Aim:To construct classification scores based on a combination of cancer patient plasma biomarker levels, for predicting progression-free survival.Methods:The approach is based on the optimization of the biomarker cut-off values, which maximize the statistical differences between the groups with values lower or larger than the cut-offs, respectively. An intuitive visualization of the quality of the classification score is also proposed.Results:Even if there are only weak correlations between individual biomarker levels and progression-free survival, scores based on suitably chosen combination of three biomarkers have classification power comparable with the Response Evaluation Criteria in Solid Tumors criteria classification of response to treatments in solid tumors.Conclusion:Our approach has the potential to improve the selection of the patients who will benefit from a given anticancer treatment.
Background: Statistical methods commonly used in survival analysis typically provide the probability that the difference between groups is due to chance, but do not offer a reliable estimate of the average survival time difference between groups (the differences between median survival time is usually reported). OBJECTIVE: We suggest a Maximum-Entropy estimator for the average Survival Time Difference (MESTD) between groups. METHOD: The estimator based on the extra survival time, which should be added to each member of the one group, to produce the maximum entropy of the result (resulting in the groups becoming most similar). The estimator is calculated only from time to event data, does not necessarily assume hazard proportionality and provides the magnitude of the clinical differences between the groups. RESULTS: Monte Carlo simulations show that, even at low sample numbers (much lower than the ones needed to prove that the two groups are statistically different), the MESTD estimator is a reliable predictor of the clinical differences between the groups, and therefore can be used to estimate from (low sample numbers) preliminary data whether or not the large sample number experiment is worth pursuing. CONCLUSION: By providing a reasonable estimate for the efficacy of a treatment (e.g., for cancer) even for low sample data, it might provide useful insight in testing new methods for treatment (for example, for quick testing of multiple combinations of cancer drugs). AVAILABILITY OF DATA AND MATERIALS: The suggested method was tested using the largest data set available in the library "survival" of the R software, representing the survival in patients with advanced lung cancer from the North Central Cancer Treatment Group.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.