Open AccessHow to account for the uncertainty from standard toxicity tests in species sensitivity distributions: An example in non-target plants
Author(s) -
Sandrine Charles,
Dan Wu,
Virginie Ducrot
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0245071
Subject(s) - censoring (clinical trials) , statistics , frequentist inference , point estimation , mathematics , bayesian probability , confidence interval , hazard ratio , credible interval , sensitivity (control systems) , econometrics , bayesian inference , engineering , electronic engineering
This research proposes new perspectives accounting for the uncertainty on 50% effective rates ( ER 50 ) as interval input for species sensitivity distribution (SSD) analyses and evaluating how to include this uncertainty may influence the 5% Hazard Rate ( HR 5 ) estimation. We explored various endpoints (survival, emergence, shoot-dry-weight) for non-target plants from seven standard greenhouse studies that used different experimental approaches (vegetative vigour vs. seedling emergence) and applied seven herbicides at different growth stages. Firstly, for each endpoint of each study, a three-parameter log-logistic model was fitted to experimental toxicity test data for each species under a Bayesian framework to get a posterior probability distribution for ER 50 . Then, in order to account for the uncertainty on the ER 50 , we explored two censoring criteria to automatically censor ER 50 taking the ER 50 probability distribution and the range of tested rates into account. Secondly, based on dose-response fitting results and censoring criteria, we considered input ER 50 values for SSD analyses in three ways (only point estimates chosen as ER 50 medians, interval-censored ER 50 based on their 95% credible interval and censored ER 50 according to one of the two criteria), by fitting a log-normal distribution under a frequentist framework to get the three corresponding HR 5 estimates. We observed that SSD fitted reasonably well when there were at least six distinct intervals for the ER 50 values. By comparing the three SSD curves and the three HR 5 estimates, we shed new light on the fact that both propagating the uncertainty from the ER 50 estimates and including censored data into SSD analyses often leads to smaller point estimates of HR 5 , which is more conservative in a risk assessment context. In addition, we recommend not to focus solely on the point estimate of the HR 5 , but also to look at the precision of this estimate as depicted by its 95% confidence interval.