15 papers · ranked by Valyu relevance
Wenwen Si, Hongxiang Qiu
Uncertainty quantification of prediction models through prediction sets is increasingly popular and successful, but most existing methods rely on directly observing the outcome and do not appropriately handle censored outcomes, such as time-to-event outcomes. Candès et al. ([3]) and Gui et al. ([11]) have introduced…
Lan Wen, Aaron L. Sarvet, Jessica G. Young
In causal inference literature, potential outcomes are often indexed by the "elimination of all right-censoring events," leading to the perception that such a restriction is necessary for defining well-posed causal estimands. In this paper, we clarify that this restriction is not required: a well-defined estimand can…
Andrew Ying
Many epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still surviving after the study terminates or move out of the study area, in which case right censoring is typically treated as independent or…
Wenwen Si, Hongxiang Qiu
Uncertainty quantification of prediction models through prediction sets is increasingly popular and successful, but most existing methods rely on directly observing the outcome and do not appropriately handle censored outcomes, such as time-to-event outcomes. Candès et al. [2023] and Gui et al. [2024] have introduced…
Yizhuo Wang, Christopher R. Flowers, Ziyi Li, Xuelin Huang
Data analyses by machine learning (ML) algorithms are gaining popularity in biomedical research. When time-to-event data are of interest, censoring is common and needs to be properly addressed. Most ML methods cannot conveniently and appropriately take the censoring information into consideration, potentially leading…
Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke
Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rules inapplicable in their standard form. We propose a framework for proper…
Thomas Prince, Andrea Bommert, Jörg Rahnenführer, Matthias Schmid + 1 more
'Li-Pang Chen'] Inverse probability weighting (IPW) is a popular method for making inferences regarding unobserved or unobservable data of a target population based on observed data. This paper considers IPW applied to right-censored time-to-event data. We investigate the behavior of the…
Ersin Yılmaz, Dursun Aydın, S. Ejaz Ahmed, Donald J. Jacobs + 2 more
'Abhijit Mandal' 'Suneel Babu Chatla'] This paper introduces a modified local linear estimator (LLR) for partially linear additive models (PLAM) when the response variable is subject to random right-censoring. In the case of modeling right-censored data, PLAM offers a more flexible and realistic approach to the…
Yue Lyu, Steven Hsesheng Lin, Xuelin Huang, Ziyi Li
This paper introduces SuperSurv, a user-friendly R package for building, evaluating, and interpreting ensemble models for right-censored survival data. Although many survival modeling methods are available, existing tools are often model-specific and lack a unified platform for systematically integrating, comparing…
Jesus E. Vazquez, Marissa C. Ashner, Yanyuan Ma, Karen Marder + 1 more
Missing Covariates Authors: ['Jesus E. Vazquez' 'Marissa C. Ashner' 'Yanyuan Ma' 'Karen Marder' 'Tanya P. Garcia'] While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as right-censored covariates, is now of growing interest. So far, the literature has…
Kimberly A. Barchard, James A. Russell
Data censoring occurs when researchers do not know precise values of data points (e.g., age is 55+ or concentration ≤ .001). Censoring is frequent within psychology but typically unrecognized outside of longitudinal studies. We describe five circumstances when censoring may occur, demonstrate censoring distorts…
Carlos García Meixide, Marcos Matabuena
Counterfactual inference at the distributional level presents new challenges with censored targets, especially in modern healthcare problems. To mitigate selection bias in this context, we exploit the intrinsic structure of reproducing kernel Hilbert spaces (RKHS) harnessing the notion of kernel mean embedding. This…
Trevor Wei Kiat Tan, Ru Kong, Aihuiping Xue, Jingwen Cheng + 7 more
Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates.…
Michael S. Jones, Zhenchen Zhu, Aahana Bajracharya, Austin Luor + 1 more
Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring—that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors—has appeared in several task-based fMRI studies as a mitigation strategy.…
Andrew Goldberg, Isabella Rosario, Jonathan Power, Guillermo Horga + 1 more
Intrinsic neural timescale (INT) is a resting-state fMRI (rs-fMRI) measure that reflects the time window of neural integration within a brain region. Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on INT have not been…