Combining Real-World and Randomized Control Trial Data Using Data-Adaptive Weighting via the On-Trial Score

References

• Burcu, M., Dreyer, N. A., Franklin, J. M., Blum, M. D., Critchlow, C. W., Perfetto, E. M., and Zhou, W. (2020), “Real-World Evidence to Support Regulatory Decision-Making for Medicines: Considerations for External Control Arms,” Pharmacoepidemiology and Drug Safety, 29, 1228–1235. DOI: 10.1002/pds.4975.
   PubMed Web of Science ®Google Scholar
• Carrigan, G., Whipple, S., Taylor, M. D., Torres, A. Z., Gossai, A., Arnieri, B., Tucker, M., Hofmeister, P. P., Lambert, P., Griffith, S. D., and Capra, W. P. (2019), “An Evaluation of the Impact of Missing Deaths on Overall Survival Analyses of Advanced Non-small Cell Lung Cancer Patients Conducted in an Electronic Health Records Database,” Pharmacoepidemiology and Drug Safety, 28, 572–581. DOI: 10.1002/pds.4758.
   PubMed Web of Science ®Google Scholar
• Chen, M.-H., Ibrahim, J. G., and Shao, Q.-M. (2000), “Power Prior Distributions for Generalized Linear Models,” Journal of Statistical Planning and Inference, 84, 121–137. DOI: 10.1016/S0378-3758(99)00140-8.
   Web of Science ®Google Scholar
• de Bono, J. S., Logothetis, C. J., Molina, A., Fizazi, K., North, S., Chu, L., Chi, K. N., Jones, R. J., Goodman, O. B., Jr., 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., and Scher, H. I. (2011), “Abiraterone and Increased Survival in Metastatic Prostate Cancer,” The New England Journal of Medicine, 364, 1995–2005. DOI: 10.1056/NEJMoa1014618.
   PubMed Web of Science ®Google Scholar
• Duan, Y., and Ye, K. (2008), “Normalized Power Prior Bayesian Analysis,” The Univeristy of Texas at San Antonio, College of Business working paper series.
   Google Scholar
• Egevad, L., Granfors, T., Karlberg, L., Bergh, A., and Stattin, P. (2002), “Prognostic Value of the Gleason Score in Prostate Cancer,” BJU International, 89, 538–542. DOI: 10.1046/j.1464-410X.2002.02669.x.
   PubMed Web of Science ®Google Scholar
• Getz, K., Mamtani, R., and Hubbard, R. A. (2021), “Integrating Real World Data and Clinical Trial Results Using Survival Data Reconstruction and Marginal Moment-Balancing Weights,” Journal of Biopharmaceutical Statistics, pp. 1–13. DOI: 10.1080/10543406.2021.1998097.
   Web of Science ®Google Scholar
• Ibrahim, J. G., and Chen, M.-H. (2000), “Power Prior Distributions for Regression Models,” Statistical Science, 15, 46–60.
   Web of Science ®Google Scholar
• Jemielita, T., Li, K., Piperdi, B., Zhou, W., Burke, T., and Chen, C. (2021), “Overall Survival with Second-Line Pembrolizumab in Patients with Non–Small-Cell Lung Cancer: Randomized Phase III Clinical Trial Versus Propensity-Adjusted Real-World Data,” JCO Clinical Cancer Informatics, 5, 56–65. DOI: 10.1200/CCI.20.00099.
   PubMed Web of Science ®Google Scholar
• Kan, H.-C., Hou, C.-P., Lin, Y.-H., Tsui, K.-H., Chang, P.-L., and Chen, C.-L. (2017), “Prognosis of Prostate Cancer with Initial Prostate-Specific Antigen >1000 ng/mL at Diagnosis,” OncoTargets and Therapy, 10, 2943–2949. DOI: 10.2147/OTT.S134411.
   PubMed Web of Science ®Google Scholar
• Lin, J., Gamalo-Siebers, M., and Tiwari, R. (2019), “Propensity-Score-Based Priors for Bayesian Augmented Control Design,” Pharmaceutical Statistics, 18, 223–238. DOI: 10.1002/pst.1918.
   PubMed Web of Science ®Google Scholar
• Mitra, R., and Reiter, J. P. (2016), “A Comparison of Two Methods of Estimating Propensity Scores After Multiple Imputation,” Statistical Methods in Medical Research, 25, 188–204. DOI: 10.1177/0962280212445945.
   PubMed Web of Science ®Google Scholar
• Mori, K., Kimura, S., Parizi, M. K., Enikeev, D. V., Glybochko, P. V., Seebacher, V., Fajkovic, H., Mostafaei, H., Lysenko, I., and Janisch, F. (2019), “Prognostic Value of Lactate Dehydrogenase in Metastatic Prostate Cancer: A Systematic Review and Meta-Analysis,” Clinical Genitourinary Cancer, 17, 409–418. DOI: 10.1016/j.clgc.2019.07.009.
