Incorporating external trial data to improve survival extrapolations: a pilot study of the COU-AA-301 trial
Tóm tắt
Survival extrapolation plays a key role within cost effectiveness analysis and is often subject to substantial uncertainty. Use of external data to improve extrapolations has been identified as a key research priority. We present findings from a pilot study using data from the COU-AA-301 trial of abiraterone acetate for metastatic castration-resistant prostate cancer, to explore how external trial data may be incorporated into survival extrapolations. External trial data were identified via a targeted search of technology assessment reports. Four methods using external data were compared to simple parametric models (SPMs): informal reference to external data to select appropriate SPMs, piecewise models with, and without, hazard ratio adjustment, and Bayesian models fitted with a prior on the shape parameter(s). Survival and hazard plots were compared, and summary metrics (point estimate accuracy and restricted mean survival time) were calculated. Without consideration of external data, several SPMs may have been selected as the ‘best-fitting’ model. The range of survival probability estimates was generally reduced when external data were included in model estimation, and external hazard plots aided model selection. Different methods yielded varied results, even with the same data source, highlighting potential issues when integrating external trial data within model estimation. By using external trial data, the most (in)appropriate models may be more easily identified. However, benefits of using external data are contingent upon their applicability to the research question, and the choice of method can have a large impact on extrapolations.
Tài liệu tham khảo
Baio, G.: survHE: Survival analysis in health economic evaluation. R package version 1.1.2 (2018). https://CRAN.R-project.org/package=survHE
Brownlee, J.: What is the Difference Between Test and Validation Datasets?, https://machinelearningmastery.com/difference-test-validation-datasets/, (2017)
Collins, R., Trowman, R., Norman, G., Light, K., Birtle, A., Fenwick, E., Palmer, S., Riemsma, R.: A systematic review of the effectiveness of docetaxel and mitoxantrone for the treatment of metastatic hormone-refractory prostate cancer. Br. J. Cancer. 95, 457–462 (2006). https://doi.org/10.1038/sj.bjc.6603287
Cope, S., Ayers, D., Zhang, J., Batt, K., Jansen, J.P.: Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia. BMC Med. Res. Methodol. 19, 182 (2019). https://doi.org/10.1186/s12874-019-0823-8
Cox, D.R.: Regression models and life-tables. J. r. Stat. Soc. Ser. B Methodol. 34, 187–220 (1972)
de Bono, J.S., Logothetis, C.J., Molina, A., Fizazi, K., North, S., Chu, L., Chi, K.N., Jones, R.J., Goodman, O.B., Saad, F., Staffurth, J.N., Mainwaring, P., Harland, S., Flaig, T.W., Hutson, T.E., Cheng, T., Patterson, H., Hainsworth, J.D., Ryan, C.J., Sternberg, C.N., Ellard, S.L., Fléchon, A., Saleh, M., Scholz, M., Efstathiou, E., Zivi, A., Bianchini, D., Loriot, Y., Chieffo, N., Kheoh, T., Haqq, C.M., Scher, H.I.: COU-AA-301 investigators: abiraterone and increased survival in metastatic prostate cancer. N. Engl. J. Med. 364, 1995–2005 (2011). https://doi.org/10.1056/NEJMoa1014618
Dellis, A., Zagouri, F., Liontos, M., Mitropoulos, D., Bamias, A., Papatsoris, A.G.: Management of advanced prostate cancer: a systematic review of existing guidelines and recommendations. Cancer Treat. Rev. 73, 54–61 (2019). https://doi.org/10.1016/j.ctrv.2018.11.005
Fizazi, K., Scher, H.I., Molina, A., Logothetis, C.J., Chi, K.N., Jones, R.J., Staffurth, J.N., North, S., Vogelzang, N.J., Saad, F., Mainwaring, P., Harland, S., Goodman, O.B., Sternberg, C.N., Li, J.H., Kheoh, T., Haqq, C.M., de Bono, J.S.: COU-AA-301 investigators: abiraterone acetate for treatment of metastatic castration-resistant prostate cancer: final overall survival analysis of the COU-AA-301 randomised, double-blind, placebo-controlled phase 3 study. Lancet Oncol. 13, 983–992 (2012). https://doi.org/10.1016/S1470-2045(12)70379-0
