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      Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

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          Abstract

          Background

          The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.

          Methods

          Scoping review of the scientific literature published during the period 2000–2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.

          Results

          A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and “immortal time” bias (25.6%) were the subcategories most frequently mentioned.

          Conclusions

          Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.

          Electronic supplementary material

          The online version of this article (10.1186/s12874-019-0695-y) contains supplementary material, which is available to authorized users.

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          Most cited references132

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          Guidance for conducting systematic scoping reviews.

          Reviews of primary research are becoming more common as evidence-based practice gains recognition as the benchmark for care, and the number of, and access to, primary research sources has grown. One of the newer review types is the 'scoping review'. In general, scoping reviews are commonly used for 'reconnaissance' - to clarify working definitions and conceptual boundaries of a topic or field. Scoping reviews are therefore particularly useful when a body of literature has not yet been comprehensively reviewed, or exhibits a complex or heterogeneous nature not amenable to a more precise systematic review of the evidence. While scoping reviews may be conducted to determine the value and probable scope of a full systematic review, they may also be undertaken as exercises in and of themselves to summarize and disseminate research findings, to identify research gaps, and to make recommendations for the future research. This article briefly introduces the reader to scoping reviews, how they are different to systematic reviews, and why they might be conducted. The methodology and guidance for the conduct of systematic scoping reviews outlined below was developed by members of the Joanna Briggs Institute and members of five Joanna Briggs Collaborating Centres.
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            A structural approach to selection bias.

            The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or a cause of the outcome. This structure is shared by other biases (eg, adjustment for variables affected by prior exposure). A structural classification of bias distinguishes between biases resulting from conditioning on common effects ("selection bias") and those resulting from the existence of common causes of exposure and outcome ("confounding"). This classification also leads to a unified approach to adjust for selection bias.
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              The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study.

              Measurement error in explanatory variables and unmeasured confounders can cause considerable problems in epidemiologic studies. It is well recognized that under certain conditions, nondifferential measurement error in the exposure variable produces bias towards the null. Measurement error in confounders will lead to residual confounding, but this is not a straightforward issue, and it is not clear in which direction the bias will point. Unmeasured confounders further complicate matters. There has been discussion about the amount of bias in exposure effect estimates that can plausibly occur due to residual or unmeasured confounding. In this paper, the authors use simulation studies and logistic regression analyses to investigate the size of the apparent exposure-outcome association that can occur when in truth the exposure has no causal effect on the outcome. The authors consider two cases with a normally distributed exposure and either two or four normally distributed confounders. When the confounders are uncorrelated, bias in the exposure effect estimate increases as the amount of residual and unmeasured confounding increases. Patterns are more complex for correlated confounders. With plausible assumptions, effect sizes of the magnitude frequently reported in observational epidemiologic studies can be generated by residual and/or unmeasured confounding alone.
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                Author and article information

                Contributors
                guillermoj.prada@rai.usc.es
                bahi.takkouche@usc.es
                (+34) 981 95 11 92 , adolfo.figueiras@usc.es
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                11 March 2019
                11 March 2019
                2019
                : 19
                : 53
                Affiliations
                [1 ]ISNI 0000000109410645, GRID grid.11794.3a, Department of Preventive Medicine and Public Health, , University of Santiago de Compostela, ; c/ San Francisco s/n, 15786 Santiago de Compostela, A Coruña Spain
                [2 ]ISNI 0000 0004 0408 4897, GRID grid.488911.d, Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela - IDIS), Clinical University Hospital of Santiago de Compostela, ; 15706 Santiago de Compostela, Spain
                [3 ]Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública – CIBERESP), Santiago de Compostela, Spain
                Author information
                http://orcid.org/0000-0002-5766-8672
                Article
                695
                10.1186/s12874-019-0695-y
                6419460
                30871502
                6028bbbf-2d2d-4388-b951-8d69efa6b5cc
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 28 April 2018
                : 26 February 2019
                Funding
                Funded by: Consellería de Educación, Universidad y Formación Profesional, Xunta de Galicia (ES)
                Award ID: Grant ED431C 2018/20
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Medicine
                pharmacoepidemiology,observational studies,bias,confounding factors,medical records,electronic health records,administrative claims,medical record linkage

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