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Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review
PURPOSE: To identify pharmacoepidemiological multi‐database studies and to describe data management and data analysis techniques used for combining data. METHODS: Systematic literature searches were conducted in PubMed and Embase complemented by a manual literature search. We included pharmacoepidem...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5034829/ https://www.ncbi.nlm.nih.gov/pubmed/26175179 http://dx.doi.org/10.1002/pds.3828 |
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author | Bazelier, Marloes T. Eriksson, Irene de Vries, Frank Schmidt, Marjanka K. Raitanen, Jani Haukka, Jari Starup‐Linde, Jakob De Bruin, Marie L. Andersen, Morten |
author_facet | Bazelier, Marloes T. Eriksson, Irene de Vries, Frank Schmidt, Marjanka K. Raitanen, Jani Haukka, Jari Starup‐Linde, Jakob De Bruin, Marie L. Andersen, Morten |
author_sort | Bazelier, Marloes T. |
collection | PubMed |
description | PURPOSE: To identify pharmacoepidemiological multi‐database studies and to describe data management and data analysis techniques used for combining data. METHODS: Systematic literature searches were conducted in PubMed and Embase complemented by a manual literature search. We included pharmacoepidemiological multi‐database studies published from 2007 onwards that combined data for a pre‐planned common analysis or quantitative synthesis. Information was retrieved about study characteristics, methods used for individual‐level analyses and meta‐analyses, data management and motivations for performing the study. RESULTS: We found 3083 articles by the systematic searches and an additional 176 by the manual search. After full‐text screening of 75 articles, 22 were selected for final inclusion. The number of databases used per study ranged from 2 to 17 (median = 4.0). Most studies used a cohort design (82%) instead of a case–control design (18%). Logistic regression was most often used for individual‐level analyses (41%), followed by Cox regression (23%) and Poisson regression (14%). As meta‐analysis method, a majority of the studies combined individual patient data (73%). Six studies performed an aggregate meta‐analysis (27%), while a semi‐aggregate approach was applied in three studies (14%). Information on central programming or heterogeneity assessment was missing in approximately half of the publications. Most studies were motivated by improving power (86%). CONCLUSIONS: Pharmacoepidemiological multi‐database studies are a well‐powered strategy to address safety issues and have increased in popularity. To be able to correctly interpret the results of these studies, it is important to systematically report on database management and analysis techniques, including central programming and heterogeneity testing. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd. |
format | Online Article Text |
id | pubmed-5034829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-50348292016-10-03 Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review Bazelier, Marloes T. Eriksson, Irene de Vries, Frank Schmidt, Marjanka K. Raitanen, Jani Haukka, Jari Starup‐Linde, Jakob De Bruin, Marie L. Andersen, Morten Pharmacoepidemiol Drug Saf Review PURPOSE: To identify pharmacoepidemiological multi‐database studies and to describe data management and data analysis techniques used for combining data. METHODS: Systematic literature searches were conducted in PubMed and Embase complemented by a manual literature search. We included pharmacoepidemiological multi‐database studies published from 2007 onwards that combined data for a pre‐planned common analysis or quantitative synthesis. Information was retrieved about study characteristics, methods used for individual‐level analyses and meta‐analyses, data management and motivations for performing the study. RESULTS: We found 3083 articles by the systematic searches and an additional 176 by the manual search. After full‐text screening of 75 articles, 22 were selected for final inclusion. The number of databases used per study ranged from 2 to 17 (median = 4.0). Most studies used a cohort design (82%) instead of a case–control design (18%). Logistic regression was most often used for individual‐level analyses (41%), followed by Cox regression (23%) and Poisson regression (14%). As meta‐analysis method, a majority of the studies combined individual patient data (73%). Six studies performed an aggregate meta‐analysis (27%), while a semi‐aggregate approach was applied in three studies (14%). Information on central programming or heterogeneity assessment was missing in approximately half of the publications. Most studies were motivated by improving power (86%). CONCLUSIONS: Pharmacoepidemiological multi‐database studies are a well‐powered strategy to address safety issues and have increased in popularity. To be able to correctly interpret the results of these studies, it is important to systematically report on database management and analysis techniques, including central programming and heterogeneity testing. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd. John Wiley and Sons Inc. 2015-07-14 2015-09 /pmc/articles/PMC5034829/ /pubmed/26175179 http://dx.doi.org/10.1002/pds.3828 Text en © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Review Bazelier, Marloes T. Eriksson, Irene de Vries, Frank Schmidt, Marjanka K. Raitanen, Jani Haukka, Jari Starup‐Linde, Jakob De Bruin, Marie L. Andersen, Morten Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review |
title | Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review
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title_full | Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review
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title_fullStr | Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review
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title_full_unstemmed | Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review
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title_short | Data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review
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title_sort | data management and data analysis techniques in pharmacoepidemiological studies using a pre‐planned multi‐database approach: a systematic literature review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5034829/ https://www.ncbi.nlm.nih.gov/pubmed/26175179 http://dx.doi.org/10.1002/pds.3828 |
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