Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data [0.03%]
基于语言死亡报告数据推断死亡原因的贝叶斯分层因子回归模型
Kelly R Moran,Elizabeth L Turner,David Dunson et al.
Kelly R Moran et al.
In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations. Pos...
Nested g-computation: A causal approach to analysis of censored medical costs in the presence of time-varying treatment [0.03%]
嵌套的g计算：时间变化处理存在下的删失医疗费用分析的因果方法
Andrew J Spieker,Emily M Ko,Jason A Roy et al.
Andrew J Spieker et al.
Rising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right-censoring induces informative missingness due to heterogeneity in cost accumulation rate...
Using Cox regression to develop linear rank tests with zero-inflated clustered data [0.03%]
使用Cox回归方法对含有零膨胀的集群数据进行线性等级测试的研究
Stuart R Lipsitz,Garrett M Fitzmaurice,Debajyoti Sinha et al.
Stuart R Lipsitz et al.
Zero-inflated data arise in many fields of study. When comparing zero-inflated data between two groups with independent subjects, a two degree-of-freedom test has been developed, which is the sum of a 1 degree-of-freedom Pearson chi-square ...
Threshold-based subgroup testing in logistic regression models in two-phase sampling designs [0.03%]
分两阶段抽样设计的逻辑回归模型中的阈值子群检验
Ying Huang,Juhee Cho,Youyi Fong
Ying Huang
The effect of treatment on binary disease outcome can differ across subgroups characterized by other covariates. Testing for the existence of subgroups that are associated with heterogeneous treatment effects can provide valuable insight re...
Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death [0.03%]
半参贝叶斯G-公式在含未缺失即缺失脱落队列研究中的因果推断应用
Maria Josefsson,Michael J Daniels
Maria Josefsson
Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. The G-computation formula is one approach for estimating a causal effect in this setting. The...
RNASeqDesign: A framework for RNA-Seq genome-wide power calculation and study design issues [0.03%]
RNASeqDesign：一个用于RNA序全基因组功效计算和研究设计的框架
Chien-Wei Lin,Serena G Liao,Peng Liu et al.
Chien-Wei Lin et al.
Massively parallel sequencing (a.k.a. next-generation sequencing, NGS) technology has emerged as a powerful tool in characterizing genomic profiles. Among many NGS applications, RNA sequencing (RNA-Seq) has gradually become a standard tool ...
Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach [0.03%]
基于贝叶斯方法的变量选择和预测：整合生存数据与二进制数据
Arnab Kumar Maity,Raymond J Carroll,Bani K Mallick
Arnab Kumar Maity
We consider the problem where the data consist of a survival time and a binary outcome measurement for each individual, as well as corresponding predictors. The goal is to select the common set of predictors which affect both the responses,...
Indices of non-ignorable selection bias for proportions estimated from non-probability samples [0.03%]
非忽略选择偏差的比例估计指标非概率样本
Rebecca R Andridge,Brady T West,Roderick J A Little et al.
Rebecca R Andridge et al.
Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples ma...
A Hybrid Approach for the Stratified Mark-Specific Proportional Hazards Model with Missing Covariates and Missing Marks, with Application to Vaccine Efficacy Trials [0.03%]
具有缺失协变量和缺失标记的分层标识 proportional hazards 模型的混合方法及其在疫苗功效试验中的应用
Yanqing Sun,Li Qi,Fei Heng et al.
Yanqing Sun et al.
Deployment of the recently licensed CYD-TDV dengue vaccine requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titer - NAb) and on a "...
Maya B Mathur,Tyler J VanderWeele
Maya B Mathur
We propose sensitivity analyses for publication bias in meta-analyses. We consider a publication process such that 'statistically significant' results are more likely to be published than negative or "non-significant" results by an unknown ...