Fizazi, K., Massard, C., Smith, M., Rader, M., Brown, J., Milecki, P., Shore, N., Oudard, S., Karsh, L., Carducci, M., Damião, R., Wang, H., Ying, W., Goessl, C.: Bone-related parameters are the main prognostic factors for overall survival in men with bone metastases from castration-resistant prostate cancer. Eur. Urol. 68, 42–50 (2015). https://doi.org/10.1016/j.eururo.2014.10.001
Guyot, P., Welton, N.J., Ouwens, M.J.N.M., Ades, A.E.: Survival time outcomes in randomized, controlled trials and meta-analyses: the parallel universes of efficacy and cost-effectiveness. Value Health J. Int. Soc. Pharmacoeconomics Outcomes Res. 14, 640–646 (2011). https://doi.org/10.1016/j.jval.2011.01.008
Guyot, P., Ades, A., Ouwens, M.J., Welton, N.J.: Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med. Res. Methodol. 12, 9 (2012). https://doi.org/10.1186/1471-2288-12-9
Guyot, P., Ades, A.E., Beasley, M., Lueza, B., Pignon, J.-P., Welton, N.J.: Extrapolation of survival curves from cancer trials using external information. Med. Decis. Mak. Int. J. Soc. Med. Decis. Mak. 37, 353–366 (2017). https://doi.org/10.1177/0272989X16670604
Hess, K., Gentleman, R.: muhaz: Produces a smooth estimate of the hazard function for censored data. R package version 12.6.4 (2019). https://CRAN.R-project.org/package=muhaz
Jackson, C., Stevens, J., Ren, S., Latimer, N., Bojke, L., Manca, A., Sharples, L.: Extrapolating survival from randomized trials using external data: a review of methods. Med. Decis. Mak. Int. J. Soc. Med. Decis. Mak. 37, 377–390 (2017). https://doi.org/10.1177/0272989X16639900
Jackson, C., Metcalfe, P., Amdahl, J.: flexsurv: Flexible Parametric Survival and Multi-State Models. R package version 2.1 (2019). https://CRAN.R-project.org/package=flexsurv
Kearns, B., Stevenson, M.D., Triantafyllopoulos, K., Manca, A.: Generalized linear models for flexible parametric modeling of the hazard function. Med. Decis. Mak. Int. J. Soc. Med. Decis. Mak. 39, 867–878 (2019). https://doi.org/10.1177/0272989X19873661
Kearns, B., Stevens, J., Ren, S., Brennan, A.: How uncertain is the survival extrapolation? a study of the impact of different parametric survival models on extrapolated uncertainty about hazard functions, lifetime mean survival and cost effectiveness. Pharmacoeconomics 38, 193–204 (2020). https://doi.org/10.1007/s40273-019-00853-x
Kearns, B., Stevenson, M.D., Triantafyllopoulos, K., Manca, A.: The Extrapolation performance of survival models for data with a cure fraction: a simulation study. Value Health. (2021). https://doi.org/10.1016/j.jval.2021.05.009
Kuriyama, M., Obata, K., Miyagawa, Y., Nishikawa, E., Koide, T., Takeda, A., Komeda, Y., Kanbayashi, T., Nakano, M., Miyake, K.: Serum prostate-specific antigen values for the prediction of clinical stage and prognosis in patients with prostate cancer: an analysis of 749 cases. Int. J. Urol. off. J. Jpn. Urol. Assoc. 3, 462–467 (1996). https://doi.org/10.1111/j.1442-2042.1996.tb00577.x
National Institute for Health and Care Excellence (NICE): TA259: Abiraterone for castration-resistant metastatic prostate cancer previously treated with a docetaxel-containing regimen (2012). https://www.nice.org.uk/guidance/ta259/history
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020)
Rohatgi, A.: WebPlotDigitizer. Version 4.5. Pacifica, CA (2020). Available at: https://automeris.io/WebPlotDigitizer
Soikkeli, F., Hashim, M., Ouwens, M., Postma, M., Heeg, B.: Extrapolating survival data using historical trial-based a priori distributions. Value Health. 22, 1012–1017 (2019). https://doi.org/10.1016/j.jval.2019.03.017
Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A.: The deviance information criterion: 12 years on. J. r. Stat. Soc. Ser. B Stat. Methodol. 76, 485–493 (2014). https://doi.org/10.1111/rssb.12062