title: Data management and data analysis techniques in pharmacoepidemiological studies using a pre-planned multi-database approach: a systematic literature review.
abstract: OBJECTIVE
  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.
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Netherlands.
authors.forename: Marloes T
authors.initials: MT
authors.lastname: Bazelier
authors.affiliation: Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Irene
authors.initials: I
authors.lastname: Eriksson
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Netherlands.|Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Centre, Maastricht, Netherlands.
authors.forename: Frank
authors.initials: F
authors.lastname: de Vries
authors.affiliation: Division of Molecular Pathology, Netherlands Cancer Institute, Netherlands.
authors.forename: Marjanka K
authors.initials: MK
authors.lastname: Schmidt
authors.affiliation: School of Health Sciences, University of Tampere, Finland.|UKK Institute for Health Promotion, Tampere, Finland.
authors.forename: Jani
authors.initials: J
authors.lastname: Raitanen
authors.affiliation: University of Helsinki, Helsinki, Finland.
authors.forename: Jari
authors.initials: J
authors.lastname: Haukka
authors.affiliation: Aalborg University, Aalborg, Denmark.|Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Jakob
authors.initials: J
authors.lastname: Starup-Linde
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Netherlands.
authors.forename: Marie L
authors.initials: ML
authors.lastname: De Bruin
authors.affiliation: Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Morten
authors.initials: M
authors.lastname: Andersen
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D016208:Databases, Factual / Q000639:trends; D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods* / Q000639:trends; D013223:Statistics as Topic / Q000379:methods* / Q000639:trends
keywords: analysis techniques; data management; multi-database; pharmacoepidemiology; systematic review