Formulas and Web Application for Designing a Biospecimen Pooling Study to Compare Group Means
| Autor: | Dane R. Van Domelen, Robert H. Lyles |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Observational error Epidemiology Computer science business.industry Multiplicative function Pooling Variance (accounting) 01 natural sciences Specimen Handling 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Research Design Sample size determination Humans Web application 030212 general & internal medicine Sensitivity (control systems) 0101 mathematics business Biomarkers Software Central limit theorem |
| Zdroj: | Epidemiology. 31:98-102 |
| ISSN: | 1044-3983 |
| DOI: | 10.1097/ede.0000000000001104 |
| Popis: | BACKGROUND When research focuses on biomarker assessment in settings where per-assay costs are high relative to per-subject costs, a biospecimen pooling study design can be extremely cost-effective. However, designing a study to maximize cost savings is complicated by the fact that pooled measurements are typically subject to processing error, inducing additional variability caused by combining biospecimens, and may also be affected by assay-related measurement error. METHODS We provide formulas and an interactive web application (hereafter called app) for designing a pooling study to compare group means. Power and sample size formulas are justified by Central Limit Theorem arguments that make no distributional assumptions on the biomarker. Errors can be assumed mean-0 additive or mean-1 multiplicative, the latter being well-suited for skewed biomarkers. RESULTS User inputs for the app include usual power parameters as well as per-assay and per-subject costs and information about the errors: which are present, whether they are additive or multiplicative, and their variances. The app generates plots revealing the optimal pool size, required number of assays, cost savings, and sensitivity to the hard-to-predict processing error variance. CONCLUSIONS These tools should aid in the design and deployment of pooling studies powered to detect group mean differences while minimizing total study costs. |
| Databáze: | OpenAIRE |
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