Monitoring the introduction of a 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.
Department of Mathematical Sciences, Aalborg University, Denmark. gorst@math.aau.dk
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Gorst-Rasmussen
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DJ
Spiegelhalter
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Bull
D001499:Bayes Theorem; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D053208:Kaplan-Meier Estimate; D008297:Male; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D013514:Surgical Procedures, Operative / Q000379:methods* / Q000592:standards; D014188:Transposition of Great Vessels / Q000601:surgery; D016896:Treatment Outcome