Gi-Ming Wang,Curtis Tatsuoka
Gi-Ming Wang
We develop a new framework specifically for early Phase I clinical trials called Bayesian Ordered Lattice Design (BOLD). This study is motivated by two key factors. First, Phase I clinical trials typically involve relatively small sample si...
Personalized Nutrition Recommendations Using a Bayesian Mixture Model of Concentration Constraints and Intake Preferences [0.03%]
基于浓度约束和摄入偏好的Bayesian混合模型的个性化营养推荐
Jari Turkia,Ursula Schwab,Ville Hautamäki
Jari Turkia
Maintaining proper nutrition is crucial for preserving health and preventing disease. However, what constitutes proper nutrition may vary among individuals; evidence indicates that the effects of diet and even single nutrients can differ co...
Bayesian Tensor Decomposition for Clustering Latent Symptom Profiles for Verbal Autopsy Data [0.03%]
贝叶斯张量分解在语言学 autopsy 数据潜在症状谱聚类中的应用
Yu Zhu,Zehang Richard Li
Yu Zhu
Cause-of-death data is fundamental for understanding population health trends and inequalities as well as designing and evaluating public health interventions. A significant proportion of global deaths, particularly in low- and middle-incom...
Using Quadratic Programming to Reconstruct Data From Published Survival and Competing Risks Analyses [0.03%]
应用二次规划从发表的生存分析和竞争风险分析中重建数据
Andrew C Titman
Andrew C Titman
The ability to retrieve pseudo-individual patient data (IPD) from published survival study results is important to facilitate meta-analysis, evidence synthesis or secondary data analyses for the purpose of decision modeling for cost effecti...
Yongwu Shao,Xu Guo
Yongwu Shao
The robust Wald confidence interval (CI) for the Cox model is commonly used when the model may be misspecified or when weights are applied. However, it can perform poorly when there are few events in one or both treatment groups, as may occ...
Estimating Conditional Complier Quantile Treatment Effect via Stratified Quantile Regression [0.03%]
基于分层分位数回归的条件依从者分位数处理效应估计方法研究
Huijuan Ma,Mengjiao Peng,Jing Qin
Huijuan Ma
Understanding the causal effect of a treatment in randomized experiments with noncompliance is of fundamental interest in many domains. Within the instrumental variable (IV) framework, the causal treatment effect can only be reliably assess...
A Bayesian Approach to Estimate Causal Average Treatment Effects Under Unmeasured Confounding [0.03%]
一种估计未测量混淆下的因果平均处理效应的贝叶斯方法
Jinghong Zeng
Jinghong Zeng
One major bias source in causal inference for clinical trials is unmeasured confounding. We propose an innovative, practical Bayesian modeling approach to adjust for unmeasured confounding effects and obtain precise causal average treatment...
Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes [0.03%]
具生存结果的群随机试验的估量与双稳健估计方法
Xi Fang,Bingkai Wang,Liangyuan Hu et al.
Xi Fang et al.
Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between tre...
Estimating Risk Differences Using Large Healthcare Data Networks for Medical Product Post-Market Safety Outcomes in a Distributed Data Setting and Allowing for Active Post-Market Surveillance [0.03%]
基于分布式大数据网络估计风险差异以评价药品上市后再评价中的安全事件并允许进行主动监测研究
Andrea J Cook,Robert D Wellman,Tracey Marsh et al.
Andrea J Cook et al.
Risk differences allow decision makers to easily estimate the excess safety risk associated with a medical product relative to the potential benefits. However, in post-market observational surveillance studies that actively monitor (e.g., s...
Using Causal Diagrams to Assess Parallel Trends in Difference-in-Differences Studies [0.03%]
运用因果图评估差分回归分析中的并行趋势方法
Audrey Renson,Oliver Dukes,Zach Shahn
Audrey Renson
Difference-in-differences (DID) is popular because it can allow for unmeasured confounding when the key assumption of parallel trends holds. However, there exists little guidance on how to decide a priori whether this assumption is reasonab...