Evidence map›Paper›PMID 27091131›Full record

ReviewInternational journal of clinical pharmacy2016

Methods to control for unmeasured confounding in pharmacoepidemiology: an overview.

Md Jamal Uddin, Rolf H H Groenwold, Mohammed Sanni Ali, Anthonius de Boer, Kit C B Roes, Muhammad A B Chowdhury, Olaf H Klungel

Abstract readReview
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In one paragraph

Review in International journal of clinical pharmacy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Md Jamal UddinDepartment of Statistics (Biostatistics and Epidemiology), Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh. jamal-sta@sust.edu.
Rolf H H GroenwoldJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Mohammed Sanni AliJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Anthonius de BoerDivision of Pharmacoepidemiology and Clinical Pharmacology, University of Utrecht, Utrecht, The Netherlands.
Kit C B RoesJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Muhammad A B ChowdhuryDepartment of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA.
Olaf H KlungelDivision of Pharmacoepidemiology and Clinical Pharmacology, University of Utrecht, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Unmeasured confounding is one of the principal problems in pharmacoepidemiologic studies. Several methods have been proposed to detect or control for unmeasured confounding either at the study design phase or the data analysis phase. Aim of the Review To provide an overview of commonly used methods to detect or control for unmeasured confounding and to provide recommendations for proper application in pharmacoepidemiology. Methods/Results Methods to control for unmeasured confounding in the design phase of a study are case only designs (e.g., case-crossover, case-time control, self-controlled case series) and the prior event rate ratio adjustment method. Methods that can be applied in the data analysis phase include, negative control method, perturbation variable method, instrumental variable methods, sensitivity analysis, and ecological analysis. A separate group of methods are those in which additional information on confounders is collected from a substudy. The latter group includes external adjustment, propensity score calibration, two-stage sampling, and multiple imputation. Conclusion As the performance and application of the methods to handle unmeasured confounding may differ across studies and across databases, we stress the importance of using both statistical evidence and substantial clinical knowledge for interpretation of the study results.

Indexed as

Research DesignHumansPharmacoepidemiologyStatistics as TopicObservational studiesPharmacoepidemiologyResidual confoundingReviewStatistical methodsUnmeasured confoundingUnobserved confounding

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.