TY - JOUR
T1 - Estimating cost-effectiveness from claims and registry data with measured and unmeasured confounders
AU - Handorf, Elizabeth A.
AU - Heitjan, Daniel F.
AU - Bekelman, Justin E.
AU - Mitra, Nandita
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: USPHS grants P30-CA016520, P30-CA06927, T32-CA093283, and K12-CA076931 supported our research.
Funding Information:
This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. We acknowledge the efforts of the Applied Research Program, NCI; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries in the creation of the SEER-Medicare database.
Publisher Copyright:
© The Author(s) 2018.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
KW - Economic analysis
KW - cost-effectiveness analysis
KW - observational studies
KW - sensitivity analysis
KW - unmeasured confounding
UR - http://www.scopus.com/inward/record.url?scp=85043720901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043720901&partnerID=8YFLogxK
U2 - 10.1177/0962280218759137
DO - 10.1177/0962280218759137
M3 - Article
C2 - 29468944
AN - SCOPUS:85043720901
SN - 0962-2802
VL - 28
SP - 2227
EP - 2242
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 7
ER -