Here, we introduce a nonlinear regression method that treats the control data in a statistically balanced way, neither neglecting nor privileging it. Based on the assumption that measurement error is Gaussian distributed, our method is based on maximizing the likelihood of observing all of the data given the full set of parameters θ. The model parameters are different than when only the curve is fit (Fig. ). We demonstrate the method on simulations of complete and incomplete CRCs. We also apply the proposed procedure to 1830