Joly P, Commenges D, Letenneur L
INSERM U 330, Université de Bordeaux II, France.
Biometrics. 1998 Mar;54(1):185-94.
The Cos model is the model of choice when analyzing survival data presenting only right censoring and left truncation. There is a need for methods that can accommodate more complex observation schemes involving general censoring and truncation. In addition, it is important in many epidemiological applications to have a smooth estimate of the hazard function. We show that the penalized likelihood approach gives a solution to these problems. The solution of the maximum of the penalized likelihood is approximated on a basis of splines. The smoothing parameter is estimated using approximate cross-validation; confidence bands can be given. A simulation study shows that this approach gives better results than the smoothed Nelson-Aalen estimator. We apply this method to the analysis of data from a large cohort study on cerebral aging. The age-specific incidence of dementia is estimated and risk factors of dementia studied.
当分析仅呈现右删失和左截断的生存数据时，Cos模型是首选模型。需要有能够适应涉及一般删失和截断的更复杂观察方案的方法。此外，在许多流行病学应用中，对风险函数进行平滑估计很重要。我们表明，惩罚似然方法为这些问题提供了一个解决方案。惩罚似然最大值的解在样条的基础上进行近似。使用近似交叉验证估计平滑参数；可以给出置信带。一项模拟研究表明，这种方法比平滑的Nelson-Aalen估计量给出更好的结果。我们将此方法应用于一项关于脑老化的大型队列研究的数据的分析。估计了特定年龄的痴呆发病率，并研究了痴呆的风险因素。
