An algorithm for robust non-linear analysis of radioimmunoassays and other bioassays.
The four-parameter logistic function is an appropriate model for many types of bioassays that have continuous response variables, such as radioimmunoassays. By modelling the variance of replicates in an assay, one can modify the usual parameter estimation techniques (for example, Gauss-Newton or Marquardt-Levenberg) to produce parameter estimates for the standard curve that are robust against outlying observations. This article describes the computation of robust (M-) estimates for the parameters of the four-parameter logistic function. It describes techniques for modelling the variance structure of the replicates, modifications to the usual iterative algorithms for parameter estimation in non-linear models, and a formula for inverse confidence intervals. To demonstrate the algorithm, the article presents examples where the robustly estimated four-parameter logistic model is compared with the logit-log and four-parameter logistic models with least-squares estimates.
Normolle
D P
DP
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor 48109-2029.
D000465:Algorithms; D001681:Biological Assay / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D006801:Humans; D016018:Least-Squares Analysis; D016015:Logistic Models; D011863:Radioimmunoassay / Q000706:statistics & numerical data*