K-Adaptive Partitioning for Survival Data, with an Application to Cancer Staging
mag(2013)
摘要
In medical research, it is often needed to obtain subgroups with heterogeneous survivals, which have been predicted from a prognostic factor. For this purpose, a binary split has often been used once or recursively; however, binary partitioning may not provide an optimal set of well separated subgroups. We propose a multi-way partitioning algorithm, which divides the data into K heterogeneous subgroups based on the information from a prognostic factor. The resulting subgroups show significant differences in survival. Such a multi-way partition is found by maximizing the minimum of the subgroup pairwise test statistics. An optimal number of subgroups is determined by a permutation test. Our developed algorithm is compared with two binary recursive partitioning algorithms. In addition, its usefulness is demonstrated with a real data of colorectal cancer cases from the Surveillance Epidemiology and End Results program. We have implemented our algorithm into an R package maps, which is freely available in the Comprehensive R Archive Network (CRAN).
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