Biometric Research Branch, National Cancer Institute, Bethesda, MD 20892-7434, USA.
Clin Trials. 2010 Oct;7(5):516-24. doi: 10.1177/1740774510366454. Epub 2010 Mar 25.
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 [Simon R. An agenda for clinical trials: clinical trials in the genomic era. Clin Trials 2004; 1:468-70, Simon R. New challenges for 21st century clinical trials. Clin Trials 2007; 4: 167-9.].
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 Some of the key assumptions upon which current methods are based are no longer valid.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.
生物技术和基因组学的发展使得生物统计学家更加关注预测问题。这为预测建模带来了许多令人兴奋的发展，其中变量的数量大于病例的数量。人类疾病的异质性和新的特征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，用对大多数患者没有益处的方案治疗广泛的人群，对于昂贵的分子靶向治疗来说，在经济上是不可持续的。人类疾病的既定分子异质性要求开发新的随机临床试验设计和分析范式，作为预测医学的可靠基础[Simon R.临床试验议程：基因组时代的临床试验。Clin Trials 2004; 1:468-70，Simon R. 21 世纪临床试验的新挑战。Clin Trials 2007; 4: 167-9]。
我们回顾了具有候选预测生物标志物的新治疗方法的前瞻性设计。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类错误，又为预测哪些患者最有可能或最不可能从新方案中获益提供了可靠的内部验证基础。
开发具有预测生物标志物的新治疗方法来识别最有可能或最不可能获益的患者，使药物开发更加复杂。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，并明显有利于减少最终接受昂贵药物的患者数量，这些药物使他们面临不良事件的风险，但没有获益。这种方法对于控制医疗保健的社会支出也具有巨大的潜在价值。具有预测生物标志物的治疗方法的开发需要对临床试验的设计和分析的标准范式进行重大改变。当前方法所依据的一些关键假设不再有效。除了回顾各种新的临床试验设计来共同开发治疗方法和预测生物标志物外，我们还概述了一种基于预测的随机临床试验分析方法。这是一种非常结构化的方法，其使用需要仔细的前瞻性规划。它需要进一步发展，但可以作为新一代预测临床试验的基础，这些临床试验提供了医生和患者长期寻求的可靠个体化信息，但过去使用事后亚组分析无法提供这些信息。
