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Optimal multiwave validation of secondary use data with outcome and exposure misclassification
Sarah C Lotspeich1,2, Gustavo G C Amorim2, Pamela A Shaw3,4
1Department of Statistical Sciences, Wake Forest University, Winston-Salem, 27109, North Carolina, U.S.A.
The Canadian Journal of Statistics = Revue Canadienne De Statistique
|December 4, 2024
Summary
Validating large observational databases is crucial for biomedical research. This study proposes cost-effective two-phase designs to efficiently estimate odds ratios, minimizing variance even with data errors.
Area of Science:
- Biomedical Research
- Data Science
- Epidemiology
Background:
- Observational databases offer valuable secondary data for research.
- Data errors necessitate validation, but full database validation is often resource-prohibitive.
- Efficient validation strategies are needed for reliable secondary data analysis.
Purpose of the Study:
- To develop and evaluate cost-effective two-phase designs for validating observational databases.
- To propose optimal designs for odds ratio estimation under misclassification.
- To minimize the variance of the maximum likelihood estimator in secondary data analysis.
Main Methods:
- Utilized a two-phase design for targeted database validation.
- Developed an adaptive grid search algorithm to find optimal designs.
- Employed a multiwave strategy to approximate optimal designs with unknown parameters.
- Assessed performance through simulations and analysis of two large observational studies.
Main Results:
- The proposed two-phase designs demonstrated significant efficiency gains in simulations.
- The adaptive grid search algorithm efficiently located optimal designs.
- The multiwave strategy provided a practical approximation for optimal designs.
- Validation of the methods was confirmed in two real-world observational studies.
Conclusions:
- Cost-effective two-phase designs are efficient for validating observational databases.
- The proposed methods improve odds ratio estimation accuracy under data misclassification.
- These strategies enhance the secondary use of large observational datasets in biomedical research.
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