| Title |
Confounding in observational studies based on large health care databases: problems and potential solutions – a primer for the clinician
|
|---|---|
| Published in |
Clinical Epidemiology, March 2017
|
| DOI | 10.2147/clep.s129879 |
| Pubmed ID | |
| Authors |
Mette Nørgaard, Vera Ehrenstein, Jan P Vandenbroucke |
| Abstract |
Population-based health care databases are a valuable tool for observational studies as they reflect daily medical practice for large and representative populations. A constant challenge in observational designs is, however, to rule out confounding, and the value of these databases for a given study question accordingly depends on completeness and validity of the information on confounding factors. In this article, we describe the types of potential confounding factors typically lacking in large health care databases and suggest strategies for confounding control when data on important confounders are unavailable. Using Danish health care databases as examples, we present the use of proxy measures for important confounders and the use of external adjustment. We also briefly discuss the potential value of active comparators, high-dimensional propensity scores, self-controlled designs, pseudorandomization, and the use of positive or negative controls. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 220 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 220 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 33 | 15% |
| Student > Ph. D. Student | 31 | 14% |
| Student > Master | 27 | 12% |
| Student > Bachelor | 25 | 11% |
| Other | 14 | 6% |
| Other | 21 | 10% |
| Unknown | 69 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 64 | 29% |
| Nursing and Health Professions | 22 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 4% |
| Biochemistry, Genetics and Molecular Biology | 7 | 3% |
| Social Sciences | 6 | 3% |
| Other | 31 | 14% |
| Unknown | 81 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 11 September 2021.
All research outputs
#3,737,307
of 25,584,565 outputs
Outputs from Clinical Epidemiology
#166
of 780 outputs
Outputs of similar age
#65,427
of 324,971 outputs
Outputs of similar age from Clinical Epidemiology
#5
of 16 outputs
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So far Altmetric has tracked 780 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one has done well, scoring higher than 78% of its peers.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.