A Decision-Theoretic Comparison of Treatments to Resolve Air Leaks After Lung Surgery Based on Nonparametric Modeling

TL;DR

This paper introduces a Bayesian nonparametric group sequential design for clinical trials comparing treatments for resolving air leaks after lung surgery, focusing on utility-based weighted means due to skewed, multi-modal data.

Contribution

It develops a novel Bayesian nonparametric approach using utility-weighted means for treatment comparison in complex, skewed distributions, with a sequential testing framework.

Findings

The method effectively handles skewed, multi-modal data.

Simulation studies demonstrate desirable frequentist properties.

The approach provides a flexible, utility-based comparison of treatments.

Abstract

We propose a Bayesian nonparametric utility-based group sequential design for a randomized clinical trial to compare a gel sealant to standard care for resolving air leaks after pulmonary resection. Clinically, resolving air leaks in the days soon after surgery is highly important, since longer resolution time produces undesirable complications that require extended hospitalization. The problem of comparing treatments is complicated by the fact that the resolution time distributions are skewed and multi-modal, so using means is misleading. We address these challenges by assuming Bayesian nonparametric probability models for the resolution time distributions and basing the comparative test on weighted means. The weights are elicited as clinical utilities of the resolution times. The proposed design uses posterior expected utilities as group sequential test criteria. The procedure's frequentist properties are studied by extensive simulations.