RESUMO
Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.
RESUMO
Receiver operating characteristic (ROC) curves are useful statistical tools used to assess the precision of diagnostic markers or to compare new diagnostic markers with old ones. The most common index employed for these purposes is the area under the ROC curve (theta) and several statistical tests exist that test the null hypotheses H(0): theta= 0.5 or H(0): theta1=theta2, in the case of two-marker comparisons, against alternatives of interest. In this paper we show that goodness-of-fit of uniformity of the distribution of the false positive (true positive) rates can be used instead of tests based on the area index. A semi-parametric approach is based on a completely specified distribution of marker measurements for either the healthy (F) or diseased (G) subjects, and this is extended to the two-marker case. We then extend to the one- and two-marker case when neither distribution is specified (the non-parametric case). In general, ROC-based tests are more powerful than goodness-of-fit tests for location differences between the distributions of healthy and diseased subjects. However ROC-based tests are less powerful when location-scale differences exist (producing ROC curves that cross the diagonal) and are incapable of discriminating between healthy and diseased samples when theta=0.5 but F not equal G. In these cases, goodness-of-fit tests have a distinct advantage over ROC-based tests. In conclusion, ROC methodology should be used with recognition of its potential limitations and should be replaced by goodness-of-fit tests when appropriate. The latter are a viable alternative and can be used as a 'black box' or as an exploratory first step in the evaluation of novel diagnostic markers.
