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Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?
Carole Forfait-Dubuc1, Sandrine Charles, Elise Billoir
1Université de Lyon, Lyon, France. carole.forfait@univ-lyon1.fr
Ecotoxicology (London, England)
|February 4, 2012
Summary
This study compares methods for estimating critical effect concentrations in ecotoxicology. The no effect concentration (NEC) model is proposed as the most suitable for environmental risk assessment due to its comprehensive data description.
Area of Science:
- Ecotoxicology
- Environmental Risk Assessment
- Chemical Safety
Background:
- Critical effect concentrations are vital for assessing species risks from contaminants.
- Commonly used metrics include lowest/no observed effect concentration (LOEC/NOEC), LC(x), and no effect concentration (NEC).
Purpose of the Study:
- To compare estimation methods for LOEC/NOEC, LC(x), and NEC.
- To identify the most appropriate method for each critical effect concentration.
- To compare the critical effect concentrations against each other for environmental risk assessment.
Main Methods:
- Analysis of nine survival data sets for D. magna exposed to various contaminants over time.
- Evaluation of different statistical methods for LOEC/NOEC estimation (e.g., Cochran-Armitage test).
- Comparison of concentration-response models (e.g., Weibull) for LC50 estimation.
- Application of a time- and concentration-dependent threshold model for NEC estimation.
Main Results:
- LOEC/NOEC values at day 21 were method-dependent; the Cochran-Armitage test with step-down procedure was most protective.
- Weibull model showed the lowest global mean deviance among tested concentration-response models for LC50 at day 21.
- A threshold NEC model accurately described time-course data and precisely estimated NEC.
Conclusions:
- The Cochran-Armitage test with step-down procedure is recommended for protective LOEC/NOEC estimation.
- The Weibull model is a suitable choice for LC50 estimation.
- The NEC model provides a comprehensive approach for environmental risk assessment, outperforming LOEC/NOEC and LC(x).
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