Gauthier J, Wu Q V, Gooley T A
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Medicine, Division of Medical Oncology, University of Washington, Seattle, WA, USA.
Bone Marrow Transplant. 2020 Apr;55(4):675-680. doi: 10.1038/s41409-019-0679-x. Epub 2019 Oct 1.
We are pleased to add this typescript to the Bone Marrow Transplantation Statistics Series. We realize the term cubic splines may be a bit off-putting to some readers, but stay with us and don't get lost in polynomial equations. What the authors describe is important conceptually and in practice. Have you ever tried to buy a new pair of hiking boots? Getting the correct fit is critical; shoes that are too small or too large will get you in big trouble! Now imagine if hiking shoes came in only 2 sizes, small and large, and your foot size was somewhere in between. You are in trouble. Sailing perhaps?Transplant physicians are often interested in the association between two variables, say pre-transplant measurable residual disease (MRD) test state and an outcome, say cumulative incidence of relapse (CIR). We typically reduce the results of an MRD test to a binary, negative or positive, often defined by an arbitrary cut-point. However, MRD state is a continuous biological variable, and reducing it to a binary discards what may be important, useful data when we try to correlate it with CIR. Put otherwise, we may miss the trees from the forest.Another way to look at splines is a technique to make smooth curves out of irregular data points. Consider, for example, trying to describe the surface of an egg. You could do it with a series of straight lines connecting points on the egg surface but a much better representation would be combining groups of points into curves and then combining the curves. To prove this try drawing an egg using the draw feature in Microsoft Powerpoint; you are making splines.Gauthier and co-workers show us how to use cubic splines to get the maximum information from data points, which may, unkindly, not lend themselves to dichotomization or a best fit line. Please read on. We hope readers will find their typescript interesting and exciting, and that it will give them a new way to think about how to analyse data. And no, a spline is not a bunch of cactus spines. Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin and CIBMTR.
我们很高兴将这份文稿纳入《骨髓移植统计系列》。我们意识到“三次样条”这个术语可能会让一些读者感到有些反感，但请跟随我们，不要迷失在多项式方程中。作者所描述的内容在概念和实践上都很重要。你有没有试过买一双新的徒步靴？选到合适的尺码至关重要；太小或太大的鞋子都会给你带来大麻烦！现在想象一下，如果徒步鞋只有两种尺码，小码和大码，而你的脚尺码介于两者之间。那你就麻烦了。也许去航海？移植医生通常对两个变量之间的关联感兴趣，比如说移植前可测量残留病（MRD）检测状态和一个结果，比如说复发累积发生率（CIR）。我们通常将MRD检测结果简化为二元的，阴性或阳性，通常由一个任意的切点来定义。然而，MRD状态是一个连续的生物学变量，当我们试图将其与CIR相关联时，将其简化为二元会丢弃可能重要且有用的数据。换句话说，我们可能只见树木不见森林。看待样条的另一种方式是一种从不规则数据点生成平滑曲线的技术。例如，考虑尝试描述一个鸡蛋的表面。你可以用一系列连接鸡蛋表面各点的直线来做到这一点，但更好的表示方法是将点组合成曲线，然后将曲线组合起来。为了证明这一点，试着用微软PowerPoint中的绘图功能画一个鸡蛋；你就是在绘制样条。高蒂埃及其同事向我们展示了如何使用三次样条从数据点中获取最大信息，这些数据点可能不适合二分法或最佳拟合线。请继续阅读。我们希望读者会发现他们的文稿有趣且令人兴奋，并且它会给他们一种思考如何分析数据的新方法。而且，不，样条不是一堆仙人掌刺。伦敦帝国理工学院的罗伯特·彼得·盖尔以及威斯康星医学院和CIBMTR的张美捷
