A model-based solution for observational errors in laboratory studies
| Autor: | Brittany A. Mosher, Kristen A. Davenport, Brian M. Brost |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Inference Biology Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences 010104 statistics & probability Bias Covariate Genetics Sensitivity (control systems) 0101 mathematics Set (psychology) Ecology Evolution Behavior and Systematics business.industry nutritional and metabolic diseases Sampling (statistics) Experimental data Decision rule Models Theoretical Scientific Experimental Error Chytridiomycota Artificial intelligence business computer Biotechnology |
| Zdroj: | Molecular Ecology Resources. 18:580-589 |
| ISSN: | 1755-098X |
| DOI: | 10.1111/1755-0998.12765 |
| Popis: | Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false-negative and false-positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false-negative errors and more recently have even been used to address false-positive errors, too. Current modelling options assume false-positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false-positive test result. We present an extension to the occupancy modelling framework that allows false-positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model-based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences. |
| Databáze: | OpenAIRE |
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