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高维多变量纵向数据的潜变量学习方法AI 解读

High-dimensional multivariate longitudinal data, which arise when many outcome variables are measured repeatedly over time, are becoming increasingly common in social, behavioral and health sciences. We propose a latent variable model for drawing statistical inferences on covariate effects and predicting future outcomes based on high-dimensional multivariate longitudinal data. This model introduces unobserved factors to account for the between-variable and across-time…

含时变协变量的删失分位数回归AI 解读

Abstract We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be…

含时变协变量的删失分位区间回归模型AI 解读

Conventionally, censored quantile regression stipulates a specific, pointwise conditional quantile of the survival time given covariates.Despite its model flexibility and straightforward interpretation, the pointwise formulation oftentimes yields rather unstable estimates across neighboring quantile levels with large variances.In view of this phenomenon, we propose a new class of quantile-based regression models with timedependent covariates for censored data.The models…

删失分位数回归的高效估计AI 解读

Censored quantile regression (CQR) has received growing attention in survival analysis because of its flexibility in modeling heterogeneous effect of covariates. Advances have been made in developing various inferential procedures under different assumptions and settings. Under the conditional independence assumption, many existing CQR methods can be characterized either by stochastic integral-based estimating equations (see, e.g., Peng and Huang) or by locally weighted…

删失数据的变换动态分位数回归AI 解读

We propose a class of power-transformed linear quantile regression models for time-to-event observations subject to censoring. By introducing a process of power transformation with different transformation parameters at individual quantile levels, our framework relaxes the assumption of logarithmic transformation on survival times and provides dynamic estimation of various quantile levels. With such formulation, our proposal no longer requires the potentially restrictive…

偏倚抽样下生存数据分位数回归的估计与推断AI 解读

Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to incorrect inference. We propose a unified estimation procedure and a computationally fast resampling method to make statistical inference for quantile regression with survival data under general biased sampling schemes, including but not limited to the length-biased sampling,…