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      New‐user and prevalent‐user designs and the definition of study time origin in pharmacoepidemiology: A review of reporting practices

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          Abstract

          Background

          Guidance reports for observational comparative effectiveness and drug safety research recommend implementing a new‐user design whenever possible, since it reduces the risk of selection bias in exposure effect estimation compared to a prevalent‐user design. The uptake of this guidance has not been studied extensively.

          Methods

          We reviewed 89 observational effectiveness and safety cohort studies published in six pharmacoepidemiological journals in 2018 and 2019. We developed an extraction tool to assess how frequently new‐user and prevalent‐user designs were reported to be implemented. For studies that implemented a new‐user design in both treatment arms, we extracted information about the extent to which the moment of meeting eligibility criteria, treatment initiation, and start of follow‐up were reported to be aligned.

          Results

          Of the 89 studies included, 40% reported implementing a new‐user design for both the study exposure arm and the comparator arm, while 13% reported implementing a prevalent‐user design in both arms. The moment of meeting eligibility criteria, treatment initiation, and start of follow‐up were reported to be aligned in both treatment arms in 53% of studies that reported implementing a new‐user design. We provided examples of studies that minimized the risk of introducing bias due to unclear definition of time origin in unexposed participants, immortal time, or a time lag.

          Conclusions

          Almost half of the included studies reported implementing a new‐user design. Implications of misalignment of study design origin were difficult to assess because it would require explicit reporting of the target estimand in original studies. We recommend that the choice for a particular study time origin is explicitly motivated to enable assessment of validity of the study.

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          Most cited references38

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          ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

          Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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            A Coefficient of Agreement for Nominal Scales

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              Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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                Author and article information

                Contributors
                k.luijken@lumc.nl
                Journal
                Pharmacoepidemiol Drug Saf
                Pharmacoepidemiol Drug Saf
                10.1002/(ISSN)1099-1557
                PDS
                Pharmacoepidemiology and Drug Safety
                John Wiley & Sons, Inc. (Chichester, UK )
                1053-8569
                1099-1557
                10 May 2021
                July 2021
                : 30
                : 7 ( doiID: 10.1002/pds.v30.7 )
                : 960-974
                Affiliations
                [ 1 ] Department of Clinical Epidemiology Leiden University Medical Center Leiden The Netherlands
                [ 2 ] Department of Paediatrics Diakonessenhuis Utrecht The Netherlands
                [ 3 ] Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht Utrecht University Utrecht The Netherlands
                [ 4 ] Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences Utrecht University Utrecht The Netherlands
                [ 5 ] Department of Clinical Pharmacy, Division Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht Utrecht University Utrecht The Netherlands
                [ 6 ] Faculty of Pharmaceutical Sciences University of Iceland Reykjavik Iceland
                [ 7 ] Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands
                Author notes
                [*] [* ] Correspondence

                Kim Luijken, Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

                Email: k.luijken@ 123456lumc.nl

                Author information
                https://orcid.org/0000-0001-5192-8368
                Article
                PDS5258
                10.1002/pds.5258
                8252086
                33899305
                3854cc98-ed37-40b2-9d5b-c13b5b28e5c7
                © 2021 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.

                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
                : 01 April 2021
                : 26 January 2021
                : 20 April 2021
                Page count
                Figures: 3, Tables: 4, Pages: 15, Words: 9631
                Funding
                Funded by: Leids Universitair Medisch Centrum , open-funder-registry 10.13039/501100005039;
                Funded by: ZonMw , open-funder-registry 10.13039/501100001826;
                Categories
                Review
                Review
                Custom metadata
                2.0
                July 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.4 mode:remove_FC converted:02.07.2021

                Pharmacology & Pharmaceutical medicine
                causal inference,new‐user design,pharmacoepidemiology,prevalent‐user design

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