High-dimensional data are becoming increasingly common in the medical field as large volumes of patient information are collected and processed by high-throughput screening, electronic health records, and comprehensive genomic testing. Statistical models that attempt to study the effects of many predictors on survival typically implement feature selection or penalized methods to mitigate the undesirable consequences of overfitting. In some cases survival data are also left-truncated which can give rise to an immortal time bias, but penalized survival methods that adjust for left truncation are not commonly implemented. To address these challenges, we apply a penalized Cox proportional hazards model for left-truncated and right-censored survival data and assess implications of left truncation adjustment on bias and interpretation. We use simulation studies and a high-dimensional, real-world clinico-genomic database to highlight the pitfalls of failing to account for left truncation in survival modeling.

中文翻译：

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左截断和右删失生存数据的惩罚回归

随着通过高通量筛查、电子健康记录和综合基因组测试收集和处理大量患者信息，高维数据在医疗领域变得越来越普遍。试图研究许多预测变量对生存的影响的统计模型通常实施特征选择或惩罚方法以减轻过度拟合的不良后果。在某些情况下，生存数据也会被左截断，这可能会导致不朽的时间偏差，但调整左截断的惩罚生存方法并不常见。为了应对这些挑战，我们对左截断和右截尾生存数据应用惩罚 Cox 比例风险模型，并评估左截断调整对偏差和解释的影响。