Lindsey J K
Biostatistics, Limburgs Universitair Centrum, Diepenbeek, Belgium.
Lifetime Data Anal. 1998;4(4):329-54. doi: 10.1023/a:1009681919084.
Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation. Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding non-parametric models.
在某些标准统计软件包中，现在可以通过最少的编程轻松拟合区间删失数据的参数模型。可以引入位置参数和离散参数的回归方程。还可以拟合有限混合模型，在右（或左）删失观测值上设置点质量，以考虑不可能发生事件（或已经发生事件）的个体。这种混合概率也可以遵循回归方程。在这里，针对三个严重删失数据的示例以及一组模拟数据，比较了基于九种不同分布的模型。我们发现，对于参数模型，区间删失通常可以忽略，并且在似然函数中可以使用区间中心的密度代替，尽管这种近似并不总是可靠的。在严重区间删失数据的情况下，参数模型的结论在分布假设变化时非常稳健，并且通常比相应的非参数模型提供更多信息。
