Steingrimsson Jon Arni, Morrison Samantha
Department of Biostatistics, Brown University, Providence, Rhode Island, USA.
Stat Med. 2020 Jul 30;39(17):2339-2349. doi: 10.1002/sim.8542. Epub 2020 Apr 13.
Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. We develop a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the absence of censoring is replaced by a censoring unbiased transformation. The resulting class of algorithms can be used to estimate both survival probabilities and restricted mean survival. We show how the deep learning algorithms can be implemented by adapting software for uncensored data by using a form of response transformation. We provide comparisons of the proposed deep learning algorithms to existing risk prediction algorithms for predicting survival probabilities and restricted mean survival through both simulated datasets and analysis of data from breast cancer patients.
深度学习是一类机器学习算法，在构建风险预测模型方面很受欢迎。当观测值被删失时，结果只是部分可观测的，标准的深度学习算法不能直接应用。我们针对可能被删失的结果开发了一类新的深度学习算法。为了考虑删失情况，在无删失情况下使用的不可观测损失函数被一个删失无偏变换所取代。由此产生的算法类别可用于估计生存概率和受限平均生存时间。我们展示了如何通过使用一种响应变换形式来改编无删失数据的软件，从而实现深度学习算法。我们通过模拟数据集以及对乳腺癌患者数据的分析，将所提出的深度学习算法与现有的风险预测算法进行比较，以预测生存概率和受限平均生存时间。
