Bias of the additive hazard model in the presence of causal effect heterogeneity [0.03%]
因果效应异质性存在时加性风险模型的偏差
Richard A J Post,Edwin R van den Heuvel,Hein Putter
Richard A J Post
Hazard ratios are prone to selection bias, compromising their use as causal estimands. On the other hand, if Aalen's additive hazard model applies, the hazard difference has been shown to remain unaffected by the selection of frailty factor...
Randomized Controlled Trial
Lifetime data analysis. 2024 Apr;30(2):383-403. DOI:10.1007/s10985-024-09616-z 2024
Motahareh Parsa,Seyed Mahmood Taghavi-Shahri,Ingrid Van Keilegom
Motahareh Parsa
In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in surv...
A Bayesian quantile joint modeling of multivariate longitudinal and time-to-event data [0.03%]
多元纵向和时变数据的贝叶斯分位数联合建模
Damitri Kundu,Shekhar Krishnan,Manash Pratim Gogoi et al.
Damitri Kundu et al.
Linear mixed models are traditionally used for jointly modeling (multivariate) longitudinal outcomes and event-time(s). However, when the outcomes are non-Gaussian a quantile regression model is more appropriate. In addition, in the presenc...
Pseudo-value regression trees [0.03%]
伪值回归树
Alina Schenk,Moritz Berger,Matthias Schmid
Alina Schenk
This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our method, named pseudo-value regression trees (PRT), is based on the pseudo-value regression fr...
The built-in selection bias of hazard ratios formalized using structural causal models [0.03%]
使用结构因果模型形式化的风险比的内在选择偏见
Richard A J Post,Edwin R van den Heuvel,Hein Putter
Richard A J Post
It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the expos...
Cui-Juan Kong,Han-Ying Liang
Cui-Juan Kong
In this paper, we define estimators of distribution functions when the data are right-censored and the censoring indicators are missing at random, and establish their strong representations and asymptotic normality. Besides, based on empiri...
Jialiang Li,Stijn Vansteelandt
Jialiang Li
A Bayesian proportional hazards mixture cure model for interval-censored data [0.03%]
一种用于区间删失数据的贝叶斯比例风险混合治愈模型
Chun Pan,Bo Cai,Xuemei Sui
Chun Pan
The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval-censored, the estimation of this model is challenging due to its complex data stru...
Efficiency of the Breslow estimator in semiparametric transformation models [0.03%]
半参数变换模型中Breslow估计的效率上限研究
Theresa P Devasia,Alexander Tsodikov
Theresa P Devasia
Semiparametric transformation models for failure time data consist of a parametric regression component and an unspecified cumulative baseline hazard. The nonparametric maximum likelihood estimator (NPMLE) of the cumulative baseline hazard ...
Assessing model prediction performance for the expected cumulative number of recurrent events [0.03%]
评估复发事件预期累积数的模型预测性能
Olivier Bouaziz
Olivier Bouaziz
In a recurrent event setting, we introduce a new score designed to evaluate the prediction ability, for a given model, of the expected cumulative number of recurrent events. This score can be seen as an extension of the Brier Score for sing...