Clinical trials for predictive medicine: new challenges and paradigms.
Clinical trials for predictive medicine: new challenges and paradigms.
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预测医学的临床试验：新的挑战和范式。
DOI：
10.1177/1740774510366454
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发表时间：
2010-10
期刊：
影响因子：
--
通讯作者：
Simon R
中科院分区：
文献类型：
--
作者：
Simon R
生物技术和基因组学的发展增加了生物统计学家对预测问题的关注。这导致了预测建模的许多令人兴奋的发展，其中变量的数量大于案例的数量。人类疾病的异质性和表征疾病的新技术为临床试验的设计和分析带来了新的机遇和挑战。在肿瘤学中，用不能使大多数患者受益的方案治疗广泛人群，用昂贵的分子靶向治疗剂在经济上不太可持续。已建立的人类疾病的分子异质性，需要开发新的范例的设计和分析的随机临床试验作为一个可靠的基础，预测医学。我们回顾了具有候选预测生物标志物的新疗法的开发的前瞻性设计。我们还概述了一种基于预测的随机临床试验分析方法，该方法既保留了I型错误，又为预测哪些患者最有可能或不可能从新方案中获益提供了可靠的内部验证基础。开发具有预测性生物标志物的新治疗方法，以识别最有可能或最不可能受益的患者，使药物开发变得更加复杂。但对于许多新的肿瘤药物来说，这是唯一基于科学的方法，应该会增加成功的机会。它还可能导致试验结果更加一致，并对减少最终接受昂贵药物的患者数量有明显的好处，这些药物使他们面临不良事件的风险，但没有好处。这种方法对于控制社会卫生保健支出也具有巨大的潜在价值。开发具有预测性生物标志物的治疗方法需要对临床试验设计和分析的标准范式进行重大改变。目前方法所依据的一些关键假设不再有效。除了审查各种新的临床试验设计，共同开发的治疗和预测生物标志物，我们已经概述了一个基于预测的方法来分析随机临床试验。这是一个非常结构化的方法，其使用需要仔细的前瞻性规划。它需要进一步的发展，但可以作为新一代的预测性临床试验的基础，提供各种可靠的个性化信息，医生和患者长期以来一直在寻求，但还没有从过去使用的事后子集分析。
Developments in biotechnology and genomics have increased the focus of biostatisticians on prediction problems. This has led to many exciting developments for predictive modeling where the number of variables is larger than the number of cases. Heterogeneity of human diseases and new technology for characterizing them presents new opportunities and challenges for the design and analysis of clinical trials. In oncology, treatment of broad populations with regimens that do not benefit most patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We have reviewed prospective designs for the development of new therapeutics with candidate predictive biomarkers. We have also outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from the new regimen. Developing new treatments with predictive biomarkers for identifying the patients who are most likely or least likely to benefit makes drug development more complex. But for many new oncology drugs it is the only science based approach and should increase the chance of success. It may also lead to more consistency in results among trials and has obvious benefits for reducing the number of patients who ultimately receive expensive drugs which expose them risks of adverse events but no benefit. This approach also has great potential value for controlling societal expenditures on health care. Development of treatments with predictive biomarkers requires major changes in the standard paradigms for the design and analysis of clinical trials. Some of the key assumptions upon which current methods are based are no longer valid. In addition to reviewing a variety of new clinical trial designs for co-development of treatments and predictive biomarkers, we have outlined a prediction based approach to the analysis of randomized clinical trials. This is a very structured approach whose use requires careful prospective planning. It requires further development but may serve as a basis for a new generation of predictive clinical trials which provide the kinds of reliable individualized information which physicians and patients have long sought, but which have not been available from the past use of post-hoc subset analysis.
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影响因子：
8.8
作者：
Peto, R;Pike, M C;Armitage, P;Breslow, N E;Cox, D R;Howard, S V;Mantel, N;McPherson, K;Peto, J;Smith, P G
通讯作者：
Smith, P G
影响因子：
11.5
作者：
Simon, Richard
通讯作者：
Simon, Richard
影响因子：
5
作者：
Pepe, MS;Janes, H;Newcomb, P
通讯作者：
Newcomb, P
影响因子：
11.5
作者：
Freidlin, Boris;Jiang, Wenyu;Simon, Richard
通讯作者：
Simon, Richard
影响因子：
8.8
作者：
Peto R;Pike MC;Armitage P;Breslow NE;Cox DR;Howard SV;Mantel N;McPherson K;Peto J;Smith PG
通讯作者：
Smith PG