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Monitoring the introduction of a surgical intervention with long‐term consequences
Author(s) -
GorstRasmussen A.,
Spiegelhalter D. J.,
Bull C.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2548
Subject(s) - term (time) , intervention (counseling) , computer science , bayesian probability , construct (python library) , variety (cybernetics) , cohort , medicine , artificial intelligence , physics , quantum mechanics , psychiatry , programming language
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. Copyright © 2006 John Wiley & Sons, Ltd.