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A Future for Observational Epidemiology: Clarity, Credibility, Transparency.
Sam Harper1,2
1Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Quebec.
American Journal of Epidemiology
|March 17, 2019
Summary
Epidemiology observational studies can be improved by clearly defining research questions, using bias analysis, and adopting reproducible research standards. These enhancements will increase the credibility and transparency of findings for public health decisions.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Observational studies are crucial for epidemiology but face concerns regarding credibility and research waste.
- Current evidence from observational epidemiology is often perceived as unreliable, hindering effective decision-making.
Purpose of the Study:
- To propose improvements for observational epidemiology to enhance the credibility and utility of its findings.
- To address the challenges of ambiguity and difficulty in current observational study designs.
Main Methods:
- Focusing on clearly defining inferential goals (descriptive vs. causal).
- Increasing the use of quantitative bias analysis and alternative research designs.
- Promoting reproducible research standards and replication studies.
Main Results:
- Implementing these methods can lead to clearer, more credible, and transparent observational epidemiological research.
- Enhanced research practices will reduce assumptions needed for causal effect estimation.
Conclusions:
- Improving clarity, credibility, and transparency in observational epidemiology is essential for reliable evidence.
- Reliable evidence from observational studies supports informed decisions on clinical and population-health interventions.
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