The impact of different strategies to handle missing data on both precision and bias in a drug safety study: a multidatabase multinational population-based cohort study
Received 23 October 2017
Accepted for publication 11 February 2018
Published 5 June 2018 Volume 2018:10 Pages 643—654
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Amy Norman
Peer reviewer comments 2
Editor who approved publication: Professor Vera Ehrenstein
Elisa Martín-Merino,1 Amaia Calderón-Larrañaga,2,3 Samuel Hawley,4 Beatriz Poblador-Plou,3 Ana Llorente-García,1 Irene Petersen,5,6 Daniel Prieto-Alhambra4,7
1Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria, Division of Pharmacoepidemiology and Pharmacovigilance, Spanish Agency of Medicines and Medical Devices, Madrid, Spain; 2Aging Research Center, Karolinska Institutet, Stockholm University, Stockholm, Sweden; 3EpiChron Research Group on Chronic Diseases, Aragon Health Sciences Institute, Aragon Health Research Institute, Miguel Servet University Hospital, Zaragoza, Spain; 4Centre for Statistics in Medicine, Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; 5Department of Primary Care and Population Health, University College London, London, UK; 6Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark; 7GREMPAL (Grup de Recerca en Malalties Prevalents de l’Aparell Locomotor) Research Group, Idiap Jordi Gol and CIBERFes, Instituto de Salud Carlos III, Universitat Autonoma de Barcelona, Barcelona, Spain
Background: Missing data are often an issue in electronic medical records (EMRs) research. However, there are many ways that people deal with missing data in drug safety studies.
Aim: To compare the risk estimates resulting from different strategies for the handling of missing data in the study of venous thromboembolism (VTE) risk associated with antiosteoporotic medications (AOM).
Methods: New users of AOM (alendronic acid, other bisphosphonates, strontium ranelate, selective estrogen receptor modulators, teriparatide, or denosumab) aged ≥50 years during 1998–2014 were identified in two Spanish (the Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria [BIFAP] and EpiChron cohort) and one UK (Clinical Practice Research Datalink [CPRD]) EMR. Hazard ratios (HRs) according to AOM (with alendronic acid as reference) were calculated adjusting for VTE risk factors, body mass index (that was missing in 61% of patients included in the three databases), and smoking (that was missing in 23% of patients) in the year of AOM therapy initiation. HRs and standard errors obtained using cross-sectional multiple imputation (MI) (reference method) were compared to complete case (CC) analysis – using only patients with complete data – and longitudinal MI – adding to the cross-sectional MI model the body mass index/smoking values as recorded in the year before and after therapy initiation.
Results: Overall, 422/95,057 (0.4%), 19/12,688 (0.1%), and 2,051/161,202 (1.3%) VTE cases/participants were seen in BIFAP, EpiChron, and CPRD, respectively. HRs moved from 100.00% underestimation to 40.31% overestimation in CC compared with cross-sectional MI, while longitudinal MI methods provided similar risk estimates compared with cross-sectional MI. Precision for HR improved in cross-sectional MI versus CC by up to 160.28%, while longitudinal MI improved precision (compared with cross-sectional) only minimally (up to 0.80%).
Conclusion: CC may substantially affect relative risk estimation in EMR-based drug safety studies, since missing data are not often completely at random. Little improvement was seen in these data in terms of power with the inclusion of longitudinal MI compared with cross-sectional MI. The strategy for handling missing data in drug safety studies can have a large impact on both risk estimates and precision.
Keywords: missing data, electronic medical records, pharmacoepidemiology, multiple imputation, complete case analysis, longitudinal data
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