Pseudo-Observation Approach for Length-Biased Cox Proportional Hazards Model [0.03%]
Cox比例风险模型的长度偏差数据的伪概量法研究
Mahboubeh Akbari,Najmeh Nakhaei Rad,Ding-Geng Chen
Mahboubeh Akbari
Pseudo-observations are used to estimate the expectation of a function of interest in a population when survival data are incomplete due to censoring or truncation. Length-biased sampling is a special case of a left-truncation model, in whi...
Sophie Hanna Langbein,Mateusz Krzyziński,Mikołaj Spytek et al.
Sophie Hanna Langbein et al.
With the spread and rapid advancement of black box machine learning (ML) models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is ...
Sparse Canonical Correlation Analysis for Multiple Measurements With Latent Trajectories [0.03%]
具有潜在轨迹的多个测量的稀疏典型相关分析
Nuria Senar,Aeilko H Zwinderman,Michel H Hof
Nuria Senar
Canonical correlation analysis (CCA) is a widely used multivariate method in omics research for integrating high-dimensional datasets. CCA identifies hidden links by deriving linear projections of observed features that maximally correlate ...
Non-Markov Nonparametric Estimation of Complex Multistate Outcomes After Hematopoietic Stem Cell Transplantation [0.03%]
基于造血干细胞移植后复杂多状态结局的非马尔可夫非参数估计方法
Judith Vilsmeier,Sandra Schmeller,Daniel Fürst et al.
Judith Vilsmeier et al.
Often probabilities of nonstandard time-to-event endpoints are of interest, which are more complex than overall survival. One such probability is chronic graft-versus-host disease (GvHD-) and relapse-free survival, the probability of being ...
Rebecca Knowlton,Layla Parast
Rebecca Knowlton
In modern clinical trials, there is immense pressure to use surrogate markers in place of an expensive or long-term primary outcome to make more timely decisions about treatment effectiveness. However, using a surrogate marker to test for a...
Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data [0.03%]
基于融合稀疏分组惩罚和分子数据的多状态模型变量选择
Kaya Miah,Jelle J Goeman,Hein Putter et al.
Kaya Miah et al.
In multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model. In particular, linking covariate effects across transitions is needed to conduct joint var...
Generalized Bayesian Inference for Causal Effects Using the Covariate Balancing Procedure [0.03%]
基于协变量均衡化的因果效应的广义贝叶斯推断
Shunichiro Orihara,Tomotaka Momozaki,Tomoyuki Nakagawa
Shunichiro Orihara
In observational studies, the propensity score plays a central role in estimating causal effects of interest. The inverse probability weighting (IPW) estimator is commonly used for this purpose. However, if the propensity score model is mis...
Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders [0.03%]
具有未观察到的混淆因素的连续处理效应的尖锐界线
Jean-Baptiste Baitairian,Bernard Sebastien,Rana Jreich et al.
Jean-Baptiste Baitairian et al.
In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxing this assumption and conducting a sensitivity analysis, we ...
Weibull Regression With Both Measurement Error and Misclassification in Covariates [0.03%]
含测量误差及误分类的Weibull回归模型研究
Zhiqiang Cao,Man Yu Wong
Zhiqiang Cao
The problem of measurement error and misclassification in covariates is ubiquitous in nutritional epidemiology and some other research areas, which often leads to biased estimate and loss of power. However, addressing both measurement error...
Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes: The Genetic Latent Factor Approach [0.03%]
利用高维第二性状改善基因组预测：遗传潜在因子方法
Killian A C Melsen,Jonathan F Kunst,José Crossa et al.
Killian A C Melsen et al.
Decreasing costs and new technologies have led to an increase in the amount of data available to plant breeding programs. High-throughput phenotyping (HTP) platforms routinely generate high-dimensional datasets of secondary features that ma...