A Bayesian likely responder approach for the analysis of randomized controlled trials [0.03%]
贝叶斯可能受益者方法在随机对照试验分析中的应用
Annan Deng,Carole Siegel,Hyung G Park
Annan Deng
An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The likely responder (LR) framework, which identifies a subpopulation where tre...
Rong J B Zhu
Rong J B Zhu
Propensity score weighting is a common method in causal inference methods. However, this approach faces two well-known challenges: (i) high variance due to small probability values in the denominator, and (ii) sensitivity to model specifica...
Andrea Callegaro,Nathan W Bean
Andrea Callegaro
The ICH E9(R1) addendum stresses the importance of clearly pre-specifying clinically interpretable treatment effect measures (estimands) and proposes different strategies to deal with intercurrent events. In this paper, we consider differen...
Sensitivity bounds for bias in hazard ratios: A causal hazard perspective [0.03%]
危害比率偏倚敏感界值：因果关系危险率角度
Yan-Lin Chen,Hsiang-Hsi Hung,Sheng-Hsuan Lin
Yan-Lin Chen
The Cox proportional hazards model has popularized the conventional hazard ratio as a standard measure for assessing the effect of exposure on time-to-event outcomes. However, as noted in Hernán's influential critique, interpreting the haz...
Peijie Wang,Qihao Wang,Jianguo Sun
Peijie Wang
Truncated data frequently arise in many areas such as economics, astronomical studies, and survival analysis, and the existence of truncation makes statistical inference more difficult due to the incomplete information. In this paper, we pr...
Improving finite sample performance of causal discovery by exploiting temporal structure [0.03%]
利用时间结构改善因果发现的有限样本性能
Christine Bang,Janine Witte,Ronja Foraita et al.
Christine Bang et al.
Methods of causal discovery aim to identify causal structures in a data-driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We ...
Penalized estimation of linear transformation models for interval-censored data with time-dependent covariates [0.03%]
带有时变协变量的区间截断数据下的惩罚估计及线性转换模型
Minggen Lu,Yahui Zhang,Chin-Shang Li et al.
Minggen Lu et al.
We investigate efficient estimation strategies for partially linear transformation models with time-dependent covariates under interval censoring. The unknown monotone function is approximated using a monotone B-spline basis to enable flexi...
Eliminating residual confounding in the stratified estimator via smoothing along with the propensity score [0.03%]
利用倾向值平滑消除分层估计量中的残余混杂因素影响
Naoto Tsujimoto,Satoshi Hattori
Naoto Tsujimoto
The stratified estimator by the propensity score is one of the most popular estimator for the average causal effect in the presence of confounding. Despite of its advantages of robustness and simplicity, it has a serious shortcoming of resi...
Joint estimation of multiple graphical models for an fMRI study of brain connectivity networks [0.03%]
联合估计多个图形模型以研究fMRI的脑连接网络
Lizhe Sun,Xiaojuan Han,Aiying Zhang
Lizhe Sun
Investigating changes and similarities in brain connectivity networks across task conditions is a central topic in neuroscience. We propose a novel framework for jointly estimating multiple graphical models using a hybrid Bayesian integrati...
Addressing nonignorable missing data and heterogeneity in prognostic biomarker assessment [0.03%]
处理缺失数据和异质性以评估预后生物标志物
Xinran Huang,Ruosha Li,Jing Ning et al.
Xinran Huang et al.
Covariate-specific and time-dependent area-under-curve (AUC) is often used to evaluate the discriminative performance of biomarkers with time-to-event outcomes, particularly when certain covariates influence biomarkers' accuracy. In biomark...