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

TL;DR

This paper introduces a hierarchical modeling approach that combines multiple nonexchangeable functional or survival data sources in oncology, allowing flexible borrowing of information based on their commensurability, demonstrated through simulations and real data applications.

Contribution

It develops a novel hierarchical model with penalized splines that adaptively integrates supplemental data in nonparametric regression and hazard models, accommodating source differences.

Findings

Method effectively borrows information from supplemental data.

Simulation studies show desirable properties of the approach.

Application to liver cancer and colorectal trial data demonstrates practical utility.

Abstract

Conventional approaches to statistical inference preclude structures that facilitate incorporation of supplemental information acquired from similar circumstances. For example, the analysis of data obtained using perfusion computed tomography to characterize functional imaging biomarkers in cancerous regions of the liver can benefit from partially informative data collected concurrently in noncancerous regions. This paper presents a hierarchical model structure that leverages all available information about a curve, using penalized splines, while accommodating important between-source features. Our proposed methods flexibly borrow strength from the supplemental data to a degree that reflects the commensurability of the supplemental curve with the primary curve. We investigate our method's properties for nonparametric regression via simulation, and apply it to a set of liver cancer data. We also apply our method for a semiparametric hazard model to data from a clinical trial that compares time to disease progression for three colorectal cancer treatments, while supplementing inference with information from a previous trial that tested the current standard of care.