aconiazide and Weight-Gain

Methods of quantitative risk assessment for toxic responses that are measured on a continuous scale are not well established. Although risk-assessment procedures that attempt to utilize the quantitative information in such data have been proposed, there is no general agreement that these procedures are appreciably more efficient than common quantal dose-response procedures that operate on dichotomized continuous data. This paper points out an equivalence between the dose-response models of the nonquantal approach of Kodell and West and a quantal probit procedure, and provides results from a Monte Carlo simulation study to compare coverage probabilities of statistical lower confidence limits on dose corresponding to specified additional risk based on applying the two procedures to continuous data from a dose-response experiment. The nonquantal approach is shown to be superior, in terms of both statistical validity and statistical efficiency.

Topics: Animals; Antitubercular Agents; Benchmarking; Bias; Computer Simulation; Confidence Intervals; Dose-Response Relationship, Drug; Drug-Related Side Effects and Adverse Reactions; Isoniazid; Likelihood Functions; Linear Models; Models, Statistical; Monte Carlo Method; Normal Distribution; Pharmaceutical Preparations; Probability; Rats; Rats, Inbred F344; Reproducibility of Results; Risk Assessment; Weight Gain

Characterizing the dose-effect relationship and estimating acceptable exposure levels are the primary goals of quantitative risk assessment. A semiparametric approach is proposed for risk assessment with continuously measured or quantitative outcomes which has advantages over existing methods by requiring fewer assumptions. The approach is based on pairwise ranking between the response values in the control group and those in the exposed groups. The work generalizes the rank-based Wilcoxon-Mann-Whitney test, which for the two-group comparison is effectively a test of whether a response from the control group is different from (larger than) a response in an exposed group. We develop a regression framework that naturally extends this metric to model the dose effect in terms of a risk function. Parameters of the regression model can be estimated with standard software. However, inference requires an additional step to estimate the variance structure of the estimated parameters. An effective dose (ED) and associated lower confidence limit (LED) are easily calculated. The method is supported by a simulation study and is illustrated with a study on the effects of aconiazide. The method offers flexible modeling of the dose effect, and since it is rank-based, it is more resistant to outliers, nonconstant variance, and other departures from normality than previously described approaches.

Topics: Animals; Environmental Exposure; Environmental Pollutants; Female; Humans; Isoniazid; Models, Statistical; Monte Carlo Method; No-Observed-Adverse-Effect Level; Rats; Rats, Inbred F344; Regression Analysis; Risk Assessment; Weight Gain