ArticleClinical epidemiology2018
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.
Article in Clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- A systematic review of how missing data are handled and reported in multi-database pharmacoepidemiologic studies.Pharmacoepidemiology and drug safety · 2021Pooled it
- A real-world pharmacovigilance study of romidepsin based on FDA adverse event reporting system database.Frontiers in oncology · 2026Article
- Atrial fibrillation development in the heart failure population from nationwide British linked electronic health records.ESC heart failure · 2025Article
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- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- Methodological issues of the electronic health records' use in the context of epidemiological investigations, in light of missing data: a review of the recent literature.BMC medical research methodology · 2023Review
- The Risk of SARS-CoV-2 Infection in Pregnant Women: An Observational Cohort Study Using the BIFAP Database.Healthcare (Basel, Switzerland) · 2022Article
- The role of prenatal exposure to antidepressants, anxiolytic, and hypnotics and its underlying illness on the risk of miscarriage using BIFAP database.Pharmacoepidemiology and drug safety · 2022Article
- Completeness and representativeness of small area socioeconomic data linked with the UK Clinical Practice Research Datalink (CPRD).Journal of epidemiology and community health · 2022Article
- Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.Drug safety · 2022Article
- A multi-step approach to managing missing data in time and patient variant electronic health records.BMC research notes · 2022Article
- Adjusting for selection bias due to missing data in electronic health records-based research.Statistical methods in medical research · 2021Article
- Evaluation of the Risk of Inflammatory Bowel Disease after the HPV Vaccination in Primary Care in Spain: A Time-Varying Cohort Analysis of Around 390,000 Girls.Drug safety · 2021Article
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Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundMissing 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.
aimTo 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).
methodsNew 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.
resultsOverall, 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%).
conclusionCC 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.
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