Estimating Treatment-Switching Bias in a Randomized Clinical Trial of Ovarian Cancer Treatment: Combining Causal Inference with Decision-Analytic Modeling

被引：1

作者：

Kuehne, Felicitas ^{[1
]}

Rochau, Ursula ^{[1
]}

Paracha, Noman ^{[2
]}

Yeh, Jennifer M. ^{[3
,4
]}

Sabate, Eduardo ^{[5
]}

Siebert, Uwe ^{[1
,6
,7
,8
,9
,10
]}

机构：

[1] UMIT Univ Hlth Sci Med Informat & Technol, Dept Publ Hlth Hlth Serv Res & Hlth Technol Asses, Inst Publ Hlth Med Decis Making & Hlth Technol As, Hall In Tirol, Austria

[2] Bayer Consumer Care AG, Pharmaceut, Oncol SBU, Basel, Switzerland

[3] Harvard Med Sch, Dept Pediat, Boston, MA 02115 USA

[4] Boston Childrens Hosp, Boston, MA USA

[5] Daiichi Sankyo Inc, Oncol, Basking Ridge, NJ USA

[6] ONCOTYROL Ctr Personalized Canc Med, Div Hlth Technol Assessment, Innsbruck, Austria

[7] Harvard TH Chan Sch Publ Hlth, Ctr Hlth Decis Sci, Dept Epidemiol, Boston, MA USA

[8] Harvard TH Chan Sch Publ Hlth, Ctr Hlth Decis Sci, Dept Hlth Policy & Management, Boston, MA USA

[9] Harvard Med Sch, Inst Technol Assessment, Massachusetts Gen Hosp, Boston, MA 02115 USA

[10] Harvard Med Sch, Dept Radiol, Massachusetts Gen Hosp, Boston, MA 02115 USA

来源：

MEDICAL DECISION MAKING | 2022年 / 42卷 / 02期

关键词：

causal inference; decision analysis; health decision science; markov model; oncology; survival analysis; switching bias; PHASE-3; TRIAL; EPITHELIAL OVARIAN; COST-EFFECTIVENESS; CELL CARCINOMA; OPEN-LABEL; TO-TREAT; BEVACIZUMAB; CHEMOTHERAPY; RECURRENT; SURVIVAL;

D O I：

10.1177/0272989X211026288

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Background Bevacizumab is efficacious in delaying ovarian cancer progression and controlling ascites. The ICON7 trial showed a significant benefit in overall survival for bevacizumab, whereas the GOG-218 trial did not. GOG-218 allowed control group patients to switch to bevacizumab upon progression, which may have biased the results. Lack of data on switching behavior prevented the application of g-methods to adjust for switching. The objective of this study was to apply decision-analytic modeling to estimate the impact of switching bias on causal treatment-effect estimates. Methods We developed a causal decision-analytic Markov model (CDAMM) to emulate the GOG-218 trial and estimate overall survival. CDAMM input parameters were based on data from randomized clinical trials and the published literature. Overall switching proportion was based on GOG-218 trial information, whereas the proportion switching with and without ascites was estimated using calibration. We estimated the counterfactual treatment effect that would have been observed had no switching occurred by denying switching in the CDAMM. Results The survival curves generated by the CDAMM matched well with the ones reported in the GOG-218 trial. The survival curve correcting for switching showed an estimated bias such that 79% of the true treatment effect could not be observed in the GOG-218 trial. Results were most sensitive to changes in the proportion progressing with severe ascites and mortality. Limitations We used a simplified model structure and based model parameters on published data and assumptions. Robustness of the CDAMM was tested and model assumptions transparently reported. Conclusions Medical-decision science methods may be merged with empirical methods of causal inference to integrate data from other sources where empirical data are not sufficient. We recommend collecting sufficient information on switching behavior when switching cannot be avoided.

引用

页码：194 / 207

页数：14

共 88 条

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