Problems in defining cutoff points of continuous prognostic factors: example of tumor thickness in primary cutaneous melanoma.

Continuous prognostic factors are often categorized by defining optimized cutoff points. One component of criticism of this approach is the problem of multiple testing that leads to an overestimation of the true prognostic impact of the variable. The present study focuses on another crucial point by investigating the dependence of optimized cutoff points on the observed distribution of the continuous variable. The continuous variable investigated was the vertical tumor thickness according to Breslow, which is known to be the most important prognostic factor in primary melanoma. Based on the data of 5093 patients, stratified random samples were drawn out of six artificially created distributions of tumor thickness. For each of these samples, Cox models were calculated to explore optimized cutoff points for tumor thickness together with other prognostic variables. The optimized cutoff points for tumour thickness varied considerably with the underlying distribution. Even in samples from the same distribution, the range of cutoff points was amazingly broad and, for some of the distributions, covered the whole region of possible values. The results of the present study demonstrate that optimized cutoff points are extremely data dependent and vary notably even if prerequisites are constant. Therefore, if the classification of a continuous prognostic factor is necessary, it should not be based on the results of one single study, but on consensus discussions including the findings of several investigations.