An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records. Recently, several neural-network based solutions have been proposed, some of which are binary classifiers. Parametric, distribution-free approaches which make full use of survival time and censoring status have not received much attention. We present deep conditional transformation models (DCTMs) for survival outcomes as a unifying approach to parametric and semiparametric survival analysis. DCTMs allow the specification of non-linear and non-proportional hazards for both tabular and non-tabular data and extend to all types of censoring and truncation. On real and semi-synthetic data, we show that DCTMs compete with state-of-the-art DL approaches to survival analysis.

翻译：越来越多的临床试验都具有时间到活动的结果和记录非临床病人数据,例如磁共振成像或电子健康记录形式的文本数据;最近,提出了若干基于神经网络的解决办法,其中一些是二元分类法;充分利用生存时间和审查地位的参数性、不分发方法没有受到多少注意;我们提出了生存结果的深度有条件转换模型(DCTMs),作为参数和半参数生存分析的统一方法;DCTMs允许对表单数据和非表单数据的非线性和非比例性危险作出说明,并扩大到所有类型的检查和截网;关于真实和半合成数据,我们表明DCTMs与最新的DL生存分析方法竞争。