Abstract
BackgroundSurvival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance.MethodsWe address the issue of testing marker combinations that affect survival by adapting the recently developed Limitless Arity Multiple-testing Procedure (LAMP), a p-value correction technique for statistical tests for combination of markers. LAMP cannot handle survival data statistics, and hence we extended LAMP for the log-rank test, making it more appropriate for clinical data, with newly introduced theoretical lower bound of the p-value.ResultsWe applied the proposed method to gene combination detection for cancer and obtained gene interactions with statistically significant log-rank p-values. Gene combinations with orders of up to 32 genes were detected by our algorithm, and effects of some genes in these combinations are also supported by existing literature.ConclusionThe novel approach for detecting prognostic markers presented here can identify statistically significant markers with no limitations on the order of interaction. Furthermore, it can be applied to different types of genomic data, provided that binarization is possible.
Highlights
Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases
Relator et al BMC Medical Genomics 2018, 11(Suppl 2):31 algorithms and techniques have already been developed for marker detection, with strategies ranging from variable selection to Cox score ranking [2] and log-rank test
We propose an extension of Limitless Arity Multiple-testing Procedure (LAMP) for log-rank test to detect prognostic gene combinations
Summary
Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. Owing to the high dimension of data leading to combinatorial explosion, most existing methods can only exhaustively inspect individual candidate markers, failing to consider high order interactions. Multiple hypothesis testing has complicated the evaluation of statistical significance of detected markers, even in individual inspections, as the large correction factor limits novel discovery from data
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