On the mixed-model analysis of covariance in cluster-randomized trials [0.03%]
分层集群随机试验的混合模型协方差分析方法研究
Bingkai Wang,Michael O Harhay,Jiaqi Tong et al.
Bingkai Wang et al.
In the analyses of cluster-randomized trials, mixed-model analysis of covariance (ANCOVA) is a standard approach for covariate adjustment and handling within-cluster correlations. However, when the normality, linearity, or the random-interc...
Kelly W Zhang,Nowell Closser,Anna L Trella et al.
Kelly W Zhang et al.
Adaptive treatment assignment algorithms, such as bandit algorithms, are increasingly used in digital health intervention clinical trials. Frequently the data collected from these trials is used to conduct causal inference and related data ...
Khanh N Dinh,Roman Jaksik,Marek Kimmel et al.
Khanh N Dinh et al.
Recent years have seen considerable work on inference about cancer evolution from mutations identified in cancer samples. Much of the modeling work has been based on classical models of population genetics, generalized to accommodate time-v...
Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review [0.03%]
结合随机试验和观察性研究的因果推理方法回顾
Bénédicte Colnet,Imke Mayer,Guanhua Chen et al.
Bénédicte Colnet et al.
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) ...
On the Use of Auxiliary Variables in Multilevel Regression and Poststratification [0.03%]
辅助变量在多水平回归和后分层中的应用研究
Yajuan Si
Yajuan Si
Multilevel regression and poststratification (MRP) is a popular method for addressing selection bias in subgroup estimation, with broad applications across fields from social sciences to public health. In this paper, we examine the inferent...
Hani Doss,Antonio Linero
Hani Doss
Consider a Bayesian setup in which we observe Y , whose distribution depends on a parameter θ , that is, Y ∣ θ ~ π Y ∣ θ . The parameter θ is unknown and treated as random, and a prior distribution c...
Robust Tests in Genome-Wide Scans under Incomplete Linkage Disequilibrium [0.03%]
不平衡连锁不平衡下的全基因组扫描的鲁棒检验方法研究
Gang Zheng,Jungnam Joo,Dmitri Zaykin et al.
Gang Zheng et al.
When genetic markers are in complete linkage disequilibrium with disease loci, it has been shown that the efficiency robust tests, including maximum (minimum) type statistics and the procedure with genetic model selection, are often preferr...
Chuji Luo,Michael J Daniels
Chuji Luo
Variable selection is an important statistical problem. This problem becomes more challenging when the candidate predictors are of mixed type (e.g. continuous and binary) and impact the response variable in nonlinear and/or non-additive way...
Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity [0.03%]
整合试验和非实验数据考察处理异质性的方法
Carly Lupton Brantner,Ting-Hsuan Chang,Trang Quynh Nguyen et al.
Carly Lupton Brantner et al.
Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately est...
David S Robertson,James M S Wason,Aaditya Ramdas
David S Robertson
Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and techno...