Accelerated failure time (AFT) models are frequently used to model survival data, providing a direct quantification of the relationship between event times and covariates. These models allow for the acceleration or deceleration of failure times through a multiplicative factor that accounts for the effect of covariates. While existing literature provides numerous methods for fitting AFT models with time-fixed covariates, adapting these approaches to scenarios involving both time-varying covariates and partly interval-censored data remains challenging. Motivated by a randomised clinical trial dataset on advanced melanoma patients, we propose a maximum penalised likelihood approach for fitting a semiparametric AFT model to survival data with partly interval-censored failure times. This method also accommodates both time-fixed and time-varying covariates. We utilise Gaussian basis functions to construct a smooth approximation of the non-parametric baseline hazard and fit the model using a constrained optimisation approach. The effectiveness of our method is demonstrated through extensive simulations. Finally, we illustrate the relevance of our approach by applying it to a dataset from a randomised clinical trial involving patients with advanced melanoma.

翻译：加速失效时间模型常用于生存数据分析，可直接量化事件时间与协变量之间的关系。该模型通过一个考虑协变量效应的乘性因子，实现对失效时间的加速或减速。现有文献已提出多种适用于时间固定协变量的AFT模型拟合方法，但将其推广至同时包含时间相依协变量与部分区间删失数据的场景仍具挑战性。基于一项晚期黑色素瘤患者的随机临床试验数据集，本文提出一种最大惩罚似然方法，用于拟合具有部分区间删失失效时间的半参数AFT生存模型。该方法同时兼容时间固定与时间相依协变量。我们采用高斯基函数构建非参数基准风险函数的平滑近似，并通过约束优化方法进行模型拟合。大量模拟实验验证了本方法的有效性。最后，通过将其应用于晚期黑色素瘤患者的随机临床试验数据集，展示了本方法的实际应用价值。