| Title |
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
|
|---|---|
| Published in |
Clinical Epidemiology, June 2018
|
| DOI | 10.2147/clep.s154914 |
| Pubmed ID | |
| Authors |
Elisa Martín-Merino, Amaia Calderón-Larrañaga, Samuel Hawley, Beatriz Poblador-Plou, Ana Llorente-García, Irene Petersen, Daniel Prieto-Alhambra |
| Abstract |
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. 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). 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. 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%). 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. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 32% |
| Spain | 4 | 21% |
| Belgium | 1 | 5% |
| Netherlands | 1 | 5% |
| Unknown | 7 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 58% |
| Scientists | 6 | 32% |
| Practitioners (doctors, other healthcare professionals) | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 82 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 16% |
| Student > Ph. D. Student | 11 | 13% |
| Student > Bachelor | 10 | 12% |
| Student > Master | 9 | 11% |
| Other | 3 | 4% |
| Other | 13 | 16% |
| Unknown | 23 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 23 | 28% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 10% |
| Nursing and Health Professions | 5 | 6% |
| Agricultural and Biological Sciences | 3 | 4% |
| Engineering | 3 | 4% |
| Other | 11 | 13% |
| Unknown | 29 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 July 2018.
All research outputs
#2,882,416
of 24,178,331 outputs
Outputs from Clinical Epidemiology
#119
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Outputs of similar age
#58,473
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Outputs of similar age from Clinical Epidemiology
#5
of 25 outputs
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