Assessing the sensitivity of decision-analytic results to unobserved markers of risk: defining the effects of heterogeneity bias.

An important assumption made when constructing a Markov model is that all persons residing in a health state are identical. Failure to adjust for population heterogeneity caused by unobserved variables can therefore cause bias in model results. The authors used a simple model to evaluate the potential impact of heterogeneity bias, defined as the percentage change in the life expectancy gain with an intervention predicted by a model that does not adjust for heterogeneity (unadjusted model) compared to one that does (adjusted model). The life expectancy gains were consistently greater in the unadjusted model compared to the adjusted model (positive bias). For an annual probability of developing disease of 1%, the heterogeneity bias exceeded 50% when the relative risk of disease with the heterogeneity factor versus without the factor was greater than 15 and the prevalence of the heterogeneity factor was between 5% and 25%. When constructing decision-analytic models, analysts need to be cognizant of unobserved factors that introduce heterogeneity into the cohort. This analysis provides a general framework to determine when issues of heterogeneity may be important.