@gmail.com

Journal

Journal ID (nlm-ta): Pharmacoepidemiol Drug Saf

Journal ID (iso-abbrev): Pharmacoepidemiol Drug Saf

Journal ID (doi): 10.1002/(ISSN)1099-1557

Journal ID (publisher-id): PDS

Title: Pharmacoepidemiology and Drug Safety

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 1053-8569

ISSN (Electronic): 1099-1557

Publication date (Electronic): 17 October 2017

Publication date (Print): February 2018

Volume: 27

Issue: 2 ( doiID: 10.1002/pds.v27.2 )

Pages: 140-147

Affiliations

[ ¹ ] PharmacoEpidemiology and Drug Safety Research Group, School of Pharmacy University of Oslo Norway

[ ² ] Department of Quantitative Health Sciences University of Massachusetts Medical School Worcester MA USA

[ ³ ] Department for Health Evidence, Radboud Institute for Health Sciences Radboud University Medical Center Nijmegen The Netherlands

[ ⁴ ] Radboud REshape Innovation Center Radboud University Medical Center Nijmegen The Netherlands

[ ⁵ ] School of Social and Community Medicine University of Bristol UK

[ ⁶ ] Department of Child Mental and Physical Health Norwegian Institute of Public Health Oslo Norway

Author notes

[*] [* ] Correspondence

M. E. Wood, PharmacoEpidemiology and Drug Safety Research Group, School of Pharmacy, University of Oslo, Norway.

Email: mollie.wood@ 123456gmail.com

Author information

Mollie E. Wood http://orcid.org/0000-0002-9302-2641

Article

Publisher ID: PDS4336 Other ID: PDS-17-0079.R2

DOI: 10.1002/pds.4336

PMC ID: 6646901

PubMed ID: 29044735

SO-VID: ea8d844f-bcda-46f2-9e59-94670dc7e89e

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date received : 24 February 2017

Date revision received : 29 August 2017

Date accepted : 18 September 2017

Page count

Figures: 2, Tables: 1, Pages: 8, Words: 3315

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source-schema-version-number 2.0

component-id pds4336

cover-date February 2018

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.6.2 mode:remove_FC converted:23.07.2019

ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

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ScienceOpen disciplines: Pharmacology & Pharmaceutical medicine

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Making fair comparisons in pregnancy medication safety studies: An overview of advanced methods for confounding control

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Most cited references 36

High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

Identification of Causal Effects Using Instrumental Variables

Medication use during pregnancy, with particular focus on prescription drugs: 1976-2008.

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