Details
| Original language | English |
|---|---|
| Pages (from-to) | 319-326 |
| Number of pages | 8 |
| Journal | Alternatives to laboratory animals |
| Volume | 42 |
| Issue number | 5 |
| Publication status | Published - 1 Nov 2014 |
Abstract
When a new in vitro assay method is introduced, it should be validated against the best available knowledge or a reference standard assay. For assays resulting in a simple binary outcome, the data can be displayed as a 2 × 2 table. Based on the estimated sensitivity and specificity, and the assumed prevalence of true positives in the population of interest, the positive and negative predictive values of the new assay can be calculated. We briefly discuss the experimental design of validation experiments and previously published methods for computing confidence intervals for predictive values. The application of the methods is illustrated for two toxicological examples, by using tools available in the free software, namely, R: confidence intervals for predictive values are computed for a validation study of an in vitro test battery, and sample size calculation is illustrated for an acute toxicity assay. The R code necessary to reproduce the results is given.
Keywords
- Confidence interval, Diagnostic test, Predictive value, Sample size
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Pharmacology, Toxicology and Pharmaceutics(all)
- Toxicology
- Health Professions(all)
- Medical Laboratory Technology
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In: Alternatives to laboratory animals, Vol. 42, No. 5, 01.11.2014, p. 319-326.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Statistical methods and software for validation studies on new in vitro toxicity assays
AU - Schaarschmidt, Frank
AU - Hothorn, Ludwig A.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - When a new in vitro assay method is introduced, it should be validated against the best available knowledge or a reference standard assay. For assays resulting in a simple binary outcome, the data can be displayed as a 2 × 2 table. Based on the estimated sensitivity and specificity, and the assumed prevalence of true positives in the population of interest, the positive and negative predictive values of the new assay can be calculated. We briefly discuss the experimental design of validation experiments and previously published methods for computing confidence intervals for predictive values. The application of the methods is illustrated for two toxicological examples, by using tools available in the free software, namely, R: confidence intervals for predictive values are computed for a validation study of an in vitro test battery, and sample size calculation is illustrated for an acute toxicity assay. The R code necessary to reproduce the results is given.
AB - When a new in vitro assay method is introduced, it should be validated against the best available knowledge or a reference standard assay. For assays resulting in a simple binary outcome, the data can be displayed as a 2 × 2 table. Based on the estimated sensitivity and specificity, and the assumed prevalence of true positives in the population of interest, the positive and negative predictive values of the new assay can be calculated. We briefly discuss the experimental design of validation experiments and previously published methods for computing confidence intervals for predictive values. The application of the methods is illustrated for two toxicological examples, by using tools available in the free software, namely, R: confidence intervals for predictive values are computed for a validation study of an in vitro test battery, and sample size calculation is illustrated for an acute toxicity assay. The R code necessary to reproduce the results is given.
KW - Confidence interval
KW - Diagnostic test
KW - Predictive value
KW - Sample size
UR - http://www.scopus.com/inward/record.url?scp=84939532766&partnerID=8YFLogxK
U2 - 10.1177/026119291404200505
DO - 10.1177/026119291404200505
M3 - Article
C2 - 25413292
AN - SCOPUS:84939532766
VL - 42
SP - 319
EP - 326
JO - Alternatives to laboratory animals
JF - Alternatives to laboratory animals
SN - 0261-1929
IS - 5
ER -