ABSTRACT

Many time-to-event models have been developed for left-truncated and right-censored (LTRC) data, which arise in many applications involving follow-up studies. However, there is no work on evaluating the prediction accuracy of the time-to-event models for LTRC data. This paper develops two novel weighted prediction summary measures for a nonlinear prediction function with LTRC data. They are based on a weighted variance decomposition and a weighted prediction error decomposition, by the inverse probability weighting technique. The resulting measures are shown to be consistent and asymptotically normal. Simulation studies are conducted to evaluate their good finite sample performance. An empirical application to the Channing House data set illustrates the methodology.

摘要

已经为左截断和右删失 (LTRC) 数据开发了许多事件时间模型，这些模型出现在许多涉及后续研究的应用中。但是，没有关于评估 LTRC 数据的时间到事件模型的预测准确性的工作。本文为具有 LTRC 数据的非线性预测函数开发了两种新颖的加权预测汇总度量。它们基于加权方差分解和加权预测误差分解，通过逆概率加权技术。结果表明，结果是一致且渐近正态的。进行模拟研究以评估其良好的有限样本性能。Channing House 数据集的实证应用说明了该方法。