Related Experiment Videos
Clarifying questions about "risk factors": predictors versus explanation
C Mary Schooling1,2, Heidi E Jones1
11Graduate School of Public Health and Health Policy, City University of New York, 55 West 125th St, New York, NY 10027 USA.
Emerging Themes in Epidemiology
|August 18, 2018
Summary
Reducing biomedical research waste requires distinguishing between risk prediction and explanation. Clearer research questions and methods improve efficiency and target interventions effectively, preventing disease.
Area of Science:
- Biomedical Research
- Epidemiology
- Health Sciences
Background:
- Significant effort in biomedical research is considered wasted.
- Current improvement recommendations focus on processes and procedures.
- This study proposes reducing ambiguity in research questions to minimize waste.
Purpose of the Study:
- To clarify the distinction between risk prediction and explanation.
- To provide appropriate methods and presentation for each concept.
- To reduce research waste stemming from misinterpretation.
Main Methods:
- Differentiating between prediction and explanation, often conflated under "risk factor".
- Developing distinct methodologies for predictive and explanatory studies.
- Illustrating appropriate data presentation for each study type.
Main Results:
- Risk prediction uses statistical models on representative samples to identify individuals at risk, potentially using biomarkers.
- Explanatory studies assess causal factors within a model of reality to guide interventions.
- Predictive models identify at-risk populations for targeted interventions; explanatory models identify causal factors for disease prevention.
Conclusions:
- Aligning research questions with appropriate methods and interpretation is crucial.
- Clearer distinction reduces misinterpretation and subsequent research waste.
- This approach enhances the efficiency and impact of biomedical research.