Probit Models for Estimating Effective Pharmacological Treatment Dosages (14 Tests)

Abstract

Probit regression is, just like logistic regression, for estimating the effect of predictors on yes/no outcomes. If your predictor is multiple pharmacological treatment dosages, then probit regression may be more convenient than logistic regression, because your results will be reported in the form of response rates instead of odds ratios. The dependent variable of the two methods log odds (otherwise called logit) and log prob (otherwise called probit) are closely related to one another. Log prob (probability), is the z-value corresponding to its area under the curve value of the normal distribution. It can be shown that the log odds of responding ≈ (π/√3) x log prob of responding (see Chap. 7, Machine learning in medicine part three, Probit regression, pp 63–68, 2013, Springer Heidelberg Germany, from the same authors).