独立癌症中心博士后生物医学科学家统计学课程的建设与教学

S. Patil, J. Satagopan
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摘要

在过去的十年中,癌症研究界在描述人类肿瘤的基因组特征方面取得了相当大的进展。因此,对癌症分子驱动因素的了解大大增加了。尽管肿瘤学社区希望这将导致更有效的治疗方法,但我们将实验室癌症研究转化为临床成功的能力非常低-只有5%的药物在实验室研究中证明具有抗癌活性,然后在III期临床试验中取得成功。造成如此高失败率的原因有很多。除了将实验动物治疗推广到人类的生物学困难之外,导致这一低比率的其他因素包括对统计设计和分析概念、解释和报告方法的误用或误解。认识到这些广泛流行和可纠正的问题的严重性,美国国立卫生研究院呼吁整个生物医学研究企业充分参与,实施所需的资源,以改善和维持统计严谨性,并成功转化临床前癌症研究。为此,我们为早期临床前癌症研究人员(即博士后研究人员)开发了一门统计学课程,这些研究人员在纪念斯隆凯特琳癌症中心以及更广泛的研究社区进行实验室研究。我们建议的课程为期八周,每周上一堂60-90分钟的课,包括实验设计、数据分析和参与式对话等实践活动。我们对课程进行了评估,并计划将所有资源提供给更广泛的统计和癌症研究团体。在本专栏中,我们将讨论在一个独立的癌症中心为博士后生物医学科学家建立这样一个统计学课程所做的努力。我们分享课程开发的经验,并从学生、他们的实验室领导和合作生物统计学家的角度提供见解。
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Building and Teaching a Statistics Curriculum for Post-Doctoral Biomedical Scientists at a Free-Standing Cancer Center
In the past decade, the cancer research community has made considerable progress in characterizing the genomic features of human tumors. Knowledge of molecular drivers of cancer has therefore increased greatly. Although the oncology community hoped that this would lead to more effective therapies, our ability to translate laboratory cancer research into clinical success has been remarkably low –only 5% of the agents demonstrated to have anticancer activity in laboratory studies went on to achieve success in phase III clinical trials. Many factors are responsible for this high percentage of failures. In addition to the biological difficulties of generalizing lab animal treatments to humans, other factors involved in driving this low rate include misuse or misunderstanding of statistical design and analysis concepts, interpretation and reporting methods. Recognizing the seriousness of these widely prevalent and correctable issues, the National Institutes of Health has called for the full engagement of the entire biomedical research enterprise to implement the resources needed to improve and sustain the statistical rigor and successful translation of preclinical cancer research. To this end, we developed a statistics curriculum for early-career preclinical cancer researchers (i.e., postdoctoral researchers) conducting laboratory research at Memorial Sloan Kettering Cancer Center as well as the broader research community. Our proposed curriculum was delivered over an eight-week period with one 60-90-minute class per week and included hands-on activities with experimental designs, data analysis and participatory dialogues. We developed an evaluation of the curriculum and have plans to make all resources available to the broader statistics and cancer research communities. In this column, we discuss our efforts to build such a statistics curriculum for post-doctoral biomedical scientists at a free-standing cancer center. We share experiences in the development of the curriculum and provide insights from the perspective of students, their laboratory leads, and collaborative biostatisticians.
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