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Uncertainty and the value of diagnostic information, with application to axillary lymph node dissection in breast cancer
Author(s) -
Parmigiani Giovanni
Publication year - 2004
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.1623
Subject(s) - computer science , probabilistic logic , decision tree , breast cancer , automatic summarization , value of information , axillary lymph nodes , node (physics) , sensitivity (control systems) , decision tree model , optimal decision , decision analysis , set (abstract data type) , data mining , artificial intelligence , machine learning , statistics , medicine , mathematics , cancer , programming language , engineering , structural engineering , electronic engineering
Abstract In clinical decision making, it is common to ask whether, and how much, a diagnostic procedure is contributing to subsequent treatment decisions. Statistically, quantification of the value of the information provided by a diagnostic procedure can be carried out using decision trees with multiple decision points, representing both the diagnostic test and the subsequent treatments that may depend on the test's results. This article investigates probabilistic sensitivity analysis approaches for exploring and communicating parameter uncertainty in such decision trees. Complexities arise because uncertainty about a model's inputs determines uncertainty about optimal decisions at all decision nodes of a tree. We present the expected utility solution strategy for multistage decision problems in the presence of uncertainty on input parameters, propose a set of graphical displays and summarization tools for probabilistic sensitivity analysis in multistage decision trees, and provide an application to axillary lymph node dissection in breast cancer. Copyright © 2004 John Wiley & Sons, Ltd.