Illustration of the Impact of Unmeasured Confounding Within anEconomic Evaluation Based on Nonrandomized Data

摘要

>Background: Propensity score (PS) methods are frequently used within economic evaluations based on nonrandomized data to adjust for measured confounders, but many researchers omit the fact that they cannot adjust for unmeasured confounders. >Objective: To illustrate how confounding due to unmeasured confounders can bias an economic evaluation despite PS matching. >Methods: We used data from a previously published nonrandomized study to select a prematched population consisting of 121 patients (46.5%) who received endovascular aneurysm repair (EVAR) and 139 patients (53.5%) who received open surgical repair (OSR), in which sufficient data regarding eight measured confounders were available. One-to-one PS matching was used within this population to select two PS-matched subpopulations. The Matched PS-Smoking Excluded Subpopulation was selected by matching patients using a PS model that omitted patients’ smoking status (one of the measured confounders), whereas the Matched PS-Smoking Included Subpopulation was selected by matching patients using a PS model that included all eight measured confounders. Incremental cost-effectiveness ratios (ICERs) were assessed within bothsubpopulations. >Results: Both subpopulations were composed of twodifferent sets of 164 patients. Balance within the Matched PS-Smoking ExcludedSubpopulation was achieved on all confounders except for patients’ smokingstatus, whereas balance within the Matched PS-Smoking Included Subpopulation wasachieved on all confounders. Results indicated that the ICER of EVAR over OSRdiffered between both subpopulations; the ICER was estimated at $157,909 perlife-year gained (LYG) within the Matched PS-Smoking Excluded Subpopulation,while it was estimated at $235,074 per LYG within the Matched PS-SmokingIncluded Subpopulation. >Discussion: Although effective incontrolling for measured confounding, PS matching may not adjust for unmeasuredconfounders that may bias the results of an economic evaluation based onnonrandomized data.