Literature DB >> 35019190

Core concepts in pharmacoepidemiology: Confounding by indication and the role of active comparators.

Rachel Sendor1, Til Stürmer1.   

Abstract

Confounding by indication poses a significant threat to the validity of nonexperimental studies assessing effectiveness and safety of medical interventions. While no different from other forms of confounding in theory, confounding by indication often requires specific methods to address the bias it creates in addition to common epidemiological adjustment or restriction methods. Clinical indication influencing treatment prescription is patient-specific and complex, making it challenging to measure within nonexperimental research. Restriction of the study population to patients with the indication for treatment would effectively mitigate confounding by indication and bring about comparability between exposure and comparator populations with respect to probability of the exposure. Active comparators are often an effective practical solution to restrict the study population in this manner when indication cannot be measured accurately. This article discusses various forms of confounding by indication, the utility of active comparators for nonexperimental studies of treatment effects, and the active comparator, new user (ACNU) study design to implicitly condition on indication. Considerations for selecting active comparators and conducting an ACNU study design are discussed to enable increased adoption of these methods, improve quality of nonexperimental studies, and ultimately strengthen our evidence base for intended and unintended treatment effects in relevant target populations.
© 2022 John Wiley & Sons Ltd.

Entities:  

Keywords:  bias; confounding; epidemiology; nonexperimental studies; pharmacoepidemiology; study design; therapy

Mesh:

Year:  2022        PMID: 35019190      PMCID: PMC9121653          DOI: 10.1002/pds.5407

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.732


  30 in total

Review 1.  A review of uses of health care utilization databases for epidemiologic research on therapeutics.

Authors:  Sebastian Schneeweiss; Jerry Avorn
Journal:  J Clin Epidemiol       Date:  2005-04       Impact factor: 6.437

2.  Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results.

Authors:  Sebastian Schneeweiss; Amanda R Patrick; Til Stürmer; M Alan Brookhart; Jerry Avorn; Malcolm Maclure; Kenneth J Rothman; Robert J Glynn
Journal:  Med Care       Date:  2007-10       Impact factor: 2.983

3.  Effects of socioeconomic and racial residential segregation on preterm birth: a cautionary tale of structural confounding.

Authors:  Lynne C Messer; J Michael Oakes; Susan Mason
Journal:  Am J Epidemiol       Date:  2010-02-05       Impact factor: 4.897

4.  Confounding by Indication in Clinical Research.

Authors:  Demetrios N Kyriacou; Roger J Lewis
Journal:  JAMA       Date:  2016-11-01       Impact factor: 56.272

5.  Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

Authors:  Miguel A Hernán; James M Robins
Journal:  Am J Epidemiol       Date:  2016-03-18       Impact factor: 4.897

6.  Confounding by indication: an example of variation in the use of epidemiologic terminology.

Authors:  M Salas; A Hofman; B H Stricker
Journal:  Am J Epidemiol       Date:  1999-06-01       Impact factor: 4.897

7.  Comparison of alternative approaches to trim subjects in the tails of the propensity score distribution.

Authors:  Robert J Glynn; Mark Lunt; Kenneth J Rothman; Charles Poole; Sebastian Schneeweiss; Til Stürmer
Journal:  Pharmacoepidemiol Drug Saf       Date:  2019-08-05       Impact factor: 2.890

8.  Prescribed fenoterol and death from asthma in New Zealand, 1981-83: case-control study.

Authors:  J Crane; N Pearce; A Flatt; C Burgess; R Jackson; T Kwong; M Ball; R Beasley
Journal:  Lancet       Date:  1989-04-29       Impact factor: 79.321

9.  Evaluating medication effects outside of clinical trials: new-user designs.

Authors:  Wayne A Ray
Journal:  Am J Epidemiol       Date:  2003-11-01       Impact factor: 4.897

10.  The importance and implications of comparator selection in pharmacoepidemiologic research.

Authors:  Monica D'Arcy; Til Stürmer; Jennifer L Lund
Journal:  Curr Epidemiol Rep       Date:  2018-07-06
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