Experimental designs for controlling the correlation of estimators in two‐parameter models.

The state of the art related to parameter correlation in two‐parameter models has been reviewed in this paper. The apparent contradictions between the different authors regarding the ability of D‐optimality to simultaneously reduce the correlation and the area of the confidence ellipse in two‐parameter models were analyzed. Two main approaches were found: (1) those who consider that the optimality criteria simultaneously control the precision and correlation of the parameter estimators and (2) those that consider a combination of criteria to achieve the same objective. An analytical criterion combining in its structure both the optimality of the precision of the estimators of the parameters and the reduction of the correlation between their estimators is provided. The criterion was tested both in a simple linear regression model, considering all possible design spaces, and in a nonlinear model with strong correlation of the estimators of the parameters (Michaelis–Menten) to show its performance. This criterion showed a superior behavior to all the strategies and criteria to control at the same time the precision and the correlation. This paper reviews the conflicting views on using D‐optimality to control parameter correlation in two‐parameter models. It proposes an analytical criterion that effectively balances both the precision and correlation of parameter estimators. The criterion outperforms existing strategies in both linear and nonlinear models, specifically in the Michaelis‐Menten model. [ABSTRACT FROM AUTHOR]