A semiparametric approach to analysing dose–response data

✍ Scribed by Quinton J. Nottingham; Jeffrey B. Birch

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 131 KB
• Volume: 19
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(20000215)19:3<389::aid-sim326>3.0.co;2-j

✦ Synopsis

In the analysis of a quantal dose-response experiment with grouped data, the most commonly used parametric procedure is logistic regression, commonly referred to as &logit analysis'. The adequacy of the "t by the logistic regression curve is tested using the chi-square lack-of-"t test. If the lack-of-"t test is not signi"cant, then the logistic model is assumed to be adequate and estimation of e!ective doses and con"dence intervals on the e!ective doses can be made. When the tolerance distribution of the dose}response data is not known and cannot be assumed by the user, one can use non-parametric methods, such as kernel regression or local linear regression, to estimate the dose}response curve, e!ective doses and con"dence intervals. This research proposes another alternative based on semi-parametric regression to analysing quantal dose}response data called model-robust quantal regression (MRQR). MRQR linearly combines the parametric and non-parametric predictions with the use of a mixing parameter. MRQR uses logistic regression as the parametric portion of the model and local linear regression as the non-parametric portion of the model. Our research has shown that the MRQR procedure can improve the "t of the dose}response curve by producing narrower con"dence intervals for predictions while providing improved precision of estimates of the e!ective doses with respect to either logistic or local linear regression results.