Accounting for interactions and complex inter‐subject dependency in estimating treatment effect in cluster‐randomized trials with missing outcomes-Reference-Cited by-同舟云学术

Accounting for interactions and complex inter‐subject dependency in estimating treatment effect in cluster‐randomized trials with missing outcomes

Published:2016-04-08 Issue:4 Volume:72 Page:1066-1077
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biom

Author:

Prague Melanie¹,Wang Rui¹²,Stephens Alisa³,Tchetgen Tchetgen Eric⁴,DeGruttola Victor¹

Affiliation:

1. Department of Biostatistics, Harvard T.H. Chan School of Public Health Boston, Massachusetts, U.S.A.

2. Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital Boston, Massachusetts, U.S.A.

3. Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania Philadelphia, Pennsylvania, U.S.A.

4. Department of Epidemiology, Harvard T.H. Chan School of Public Health Boston, Massachusetts, U.S.A.

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12519

Reference40 articles.

1. Measuring balance and model selection in propensity score methods

2. An Empirical Comparison of Several Clustered Data Approaches Under Confounding Due to Cluster Effects in the Analysis of Complications of Coronary Angioplasty

3. A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data

4. Variable Selection for Propensity Score Models

5. Adjusting for confounding by cluster using generalized linear mixed models

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes;Statistics in Medicine;2024-02-05

2. Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies;BMC Medical Research Methodology;2023-12-13

3. Bias-corrected and doubly robust inference for the three-level longitudinal cluster-randomized trials with missing continuous outcomes and small number of clusters: Simulation study and application to a study for adults with serious mental illnesses;Contemporary Clinical Trials Communications;2023-10

4. Covariate adjustment in randomized clinical trials with missing covariate and outcome data;Statistics in Medicine;2023-07-02

5. Sample size calculation for randomized trials via inverse probability of response weighting when outcome data are missing at random;Statistics in Medicine;2023-03-06