Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders

Abstract

The analysis of observational data to determine the cost-effectiveness of medical treatments is complicated by the need to account for skewness, censoring, and the effects of measured and unmeasured confounders. We quantify cost-effectiveness as the Net Monetary Benefit (NMB), a linear combination of the treatment effects on cost and effectiveness that denominates utility in monetary terms. We propose a parametric estimation approach that describes cost with a Gamma generalized linear model and survival time (the canonical effectiveness variable) with a Weibull accelerated failure time model. To account for correlation between cost and survival, we propose a bootstrap procedure to compute confidence intervals for NMB. To examine sensitivity to unmeasured confounders, we derive simple approximate relationships between naïve parameters, assuming only measured confounders, and the values those parameters would take if there was further adjustment for a single unmeasured confounder with a specified distribution. A simulation study shows that the method returns accurate estimates for treatment effects on cost, survival, and NMB under the assumed model. We apply our method to compare two treatments for Stage II/III bladder cancer, concluding that the NMB is sensitive to hypothesized unmeasured confounders that represent smoking status and personal income.

One-Year Costs Associated with Hospitalizations Due to Aortic Stenosis in Canada

“…For these reasons (e.g. unobserved confounders (32) and absence of statistical methods to match our 5 cohorts), our secondary analyses were exploratory and descriptive in nature and direct comparisons by treatment modalities should not be made. In addition, any statistical methods to compare the different groups of patients by treatment modalities would also have to take into consideration regional differences, as shown by our data.…”

“… 24 , 25 Furthermore, many important variables were not available in our datasets (eg, STS score, frailty index, physician and patient preferences), thus limiting our ability to adjust between cohorts to make meaningful comparisons. For these reasons (eg, unobserved confounders 32 and absence of statistical methods to match our 5 cohorts), our secondary analyses were exploratory and descriptive in nature, and direct comparisons by treatment modalities should not be made. In addition, any statistical methods to compare the different groups of patients by treatment modalities would also have to take into consideration regional differences, as shown by our data.…”

A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies

“…Performing a proper CEA using statistical methods for valid estimation and inference is not very commonplace for such nonrandomized censored data. Various methods for censored CEA 14,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] have been proposed, providing a good foundation, yet there is room for improvement (e.g., estimates are not adjusted for covariates, not efficient, not doubly robust, and not straightforward to construct a CEAC). This article illustrates net benefit regression for censored costeffectiveness data from observational studies, 8 which overcomes the aforementioned disadvantages as well as unifying many methods as special cases (e.g., the methods work for uncensored data and unadjusted analysis).…”