Data management and data analysis techniques in pharmacoepidemiological studies using a pre-planned multi-database approach: a systematic literature review
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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.
In: Pharmacoepidemiology and Drug Safety, Vol. 24, No. 9, 09.2015, p. 897-905.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Data management and data analysis techniques in pharmacoepidemiological studies using a pre-planned multi-database approach
T2 - a systematic literature review
AU - Bazelier, Marloes T
AU - Eriksson, Irene
AU - de Vries, Frank
AU - Schmidt, Marjanka K
AU - Raitanen, Jani
AU - Haukka, Jari
AU - Starup-Linde, Jakob
AU - De Bruin, Marie L
AU - Andersen, Morten
N1 - © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.
PY - 2015/9
Y1 - 2015/9
N2 - 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.
AB - 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.
KW - Case-Control Studies
KW - Cohort Studies
KW - Databases, Factual
KW - Humans
KW - Pharmacoepidemiology
KW - Statistics as Topic
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
KW - Review
U2 - 10.1002/pds.3828
DO - 10.1002/pds.3828
M3 - Journal article
C2 - 26175179
VL - 24
SP - 897
EP - 905
JO - Pharmacoepidemiology and Drug Safety
JF - Pharmacoepidemiology and Drug Safety
SN - 1053-8569
IS - 9
ER -
ID: 164618025