Calculable with the trimmed Spearman-Karber method, but is estimable with a parametric model. A computer program for computing the ED50 and associated. Feb 13, 2012 The Spearman-Kaerber calculation method (example), The theoretical dilution curve, The theoretical pfu/TCID50 ratio, The theoretical standard deviation of the Spearman-Kaerber calculation, The Monte Carlo simulation program (the take-out algorithm and the simulation procedure in pseudo code), 7,9 and Figure Figure3 3.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.Abstract
Trimmed nonparametric procedures such as the trimmed Spearman-Karber method have been proposed in the literature for overcoming the deficiencies of the probit and logit models in the analysis of quantal bioassay data. However, there are situations where the median effective dose (ED50) is not calculable with the trimmed Spearman-Karber method, but is estimable with a parametric model. Also, it is helpful to have a parametric model for estimating percentiles of the dose-response curve such as the ED10 and ED25. A trimmed logit method that combines the advantages of a parametric model with that of trimming in dealing with heavy-tailed distributions is presented here. These advantages are substantiated with examples of actual bioassay data. Simulation results are presented to support the validity of the trimmed logit method, which has been found to work well in our experience with over 200 data sets. A computer program for computing the ED50 and associated 95% asymptotic confidence interval, based on the trimmed logit method, can be obtained from the authors.
![Trimmed Spearman Karber Program Trimmed Spearman Karber Program](/uploads/1/2/5/8/125880948/603963471.png)
A Monte Carlo study was conducted to investigate the estimated standard errors of the estimate and the 95 percent confidence interval estimates associated with the trimmed Spearman-Karber (SK) estimators of the ED50 and the logistic model maximum likelihood estimator (MLE). The simulated binary response bioassay experiments had widely spaced doses with 5, 10, or 20 subjects per dose. For data following a logistic tolerance distribution, the trimmed SK confidence intervals were nearly as accurate as the logistic MLE intervals. For heavy-tailed tolerance distributions, the trimmed SK confidence intervals were more accurate than those based on the logistic MLE.
Keywords: binary response bioassay, maximum likelihood, logistic, standard error, confidence intervals