Informativeness of Diagnostic Marker Values and the Impact of Data Grouping.

Hua Ma, Andriy I Bandos, David Gur,

Computational statistics & data analysis, September 11, 2017

Assessing performance of diagnostic markers is a necessary step for their use in decision making regarding various conditions of interest in diagnostic medicine and other fields. Globally useful markers could, however, have ranges of values that are “diagnostically non-informative”. This paper demonstrates that the presence of marker values from diagnostically non-informative ranges could lead to a loss in statistical efficiency during nonparametric evaluation and shows that grouping non-informative values provides a natural resolution to this problem. These points are theoretically proven and an extensive simulation study is conducted to illustrate the possible benefits of using grouped marker values in a number of practically reasonable scenarios. The results contradict the common conjecture regarding the detrimental effect of grouped marker values during performance assessments. Specifically, contrary to the common assumption that grouped marker values lead to bias, grouping non-informative values does not introduce bias and could substantially reduce sampling variability. The proven concept that grouped marker values could be statistically beneficial without detrimental consequences implies that in practice, tied values do not always require resolution whereas the use of continuous diagnostic results without addressing diagnostically non-informative ranges could be statistically detrimental. Based on these findings, more efficient methods for evaluating diagnostic markers could be developed.

Pubmed Link: 28890588

DOI: 10.1016/j.csda.2017.07.008