A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia
A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia
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DOI：
10.1093/biostatistics/3.3.433
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发表时间：
2002-09-01
期刊：
影响因子：
2.1
通讯作者：
Letenneur, L
中科院分区：
文献类型：
--
作者：
Joly, P;Commenges, D;Letenneur, L
考虑了具有间断观测数据的连续时间“病-死”模型的强度函数估计问题。在这种情况下，可能发生受试者在两次访视之间患病并在未观察到的情况下死亡。因此，转换的精确数量存在不确定性。通过生存分析（将死亡视为删失）来估计从健康到疾病的转变强度是向下偏的。此外，国家之间的过渡日期并不确切。我们建议通过最大化惩罚似然来估计强度函数。该方法产生光滑的估计没有参数假设。这是用一项关于大脑老化的大型队列研究的数据来说明的。使用疾病-死亡方法和生存方法估计痴呆的年龄特异性发病率。
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.