Data management and data analysis techniques in pharmacoepidemiological studies using a pre-planned multi-database approach: a systematic literature review

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 - 2015/9/1

Y1 - 2015/9/1

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

SN - 1053-8569

VL - 24

SP - 897

EP - 905

JO - Pharmacoepidemiology and Drug Safety

JF - Pharmacoepidemiology and Drug Safety

IS - 9

ER -