Implementation of comparative effectiveness research in personalized medicine applications in oncology: current and future perspectives

References

• Institute of Medicine (IOM). Initial National Priorities for Comparative Effectiveness Research. Washington, DC: The National Academies Press; 2009.
   Google Scholar
• Annemans L, Redekop K, Payne K. Current methodological issues in the economic assessment of personalized medicine. Value Health. Sep 2013;16(6 Suppl):S20–S26.
   PubMed Web of Science ®Google Scholar
• Conti R, Veenstra DL, Armstrong K, Lesko LJ, Grosse SD. Personalized medicine and genomics: challenges and opportunities in assessing effectiveness, cost-effectiveness, and future research priorities. Med Decis Making. 2010;30(3):328–340.
   Web of Science ®Google Scholar
• Epstein R, Teagarden JR. Comparative effectiveness and personalized medicine: evolving together or apart? Health Aff (Millwood). 2010; 29(10):1783–1787.
   Web of Science ®Google Scholar
• Garber AM, Tunis SR. Does comparative-effectiveness research threaten personalized medicine? N Engl J Med. 2009;360(19):1925–1927.
   PubMed Web of Science ®Google Scholar
• Ramsey SD, Veenstra D, Tunis SR, Garrison L, Crowley JJ, Baker LH. How Comparative Effectiveness Research Can Help Advance “Personalized Medicine” In Cancer Treatment. Health Aff (Millwood). 2011;30(12):2259–2268.
   Web of Science ®Google Scholar
• Tuna M, Amos CI. Genomic sequencing in cancer. Cancer Lett. 2013;340(2):161–170.
   Web of Science ®Google Scholar
• Downing GJ. Key aspects of health system change on the path to personalized medicine. Transl Res. 2009;154(6):272–276.
   Web of Science ®Google Scholar
• Schleidgen S, Klingler C, Bertram T, Rogowski WH, Marckmann G. What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med Ethics. 2013;14:55.
   PubMed Web of Science ®Google Scholar
• Rubin R. Precision medicine: the future or simply politics? JAMA. 2015;313(11):1089–1091.
   Web of Science ®Google Scholar
• Espinoza MA, Manca A, Claxton K, Sculpher MJ. The value of heterogeneity for cost-effectiveness subgroup analysis: conceptual framework and application. Med Decis Making. 2014;34(8):951–964.
   PubMed Web of Science ®Google Scholar
• Luce BR, Drummond M, Jönsson B, et al. EBM, HTA, and CER: clearing the confusion. Milbank Q. 2010;88(2):256–276.
   PubMed Web of Science ®Google Scholar
• Lyman GH, Hirsch B. Comparative effectiveness research and genomic personalized medicine. Personalized Medicine. 2010;7(3):223–227.
   Web of Science ®Google Scholar
• Petticrew M, Roberts H. Evidence, hierarchies, and typologies: horses for courses. J Epidemiol Community Health. 2003;57(7):527–529.
   PubMed Web of Science ®Google Scholar
• Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Med Decis Making. 1991;11(2):88–94.
   PubMed Web of Science ®Google Scholar
• Ginsburg GS, Kuderer NM. Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: a common bond. J Clin Oncol. 2012;30(34):4233–4242.
   PubMed Web of Science ®Google Scholar
• Khoury MJ, Berg A, Coates R, Evans J, Teutsch SM, Bradley LA. The Evidence Dilemma In Genomic Medicine. Health Aff (Millwood). 2008;27(6):1600–1611.
   Web of Science ®Google Scholar
• Towse A, Garrison LP Jr. Economic incentives for evidence generation: promoting an efficient path to personalized medicine. Value Health. 2013;16(6 Suppl):S39–S43.
   PubMed Web of Science ®Google Scholar
• Deverka PA, Schully SD, Ishibe N, et al. Stakeholder assessment of the evidence for cancer genomic tests: insights from three case studies. Genet Med. 2012;14(7):656–662.
   Web of Science ®Google Scholar
• Phillips KA, Liang S-Y, van Bebber S; Canpers Research Group. Challenges to the translation of genomic information into clinical practice and health policy: Utilization, preferences and economic value. Curr Opin Mol Ther. 200810(3):260–266.
   Google Scholar
• van Til JA, IJzerman MJ. Why Should Regulators Consider Using Patient Preferences in Benefit-risk Assessment? PharmacoEconomics. 2014;32(1):1–4.
   Web of Science ®Google Scholar
• Severin F, Schmidtke J, Mühlbacher A, Rogowski WH. Eliciting preferences for priority setting in genetic testing: a pilot study comparing best-worst scaling and discrete-choice experiments. Eur J Hum Genet. 2013;21(11):1202–1208.
   PubMed Web of Science ®Google Scholar
• Groothuis-Oudshoorn CG, Fermont JM, van Til JA, IJzerman MJ. Public stated preferences and predicted uptake for genome- based colorectal cancer screening. BMC Med Inform Decis Mak. 2014;14:18.
