Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

Forfait-Dubuc, Carole; Charles, Sandrine; Billoir, Élise; Delignette-Muller, Marie Laure

doi:10.1007/s10646-012-0860-0

Cited by 26 publications

(30 citation statements)

References 31 publications

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“…In fact, the MLE NEC overestimated the apparent threshold in steep slopes with a mean of around 23% of the effluent in both designs. Overestimation and underestimation of the threshold value in relation to the EC5 may occur and were also observed by Forfait-Dubuc [8] with real datasets.…”

Section: Plos Onesupporting

confidence: 54%

“…The Bayesian approach also has a series of advantages such as the direct inclusion of uncertainty in the estimates of the threshold parameter, which can be draw from the posterior distribution. Another advantage is that prior information can also be adjusted by using expert elicitation (e.g., [8]), information from the literature, or previous experiments. For instance, information about background mortality can be easily gathered for commonly used species in ecotoxicology.…”

Section: Plos Onementioning

confidence: 99%

“…Both analysis of variance and GLM remain as the most used approaches in the field. However, serious criticisms have been made of both metrics (e.g., [5]) and ecotoxicologists are now suggesting that thresholds estimates are more ecological relevant meaningful and more useful in risk assessment [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Comparing statistical analyses to estimate thresholds in ecotoxicology

Krull

2020

PLoS ONE

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Different methods are used in ecotoxicology to estimate thresholds in survival data. This paper uses Monte Carlo simulations to evaluate the accuracy of three methods (maximum likelihood (MLE) and Markov Chain Monte Carlo estimates (Bayesian) of the no-effect concentration (NEC) model and Piecewise regression) in estimating true and apparent thresholds in survival experiments with datasets having different slopes, background mortalities, and experimental designs. Datasets were generated with models that include a threshold parameter (NEC) or not (log-logistic). Accuracy was estimated using root-mean square errors (RMSEs), and RMSE ratios were used to estimate the relative improvement in accuracy by each design and method. All methods had poor performances in shallow and intermediate curves, and accuracy increased with the slope of the curve. The EC5 was generally the most accurate method to estimate true and apparent thresholds, except for steep curves with a true threshold. In that case, the EC5 underestimated the threshold, and MLE and Bayesian estimates were more accurate. In most cases, information criteria weights did not provide strong evidence in support of the true model, suggesting that identifying the true model is a difficult task. Piecewise regression was the only method where the information criteria weights had high support for the threshold model; however, the rate of spurious threshold model selection was also high. Even though thresholds are an attractive concept from a regulatory and practical point of view, threshold estimates, under the experimental conditions evaluated in this work, should be carefully used in survival analysis or when there are any biological reasons to support the existence of a threshold.

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Section: Plos Onesupporting

confidence: 54%

Section: Plos Onementioning

confidence: 99%

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Comparing statistical analyses to estimate thresholds in ecotoxicology

Krull

2020

PLoS ONE

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“…Such approaches rarely have been employed, although a few applications that used a linear mixed model (i.e., using a normal error distribution) to investigate the metal interactions are available . This is rather surprising because the GLM and GLMM have been introduced into ecotoxicology , and more sophisticated applications such as the use of Bayesian inference have been available. Compared with conventional approaches such as those based on the concentration addition approach, the major advantages are 2‐fold: first, binary response variables such as survival can be modeled without any transformations, and thus the approaches can take sample size into consideration (i.e., using raw binary data instead of aggregated proportions); and second, the importance of the interaction can be statistically tested in an intuitive manner.…”

Section: Resultsmentioning

confidence: 99%

Application of a generalized linear mixed model to analyze mixture toxicity: Survival of brown trout affected by copper and zinc

Iwasaki

Brinkman

2015

Enviro Toxic and Chemistry

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Increased concerns about the toxicity of chemical mixtures have led to greater emphasis on analyzing the interactions among the mixture components based on observed effects. The authors applied a generalized linear mixed model (GLMM) to analyze survival of brown trout (Salmo trutta) acutely exposed to metal mixtures that contained copper and zinc. Compared with dominant conventional approaches based on an assumption of concentration addition and the concentration of a chemical that causes x% effect (ECx), the GLMM approach has 2 major advantages. First, binary response variables such as survival can be modeled without any transformations, and thus sample size can be taken into consideration. Second, the importance of the chemical interaction can be tested in a simple statistical manner. Through this application, the authors investigated whether the estimated concentration of the 2 metals binding to humic acid, which is assumed to be a proxy of nonspecific biotic ligand sites, provided a better prediction of survival effects than dissolved and free-ion concentrations of metals. The results suggest that the estimated concentration of metals binding to humic acid is a better predictor of survival effects, and thus the metal competition at the ligands could be an important mechanism responsible for effects of metal mixtures. Application of the GLMM (and the generalized linear model) presents an alternative or complementary approach to analyzing mixture toxicity.

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“…Jager et al 19 used a multinomial formulation of the error model on the number of survivors. The multinomial and the conditional binomial error models 33 are two mathematically equivalent formulations of the same error model for survival without lost to follow-up organisms (section S5 of the Supporting Information). The presence of lost to follow-up organisms precludes the use of the multinomial model formulation and suggests instead modeling the number of survivors at time conditionally to the number of organisms alive at time t k−1 and that have not disappeared.…”

Section: Methodsmentioning

confidence: 99%

Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model

King

Delignette-Muller

Kefford

et al. 2015

Environ. Sci. Technol.

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Classical species sensitivity distribution (SSD) is used to assess the threat to ecological communities posed by a contaminant and derive a safe concentration. It suffers from several well-documented weaknesses regarding its ecological realism and statistical soundness. Criticism includes that SSD does not take time-dependence of the data into account, that safe concentrations obtained from SSD might not be entirely protective of the target communities, and that there are issues of statistical representativity and of uncertainty propagation from the experimental data. We present a hierarchical toxico-dynamic (TD) model to simultaneously address these weaknesses: TD models incorporate time-dependence and allow improvement of the ecological relevance of safe concentrations, while the hierarchical approach affords appropriate propagation of uncertainty from the original data. We develop this model on a published data set containing the salinity tolerance over 72 h of 217 macroinvertebrate taxa, obtained through rapid toxicity testing (RTT). The shrinkage properties of the hierarchical model prove particularly adequate for modeling inhomogeneous RTT data. Taking into account the large variability in the species response, the model fits the whole data set well. Moreover, the model predicts a time-independent safe concentration below that obtained with classical SSD at 72 h, demonstrating under-protectiveness of the classical approach.

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Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

Cited by 26 publications

References 31 publications

Comparing statistical analyses to estimate thresholds in ecotoxicology

Comparing statistical analyses to estimate thresholds in ecotoxicology

Application of a generalized linear mixed model to analyze mixture toxicity: Survival of brown trout affected by copper and zinc

Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model

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