Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA.
Ann Epidemiol. 2011 Jul;21(7):517-25. doi: 10.1016/j.annepidem.2011.04.001.
To examine and model the dynamic association of BMI and mortality in the Framingham Heart Study (FHS).
BMI is transformed to facilitate modeling curvature associations. Logistic models are used to demonstrate whether different conclusions may be obtained for the same group of individuals under different settings created from FHS. Time-dependent covariates Cox models are used to model the association of BMI and mortality when the proportional hazards assumptions for Cox models are violated.
Both the measurement time of BMI and the length of follow-up affect the conclusions obtained from logistic models, especially for men. Time-dependent covariates Cox models show that the association between BMI and mortality for men depends on the follow-up time, while that for women depends on the age of BMI measurement.
The association of BMI and mortality in FHS is a dynamic system that traditional analyses methods may lead to different conclusions for different study designs. This finding is consistent with the results of several other studies done from different perspectives, suggesting that the dynamic features demonstrated in FHS may apply to other populations. Advanced methods such as time-dependent covariates Cox models may be helpful for future analysis.
在弗雷明汉心脏研究（FHS）中检验和建立 BMI 与死亡率的动态关联模型。
BMI 转换以促进曲线关联建模。逻辑模型用于证明在 FHS 中创建的不同设置下，同一组个体是否可能得出不同的结论。当 Cox 模型的比例风险假设被违反时，时间依赖性协变量 Cox 模型用于对 BMI 和死亡率的关联进行建模。
BMI 的测量时间和随访时间都会影响逻辑模型得出的结论，尤其是对男性而言。时间依赖性协变量 Cox 模型显示，男性 BMI 和死亡率之间的关联取决于随访时间，而女性则取决于 BMI 测量的年龄。
FHS 中 BMI 和死亡率的关联是一个动态系统，传统的分析方法可能会导致不同的研究设计得出不同的结论。这一发现与其他几个从不同角度进行的研究结果一致，表明 FHS 中显示的动态特征可能适用于其他人群。时间依赖性协变量 Cox 模型等先进方法可能对未来的分析有所帮助。
