Abstract
Data partitioning methods, including regression trees , have been widely used to describe subgroups of cancer patients with differing prognosis. We describe an alternative technique based on a modeling that characterizes subgroups through a smooth basis function representation of subgroups. The strategy allows the user to control the expected number of patients in the subgroup as well as the anticipated survival in the targeted group. An example based on data from a clinical trial for patients with Myeloma is used to illustrate the new method.
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LeBlanc, M. (2017). A Smooth Basis for Clinical Subgroup Analysis. In: Matsui, S., Crowley, J. (eds) Frontiers of Biostatistical Methods and Applications in Clinical Oncology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0126-0_15
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DOI: https://doi.org/10.1007/978-981-10-0126-0_15
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