Targeted Clinical Trials

作者: Stephen L. George , Xiaofei Wang

DOI: 10.1007/978-1-4614-0140-7_7

关键词:

摘要: Randomized clinical trials (RCTs) are the most reliable scientific tool for identification of safe and effective therapies cancer. Indeed, it can be argued that such indispensible this purpose. The design analysis these generally aimed at differences in overall population parameters. compare outcome measures as response rates, progression-free survival (PFS), disease-free survival, (OS) patients assumed to representative under study, determined by specified eligibility criteria on trial. Although no assumption homogeneity studied or relative effects treatment is required, major conclusions and, new agents, subsequent regulatory approvals based population. For example, a conclusion might some therapy better than standard with respect PFS, if observed hazard ratio significantly less unity (in favor therapy), even “average” benefit other scales (e.g., difference PFS medians) quite small. This legitimate reasonable approach, one has over time yielded advances cancer treatments. However, sufficiently broad, desirable feature traditional RCTs, unrecognized heterogeneity may have substantial effect power trial (Betensky et al. 2002; Zhang 2006). there reasons suspect study only subset any due solely largely results subset.If true reliably identified beforehand, benefits could accrue targeting rather broader one. Benefits include more efficient trials, avoidance unnecessary who will not from treatment, faster drug development times, so on. concept “personalized” medicine predicated existence exploitable patient differences. Of course, population, assuming exists, easy, but critical importance (George 2008).

参考文章(68)
Daniel J. Sargent, Barbara A. Conley, Carmen Allegra, Laurence Collette, Clinical Trial Designs for Predictive Marker Validation in Cancer Treatment Trials Journal of Clinical Oncology. ,vol. 23, pp. 2020- 2027 ,(2005) , 10.1200/JCO.2005.01.112
Stephen L. George, Statistical Issues in Translational Cancer Research Clinical Cancer Research. ,vol. 14, pp. 5954- 5958 ,(2008) , 10.1158/1078-0432.CCR-07-4537
David P. Byar, Assessing apparent treatment—covariate interactions in randomized clinical trials Statistics in Medicine. ,vol. 4, pp. 255- 263 ,(1985) , 10.1002/SIM.4780040304
E. Vittinghoff, D. C. Bauer, Case‐Only Analysis of Treatment–Covariate Interactions in Clinical Trials Biometrics. ,vol. 62, pp. 769- 776 ,(2006) , 10.1111/J.1541-0420.2006.00511.X
Boris Freidlin, Edward L. Korn, Robert Gray, Alison Martin, Multi-Arm Clinical Trials of New Agents: Some Design Considerations Clinical Cancer Research. ,vol. 14, pp. 4368- 4371 ,(2008) , 10.1158/1078-0432.CCR-08-0325
Carolyn D. Seib, Flavio G. Rocha, Dick G. Hwang, Brent T. Shoji, Gastrosplenic Fistula From Hodgkin's Lymphoma Journal of Clinical Oncology. ,vol. 27, ,(2009) , 10.1200/JCO.2008.21.7695
Sumithra J Mandrekar, Axel Grothey, Matthew P Goetz, Daniel J Sargent, Clinical Trial Designs for Prospective Validation of Biomarkers American Journal of Pharmacogenomics. ,vol. 5, pp. 317- 325 ,(2005) , 10.2165/00129785-200505050-00004