Application of logistic regression equation analysis using derivatives for optimal cutoff discriminative criterion estimation - Andrey Bokov; Svitlana Antonenko (Annals of Mathematics and Physics - Peertechz Publications, 2020)
Background: Sigmoid curve function is frequently applied for modeling in clinical studies. The main task of scientific research relevant to medicine is to find rational cutoff criterion for decision making rather than finding just equation for probability calculation. The objective of this study is to analyze the specific features of logistic regression curves in order to evaluate critical points and to assess their implication for continuous predictor variable dichotomization in order to provide optimal cutoff criterion for decision making. Methods: Second order and third order derivatives were used to analyze estimated logistic regression function, critical values of independent continuous variable that correspond to zero points of second and third derivative were calculated for each logistic regression equation. Using those values continuous predictors of each logistic regression equations were converted into dichotomized scales using 1 value that correspond to second order derivative and 2 values that correspond to zero points of third derivative then receiver operating characteristics of estimated equations with dichotomized predictor were assessed. Results: Sigmoid curve of logistic regression has the same structure with inflection point corresponding probability 0.5 (zero value of second derivative) and maximal torsion (zero values of third derivative) corresponding 0.2113 and 0.7886 probability values. Thresholds accounting for predictor values that correspond to zero values of second and third derivative provide estimation of logistic regression applying dichotomized predictor with optimal ratio of sensitivity, specificity and overall accuracy with maximal area under curve.  Conclusion: Analysis of logistic regression equation with continuous predictor applying derivatives help to choose optimal thresholds that provide maximally effective discriminative functions with priority sensitivity or specificity. Using this dichotomization discriminative function can be adjusted to the needs of particular task or study depending which characteristic is in priority - sensitivity or specificity.