About: Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications

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About: Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications
Creator	Berlin, Jesse Patrick, • Fife, Daniel Matcho, Amy Patel, Kayur Schuemie, Martijn Swerdel, Joel Weinstein, Rachel
Source	PMC
abstract	INTRODUCTION: Over-the-counter analgesics such as paracetamol and ibuprofen are among the most widely used, and having a good understanding of their safety profile is important to public health. Prior observational studies estimating the risks associated with paracetamol use acknowledge the inherent limitations of these studies. One threat to the validity of observational studies is channeling bias, i.e. the notion that patients are systematically exposed to one drug or the other, based on current and past comorbidities, in a manner that affects estimated relative risk. OBJECTIVES: The aim of this study was to examine whether evidence of channeling bias exists in observational studies that compare paracetamol with ibuprofen, and, if so, the extent to which confounding adjustment can mitigate this bias. STUDY DESIGN AND SETTING: In a cohort of 140,770 patients, we examined whether those who received any paracetamol (including concomitant users) were more likely to have prior diagnoses of gastrointestinal (GI) bleeding, myocardial infarction (MI), stroke, or renal disease than those who received ibuprofen alone. We compared propensity score distributions between drugs, and examined the degree to which channeling bias could be controlled using a combination of negative control disease outcome models and large-scale propensity score matching. Analyses were conducted using the Clinical Practice Research Datalink. RESULTS: The proportions of prior MI, GI bleeding, renal disease, and stroke were significantly higher in those prescribed any paracetamol versus ibuprofen alone, after adjusting for sex and age. We were not able to adequately remove selection bias using a selected set of covariates for propensity score adjustment; however, when we fit the propensity score model using a substantially larger number of covariates, evidence of residual bias was attenuated. CONCLUSIONS: Although using selected covariates for propensity score adjustment may not sufficiently reduce bias, large-scale propensity score matching offers a novel approach to consider to mitigate the effects of channeling bias. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40264-017-0581-7) contains supplementary material, which is available to authorized users.
has issue date	2017-08-05(xsd:dateTime)
bibo:doi	10.1007/s40264-017-0581-7
bibo:pmid	28780741
has license	cc-by-nc
sha1sum (hex)	1896cc019275c692d7e4eb32f1763b1f0631d169
schema:url	https://doi.org/10.1007/s40264-017-0581-7
resource representing a document's title	Channeling in the Use of Nonprescription Paracetamol and Ibuprofen in an Electronic Medical Records Database: Evidence and Implications
has PubMed Central identifier	PMC5688206
has PubMed identifier	28780741
schema:publication	Drug Saf
resource representing a document's body	covid:1896cc019275c692d7e4eb32f1763b1f0631d169#body_text
is schema:about of	named entity 'Use' named entity 'Paracetamol' named entity 'Nonprescription' named entity 'relative risk' named entity 'SMDs' named entity 'propensity score' named entity 'observational studies' named entity 'ClinicalTrials.gov' named entity 'logistic regression' named entity 'ibuprofen' named entity 'ibuprofen' named entity 'ibuprofen' named entity 'chronic renal disease' named entity 'GI bleed' named entity 'confounding' named entity '0.01' named entity 'confounding' named entity 'relative risk' named entity 'Propensity Score' named entity 'alcohol' named entity 'CPRD' named entity 'ibuprofen' named entity 'GI bleeding' named entity 'drug product' named entity 'SMDs' named entity 'negative control' named entity 'propensity score' named entity 'standard deviation' named entity 'propensity score matching' named entity 'Negative control' named entity 'clinical equipoise' named entity 'stroke' named entity 'epidemiology' named entity 'hemorrhagic stroke' named entity 'ischemic' named entity 'propensity scores' named entity 'conditional logistic regression' named entity 'null distribution' named entity 'propensity score' named entity 'ibuprofen' named entity 'SNOMED' named entity 'paracetamol' named entity 'independent variables' named entity 'covariate' named entity 'clinical equipoise' named entity 'paracetamol' named entity 'paracetamol' named entity 'cross-validation' named entity 'ibuprofen' named entity 'NCT' named entity 'standard deviation' named entity 'ibuprofen' named entity 'ibuprofen' named entity 'exchangeability' named entity 'ibuprofen' named entity 'confounding' named entity 'ad hoc' named entity 'propensity score matching' named entity 'CPRD' named entity 'statistical significance' named entity 'paracetamol' named entity 'propensity score' named entity 'covariate' named entity 'covariate' named entity 'negative control' named entity 'paracetamol' named entity 'stroke' named entity 'propensity score matching' named entity 'paracetamol' named entity 'musculoskeletal' named entity 'renal disease' named entity 'study population' named entity 'logistic regression' named entity 'diuretics'

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