Convergence Behavior of Optimal Cut-Off Points Derived from Receiver Operating Characteristics Curve Analysis: A Simulation Study

TY - JOUR

T1 - Convergence Behavior of Optimal Cut-Off Points Derived from Receiver Operating Characteristics Curve Analysis: A Simulation Study

AU - Gerke, Oke

AU - Zapf, Antonia

PY - 2022/11/10

Y1 - 2022/11/10

N2 - The area under the receiver operating characteristics curve is a popular measure of the overall discriminatory power of a continuous variable used to indicate the presence of an outcome of interest, such as disease or disease progression. In clinical practice, the use of cut-off points as benchmark values for further treatment planning is greatly appreciated, despite the loss of information that such a dichotomization implies. Optimal cut-off points are often derived from fixed sample size studies, and the aim of this study was to investigate the convergence behavior of optimal cut-off points with increasing sample size and to explore a heuristic and path-based algorithm for cut-off point determination that targets stagnating cut-off point values. To this end, the closest-to-(0,1) criterion in receiver operating characteristics curve analysis was used, and the heuristic and path-based algorithm aimed at cut-off points that deviated less than 1% from the cut-off point of the previous iteration. Such a heuristic determination stopped after only a few iterations, thereby implicating practicable sample sizes; however, the result was, at best, a rough estimate of an optimal cut-off point that was unbiased and positively and negatively biased for a prevalence of 0.5, smaller than 0.5, and larger than 0.5, respectively.

AB - The area under the receiver operating characteristics curve is a popular measure of the overall discriminatory power of a continuous variable used to indicate the presence of an outcome of interest, such as disease or disease progression. In clinical practice, the use of cut-off points as benchmark values for further treatment planning is greatly appreciated, despite the loss of information that such a dichotomization implies. Optimal cut-off points are often derived from fixed sample size studies, and the aim of this study was to investigate the convergence behavior of optimal cut-off points with increasing sample size and to explore a heuristic and path-based algorithm for cut-off point determination that targets stagnating cut-off point values. To this end, the closest-to-(0,1) criterion in receiver operating characteristics curve analysis was used, and the heuristic and path-based algorithm aimed at cut-off points that deviated less than 1% from the cut-off point of the previous iteration. Such a heuristic determination stopped after only a few iterations, thereby implicating practicable sample sizes; however, the result was, at best, a rough estimate of an optimal cut-off point that was unbiased and positively and negatively biased for a prevalence of 0.5, smaller than 0.5, and larger than 0.5, respectively.

UR - https://www.mdpi.com/2227-7390/10/22/4206/pdf

U2 - 10.3390/math10224206

DO - 10.3390/math10224206

M3 - SCORING: Journal article

VL - 10

JO - MATHEMATICS-BASEL

JF - MATHEMATICS-BASEL

SN - 2227-7390

IS - 22

M1 - 4206

ER -