Scopus: 3 cites, Google Scholar: cites,
The Challenges and Opportunities of Pharmacoepidemiology in Bone Diseases
Alarkawi, Dunia (University of New South Wales)
Ali, M Sanni (University of Oxford)
Bliuc, Dana (University of New South Wales)
Center, Jacqueline R (St Vincent's Hospital)
Prieto-Alhambra, Daniel (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Universitat Autònoma de Barcelona

Data: 2018
Resum: Pharmacoepidemiology is used extensively in osteoporosis research and involves the study of the use and effects of drugs in large numbers of people. Randomized controlled trials are considered the gold standard in assessing treatment efficacy and safety. However, their results can have limited external validity when applied to day-to-day patients. Pharmacoepidemiological studies aim to assess the effect/s of treatments in actual practice conditions, but they are limited by the quality, completeness, and inherent bias due to confounding. Sources of information include prospectively collected (primary) as well as readily available routinely collected (secondary) (eg, electronic medical records, administrative/claims databases) data. Although the former enable the collection of ad hoc measurements, the latter provide a unique opportunity for the study of large representative populations and for the assessment of rare events at relatively low cost. Observational cohort and case-control studies, the most commonly implemented study designs in pharmacoepidemiology, each have their strengths and limitations. However, the choice of the study design depends on the research question that needs to be answered. Despite the many advantages of observational studies, they also have limitations. First, missing data is a common issue in routine data, frequently dealt with using multiple imputation. Second, confounding by indication arises because of the lack of randomization; multivariable regression and more specific techniques such as propensity scores (adjustment, matching, stratification, trimming, or weighting) are used to minimize such biases. In addition, immortal time bias (time period during which a subject is artefactually event-free by study design) and time-varying confounding (patient characteristics changing over time) are other types of biases usually accounted for using time-dependent modeling. Finally, residual "uncontrolled" confounding is difficult to assess, and hence to account for it, sensitivity analyses and specific methods (eg, instrumental variables) should be considered. © 2018 The Authors JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
Nota: Altres ajuts: This work was supported by the National Health Medical Research Council Australia (NHMRC project ID; DA 1114676, DB 1073430, and JRC 1008219). This work was partially supported by the NIHR Biomedical Research Centre, Oxford. DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). This article presents independent research funded by the National Institute for Health Research (NIHR). Other funding bodies were the Bupa Health Foundation (formerly MBF Foundation) and the Mrs Gibson and Ernst Heine Family Foundation. The views expressed are those of the authors and not necessarily those of the NHMRC and the NIHR.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: article ; article de revisió ; Versió publicada
Publicat a: JBMR Plus, Vol. 2 (april 2018) , p. 187-194, ISSN 2473-4039

DOI: 10.1002/jbm4.10051
PMID: 30283902

8 p, 117.4 KB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
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