A comparison of Kaplan-Meier-based inverse probability of censoring weighted regression methods [0.03%]
基于Kaplan-Meier的逆概率审查加权回归方法的比较
Morten Overgaard
Morten Overgaard
Weighting with the inverse probability of censoring is an approach to deal with censoring in regression analyses where the outcome may be missing due to right-censoring. In this paper, three separate approaches involving this idea in a sett...
Modelling dependent censoring in time-to-event data using boosting copula regression [0.03%]
基于提升协同回归的生存数据相依截尾模型研究
Annika Strömer,Nadja Klein,Ingrid Van Keilegom et al.
Annika Strömer et al.
Statistical methods for composite analysis of recurrent and terminal events in clinical trials [0.03%]
临床试验中反复和终末事件合成分析的统计方法
Yiyuan Huang,Douglas Schaubel,Min Zhang
Yiyuan Huang
In many clinical trials, one is interested in evaluating the treatment effect based on different types of outcomes, including recurrent and terminal events. The most popular approach is the time-to-first-event analysis (TTFE), based on the ...
Assessing delayed treatment benefits of immunotherapy using long-term average hazard: a novel test/estimation approach [0.03%]
利用长期平均危险度评估免疫治疗的延迟疗效益处：一种新型检验/估计方法
Miki Horiguchi,Lu Tian,Kenneth L Kehl et al.
Miki Horiguchi et al.
Delayed treatment effects on time-to-event outcomes are commonly observed in randomized controlled trials of cancer immunotherapies. When the treatment effect has a delayed onset, the conventional test/estimation approach-using the log-rank...
Bayesian joint models for longitudinal, recurrent, and terminal event data [0.03%]
基于纵向、复发和终末事件数据的贝叶斯联合建模方法研究
Emily M Damone,Matthew A Psioda,Joseph G Ibrahim
Emily M Damone
Many methods exist to jointly model either recurrent and related terminal survival events or longitudinal outcome measures and related terminal survival event. However, few methods exist which can account for the dependency between all thre...
Bayesian generalized method of moments applied to pseudo-observations in survival analysis [0.03%]
贝叶斯广义矩法在生存分析伪观测值中的应用
Léa Orsini,Caroline Brard,Emmanuel Lesaffre et al.
Léa Orsini et al.
Bayesian inference for survival regression modeling offers numerous advantages, especially for decision-making and external data borrowing, but demands the specification of the baseline hazard function, which may be a challenging task. We p...
Pseudo-observations and super learner for the estimation of the restricted mean survival time [0.03%]
限制均值生存时间的估计中的伪观测值和超级学习器方法
Ariane Cwiling,Vittorio Perduca,Olivier Bouaziz
Ariane Cwiling
In the context of right-censored data, we study the problem of predicting the restricted time to event based on a set of covariates. Under a quadratic loss, this problem is equivalent to estimating the conditional restricted mean survival t...
Bayesian joint analysis of longitudinal data and interval-censored failure time data [0.03%]
纵向数据与区间删失生存数据的贝叶斯联合分析方法研究
Yuchen Mao,Lianming Wang,Xuemei Sui
Yuchen Mao
Joint modeling of longitudinal responses and survival time has gained great attention in statistics literature over the last few decades. Most existing works focus on joint analysis of longitudinal data and right-censored data. In this arti...
Causal effect estimation on restricted mean survival time under case-cohort design via propensity score stratification [0.03%]
基于倾向得分分层的病例队列设计中受限平均生存时间的因果效应估计方法研究
Wei-En Lu,Ai Ni
Wei-En Lu
In large observational studies with survival outcome and low event rates, the case-cohort design is commonly used to reduce the cost associated with covariate measurement. The restricted mean survival time (RMST) difference has been increas...
Wild bootstrap for counting process-based statistics: a martingale theory-based approach [0.03%]
基于鞅理论的计数过程统计量的野引导方法
Marina T Dietrich,Dennis Dobler,Mathisca C M de Gunst
Marina T Dietrich
The wild bootstrap is a popular resampling method in the context of time-to-event data analysis. Previous works established the large sample properties of it for applications to different estimators and test statistics. It can be used to ju...