Do medical decision analyses' largest gains grow from the smallest trees?

Abstract

Abstract Some decision analysts recommend reserving their models for careful, complete study of complex problems. Others believe simple problem analyses provide the greatest gains. To clarify these issues, I tested: (1) whether simple published analyses compared to complex ones, produced results as non‐intuitive; and (2) whether non‐intuitiveness represented analytic gain (or equivalently, intuitive loss). Very limited data also examined (3) the gain from adding model intricacy for a given problem vs. selecting more complex problems for analysis. To assess two non‐intuitiveness measures, 75 subjects (33 physicians and 42) non‐physicians stated their intuitive preferences in 40 (22 published and 18 unpublished) medical dilemmas. For both physicians and non‐physicians, simpler models (those with lees than 30 terminal branches in the decision tree) had about one third the non‐intuitiveness of more complex ones. Three tests also supported the premise that analyses outperform intuition, therefore that on average the formers' non‐intuitiveness reflects less analytic gain. In addition, for different models of the same problem, tree complexity did not correlate with gain. Thus, simpler trees may not generally gain less because they inadequately describe problems. Instead, simpler analyses may represent simpler problems, with more similar intuitive and formal solutions. If so, these findings may help us avoid some costly but unnecessary simple analyses.

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