Department of Biostatistics, Brown University, 121 South Main Street, Providence, RI 02903, USA.
Biostatistics. 2023 Jul 14;24(3):728-742. doi: 10.1093/biostatistics/kxac011.
Prediction models are often built and evaluated using data from a population that differs from the target population where model-derived predictions are intended to be used in. In this article, we present methods for evaluating model performance in the target population when some observations are right censored. The methods assume that outcome and covariate data are available from a source population used for model development and covariates, but no outcome data, are available from the target population. We evaluate the finite sample performance of the proposed estimators using simulations and apply the methods to transport a prediction model built using data from a lung cancer screening trial to a nationally representative population of participants eligible for lung cancer screening.
预测模型通常是使用与目标人群不同的人群数据构建和评估的,而这些模型的预测结果旨在用于目标人群。在本文中,我们提出了在某些观测值被右删失时,在目标人群中评估模型性能的方法。这些方法假设结局和协变量数据可从用于模型开发的源人群中获得,并且可从目标人群中获得协变量,但无法获得结局数据。我们使用模拟评估了所提出估计量的有限样本性能,并将这些方法应用于将使用肺癌筛查试验数据构建的预测模型传输到有资格接受肺癌筛查的全国代表性参与者人群中。