Joly Pierre, Commenges Daniel, Helmer Catherine, Letenneur Luc
ISPED, Université de Bordeaux II, 146, rue Léo Saignat, 33076 Bordeaux Cedex, France.
Biostatistics. 2002 Sep;3(3):433-43. doi: 10.1093/biostatistics/3.3.433.
We consider the problem of estimating the intensity functions for a continuous time 'illness-death' model with intermittently observed data. In such a case, it may happen that a subject becomes diseased between two visits and dies without being observed. Consequently, there is an uncertainty about the precise number of transitions. Estimating the intensity of transition from health to illness by survival analysis (treating death as censoring) is biased downwards. Furthermore, the dates of transitions between states are not known exactly. We propose to estimate the intensity functions by maximizing a penalized likelihood. The method yields smooth estimates without parametric assumptions. This is illustrated using data from a large cohort study on cerebral ageing. The age-specific incidence of dementia is estimated using an illness-death approach and a survival approach.
我们考虑了利用间歇性观测数据估计连续时间“疾病-死亡”模型强度函数的问题。在这种情况下，可能会出现受试者在两次就诊之间患病并在未被观察到的情况下死亡的情况。因此，关于确切的转变次数存在不确定性。通过生存分析（将死亡视为删失）来估计从健康到患病的转变强度会产生向下的偏差。此外，状态之间转变的日期并不确切知晓。我们建议通过最大化惩罚似然来估计强度函数。该方法在无参数假设的情况下产生平滑估计。这通过一项关于脑老化的大型队列研究的数据进行了说明。使用疾病-死亡方法和生存方法估计了特定年龄的痴呆发病率。