   PubMed Web of Science ®Google Scholar
• Nakasian, S., Rassen, J. A., and Franklin, J. (2017), “Effects of Expanding the Look-Back Period to all Available Data in the Assessment of Covariates: Effects of Expanding the Look-back Approach,” Pharmacoepidemiology and Drug Safety, 26, 890–899. DOI: 10.1002/pds.4210.
   PubMed Web of Science ®Google Scholar
• Neuenschwander, B., Branson, M., and Spiegelhalter, D. J. (2009), “A Note on the Power Prior,” Statistics in Medicine, 28, 3562–3566. DOI: 10.1002/sim.3722.
   PubMed Web of Science ®Google Scholar
• Pocock, S. J. (1976), “The Combination of Randomized and Historical Controls in Clinical Trials,” Journal of Chronic Diseases, 29, 175–188. DOI: 10.1016/0021-9681(76)90044-8.
   PubMedGoogle Scholar
• Ramsey, S. D., Adamson, B. J., Wang, X., Bargo, D., Baxi, S. S., Ghosh, S., and Meropol, N. J. (2020), “Using Electronic Health Record Data to Identify Comparator Populations for Comparative Effectiveness Research,” Journal of Medical Economics, 23, 1618–1622. DOI: 10.1080/13696998.2020.1840113.
   PubMed Web of Science ®Google Scholar
• Sachdeva, A., Tiwari, R. C., and Guha, S. (2021), “A Novel Approach to Augment Single-Arm Clinical Studies with Real-World Data,” Journal of Biopharmaceutical Statistics, pp. 1–17. DOI: 10.1080/10543406.2021.2011902.
   Web of Science ®Google Scholar
• Sharma, U., Pal, D., and Prasad, R. (2014), “Alkaline Phosphatase: An Overview,” Indian Journal of Clinical Biochemistry: IJCB, 29, 269–278. DOI: 10.1007/s12291-013-0408-y.
   PubMedGoogle Scholar
• Schmidli, H., Häring, D. A., Thomas, M., Cassidy, A., Weber, S., and Bretz, F. (107), “Beyond Randomized Clinical Trials: Use of External Controls,” Clinical Pharmacology & Therapeutics, 107, 806–816. DOI: 10.1002/cpt.1723.
   Google Scholar
• Sørensen, J. B., Klee, M., Palshof, T., and Hansen, H. H. (1993), “Performance Status Assessment in Cancer Patients. An Inter-Observer Variability Study,” British Journal of Cancer, 67, 773–775. DOI: 10.1038/bjc.1993.140.
   PubMed Web of Science ®Google Scholar
• The YODA Project, available at https://yoda.yale.edu/.
   Google Scholar
• Tucci, M., Scagliotti, G. V., and Vignani, F. (2015), “Metastatic Castration-Resistant Prostate Cancer: Time for Innovation,” Future Oncology, 11, 91–106. DOI: 10.2217/fon.14.145.
   PubMed Web of Science ®Google Scholar
• Van der Laan, M. J., Polley, E. C., and Hubbard, A. E. (2007), “Super Learner,” Statistical Applications in Genetics and Molecular Biology, 6, 1–21. DOI: 10.2202/1544-6115.1309.
   Web of Science ®Google Scholar
• Ventz, S., Lai, A., Cloughesy, T. F., Wen, P. Y., Trippa, L., and Alexander, B. M. (2019), “Design and Evaluation of an External Control Arm Using Prior Clinical Trials and Real-World Data,” Clinical Cancer Research, 25, 4993–5001. DOI: 10.1158/1078-0432.CCR-19-0820.
   PubMed Web of Science ®Google Scholar
• Wang, C., Lu, N., Chen, W.-C., Li, H., Tiwari, R., Xu, Y., and Yue, L. Q. (2019), “Propensity Score-Integrated Composite Likelihood Approach for Incorporating Real-World Evidence in Single-Arm Clinical Studies,” Journal of Biopharmaceutical Statistics, 30, 495–507. DOI: 10.1080/10543406.2019.1684309.
   PubMed Web of Science ®Google Scholar
• Wang, X., Suttner, L., Jemielita, T., and Li, X. (2021), “Propensity Score-Integrated Bayesian Prior Approaches for Augmented Control Designs: A Simulation Study,” Journal of Biopharmaceutical Statistics, 1–21, 2021. DOI: 10.1080/10543406.2021.2011743.
   Web of Science ®Google Scholar