Harrell F E, Lee K L, Pollock B G
Clinical Biostatistics, Duke University Medical Center, Durham, NC 27710.
J Natl Cancer Inst. 1988 Oct 5;80(15):1198-202. doi: 10.1093/jnci/80.15.1198.
Multiple regression models are increasingly being applied to clinical studies. Such models are powerful analytic tools that yield valid statistical inferences and make reliable predictions if various assumptions are satisfied. Two types of assumptions made by regression models concern the distribution of the response variable and the nature or shape of the relationship between the predictors and the response. This paper addresses the latter assumption by applying a direct and flexible approach, cubic spline functions, to two widely used models: the logistic regression model for binary responses and the Cox proportional hazards regression model for survival time data.
多元回归模型越来越多地应用于临床研究。如果满足各种假设，此类模型是强大的分析工具，能够得出有效的统计推断并做出可靠的预测。回归模型所做的两类假设涉及响应变量的分布以及预测变量与响应之间关系的性质或形状。本文通过将一种直接且灵活的方法——三次样条函数——应用于两个广泛使用的模型来探讨后一种假设：用于二元响应的逻辑回归模型和用于生存时间数据的Cox比例风险回归模型。
