Abstract
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.