Clinical Decision Analysis and Markov Modeling for Surgeons An Introductory Overview

This study addresses the use of decision analysis and Markov models to make contemplated decisions for surgical problems. Decision analysis and decision modeling in surgical research are increasing, but many surgeons are unfamiliar with the techniques and are skeptical of the results. The goal of this review is to familiarize surgeons with techniques and terminology used in decision analytic papers, to provide the reader a practical guide to read these papers, and to ensure that surgeons can critically appraise the quality of published clinical decision models and draw well founded conclusions from such reports. First, a brief explanation of decision analysis and Markov models is presented in simple steps, followed by an overview of the components of a decision and Markov model. Subsequently, commonly used terms and definitions are described and explained, including quality-adjusted life-years, disability-adjusted life-years, discounting, half-cycle correction, cycle length, probabilistic sensitivity analysis, incremental cost-effectiveness ratio, and the willingness-to-pay threshold. Finally, the advantages and limitations of research with Markov models are described, and new modeling techniques and future perspectives are discussed. It is important that surgeons are able to understand conclusions from decision analytic studies and are familiar with the specific definitions of the terminology used in the field to keep up with surgical research. Decision analysis can guide treatment strategies when complex clinical questions need to be answered and is a necessary and useful addition to the surgical research armamentarium.