RELATION OF POOLED LOGISTIC-REGRESSION TO TIME-DEPENDENT COX REGRESSION-ANALYSIS - THE FRAMINGHAM HEART-STUDY
RELATION OF POOLED LOGISTIC-REGRESSION TO TIME-DEPENDENT COX REGRESSION-ANALYSIS - THE FRAMINGHAM HEART-STUDY
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DOI：
10.1002/sim.4780091214
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发表时间：
1990-12-01
影响因子：
2
通讯作者：
KANNEL, WB
中科院分区：
文献类型：
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作者：
DAGOSTINO, RB;LEE, ML;KANNEL, WB
心脏研究数据的标准分析是一种广义的人-年方法，其中每两年测量一次风险因素或协变量，并在这些测量时间之间进行随访，以观察心血管疾病等事件的发生。将多个时间间隔的观察结果合并到单个样本中，并采用逻辑回归将风险因素与事件的发生联系起来。我们发现，这种合并的逻辑回归是接近的时间依赖的协变量考克斯回归分析。涵盖各种样本量和事件比例的数值例子显示了这种关系在典型的情况下的Fracket研究的密切程度。附录中给出了关系的证明和必要条件。
A standard analysis of the Framingham Heart Study data is a generalized person-years approach in which risk factors or covariates are measured every two years with a follow-up between these measurement times to observe the occurrence of events such as cardiovascular disease. Observations over multiple intervals are pooled into a single sample and a logistic regression is employed to relate the risk factors to the occurrence of the event. We show that this pooled logistic regression is close to the time dependent covariate Cox regression analysis. Numerical examples covering a variety of sample sizes and proportions of events display the closeness of this relationship in situations typical of the Framingham Study. A proof of the relationship and the necessary conditions are given in the Appendix.