A decision tree is a form of analytical model, in which distinct branches are used to represent a potential set of outcomes for a patient or patient cohort. A decision tree consists of a series of ‘nodes’ where branches meet: each node may take the form of a ‘choice’ (a decision about which alternative intervention to use) or a ‘probability’ (an event occurring or not occurring, governed by chance). Probabilities at any specific node must always add to 1. Costs and outcomes are assigned to each segment of each branch, including the end (‘leaf’) of each branch. Outcomes and costs for each branch are combined using branch possibilities and the tree is ‘rolled back’ to a decision node, at which the expected outcome and cost for each treatment alternative can be compared. Decision trees are frequently used to model interventions that have distinct outcomes that can be measured at a specific time point.
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