Monitoring a New Surgical Intervention with Long-Term Consequences

 

Surgical innovations are often introduced for their expected long-term benefits, but the decision to abandon the existing treatment must be based on the available short-term data and rational judgment. We present a framework for monitoring the introduction of a surgical intervention with long-term consequences and failure-time endpoints. The framework is based on Bayesian methods, and formally combines study data, clinical opinion, and external evidence to construct a posterior survival function from which intuitive summary statistics can be extracted to aid decision making. It incorporates learning effects and is adaptable to a wide variety of settings. The methods are illustrated on survival data from a cohort of 325 consecutive neonates treated for simple transposition of the great arteries with either the Senning or the Switch operation during the period 1978-1998.

Joint work with: A. Gorst-Rasmussen, and C. Bull