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Conventional approaches to statistical inference preclude structures thatfacilitate incorporation of supplemental information acquired from similarcircumstances. For example, the analysis of data obtained using perfusioncomputed tomography to characterize functional imaging biomarkers in cancerousregions of the liver can benefit from partially informative data collectedconcurrently in noncancerous regions. This paper presents a hierarchical modelstructure that leverages all available information about a curve, usingpenalized splines, while accommodating important between-source features. Ourproposed methods flexibly borrow strength from the supplemental data to adegree that reflects the commensurability of the supplemental curve with theprimary curve. We investigate our method's properties for nonparametricregression via simulation, and apply it to a set of liver cancer data. We alsoapply our method for a semiparametric hazard model to data from a clinicaltrial that compares time to disease progression for three colorectal cancertreatments, while supplementing inference with information from a previoustrial that tested the current standard of care.

Combining nonexchangeable functional or survival data sources in oncology using generalized mixture commensurate priors