   Web of Science ®Google Scholar
• Printz C. I-SPY 2 may change how clinical trials are conducted. Cancer. 2013;119(11):1925–1927.
   PubMed Web of Science ®Google Scholar
• Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA, Esserman LJ. I-SPY 2: An Adaptive Breast Cancer Trial Design in the Setting of Neoadjuvant Chemotherapy. Clin Pharmacol Ther. 2009; 86(1):97–100.
   PubMed Web of Science ®Google Scholar
• Schilsky RL. Personalized medicine in oncology: the future is now. Nat Rev Drug Discov. 2010;9(5):363–366.
   PubMed Web of Science ®Google Scholar
• Ligthart ST, Bidard FC, Decraene C, et al. Unbiased quantitative assessment of Her-2 expression of circulating tumor cells in patients with metastatic and non-metastatic breast cancer. Ann Oncol. 2013;24(5):1231–1238.
   PubMed Web of Science ®Google Scholar
• Mao C, Wu XY, Yang ZY, et al. Concordant analysis of KRAS, BRAF, PIK3CA mutations, and PTEN expression between primary colorectal cancer and matched metastases. Sci Rep. 2015;5:8065.
   PubMed Web of Science ®Google Scholar
• Magbanua MJ, Wolf DM, Yau C, et al. Serial expression analysis of breast tumors during neoadjuvant chemotherapy reveals changes in cell cycle and immune pathways associated with recurrence and response. Breast Cancer Res. 2015;17:73.
   PubMed Web of Science ®Google Scholar
• Nalejska E, Maczynska E, Lewandowska MA. Prognostic and predictive biomarkers: tools in personalized oncology. Mol Diagn Ther. 2014; 18(3):273–284.
   PubMed Web of Science ®Google Scholar
• Rubin DB. For objective causal inference, design trumps analysis. Annals of Applied Statistics. 2008;2(3):808–840.
   Google Scholar
• Rubin DB. Causal Inference Using Potential Outcomes. Journal of the American Statistical Association. 2005;100(469):322–331.
   Web of Science ®Google Scholar
• Ma J, Hobbs BP, Stingo FC. Statistical methods for establishing personalized treatment rules in oncology. BioMed Research International. Epub 2015 Feb 9.
   Google Scholar
• Murphy SA. Optimal dynamic treatment regimes. J Royal Statistical Soc B. 2003;65(2):331–366.
   Google Scholar
• Lavori PW, Dawson R. Dynamic treatment regimes: practical design considerations. Clin Trials. 2004;1(1):9–20.
   PubMedGoogle Scholar
• Chakraborty B, Murphy SA. Dynamic Treatment Regimes. Annu Rev Stat Appl. 2014;1:447–464.
   PubMed Web of Science ®Google Scholar
• Corander J, Aittokallio T, Ripatti S, Kaski S. The rocky road to personalized medicine: computational and statistical challenges. Personalized Medicine. 2012;9(2):109–114.
   Web of Science ®Google Scholar
• Ramsey SD, Barlow WE, Gonzalez-Angulo AM, et al. Integrating comparative effectiveness design elements and endpoints into a phase III, randomized clinical trial (SWOG S1007) evaluating oncotypeDX-guided management for women with breast cancer involving lymph nodes. Contemporary Clinical Trials. 2013;34(1):1–9.
   PubMed Web of Science ®Google Scholar
• Leunis A, Redekop WK, van Montfort KA, Löwenberg B, Uyl-de Groot CA. The development and validation of a decision-analytic model representing the full disease course of acute myeloid leukemia. Pharmacoeconomics. 2013;31(7):605–621.
   Web of Science ®Google Scholar
• Collins B. Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats. Pharmacoeconomics. Epub 2015 Jun 21.
   Google Scholar
• Pollack CE, Lemke KW, Roberts E, Weiner JP. Patient sharing and quality of care: measuring outcomes of care coordination using claims data. Med Care. 2015;53(4):317–323.
   Web of Science ®Google Scholar
• Crown WH. Potential application of machine learning in health outcomes research and some statistical cautions. Value Health. 2015;18(2):137–140.
   Web of Science ®Google Scholar
• Giordano SH. Comparative effectiveness research in cancer with observational data. Am Soc Clin Oncol Educ Book. 2015;35:e330–e335.
   Google Scholar
• Baker SG, Kramer BS, Lindeman KS. Latent class instrumental variables: a clinical and biostatistical perspective. Stat Med. Epub 2015 Aug 4.
   Google Scholar
• Marshall DA, Burgos-Liz L, IJzerman MJ, et al. Applying Dynamic Simulation Modeling Methods in Health Care Delivery Research-The SIMULATE Checklist: Report of the ISPOR Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015;18(1):5–16.
   PubMed Web of Science ®Google Scholar
• Marshall DA, Burgos-Liz L, IJzerman MJ, et al. Selecting a Dynamic Simulation Modeling Method for Health Care Delivery Research-Part 2: Report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force. Value Health. 2015;18(2):147–160.
   Web of Science ®Google Scholar