Search

Your search keyword '"Wang, Xueqi"' showing total 21 results
Start Over Author "Wang, Xueqi" Publisher wiley
21 results on '"Wang, Xueqi"'

10. Comparative transcriptome and proteome reveal synergistic functions of differentially expressed genes and proteins implicated in an over‐dominant silkworm heterosis of increased silk yield

Author

Xiao, Rui, primary, Yuan, Yi, additional, Xia, Hengchuan, additional, Ge, Qi, additional, Chen, Liang, additional, Zhu, Feifei, additional, Xu, Jia, additional, Wang, Xueqi, additional, Fan, Yixuan, additional, Wang, Qiang, additional, Yang, Yanhua, additional, and Chen, Keping, additional

Published
2022
Full Text
View/download PDF

20. Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs.

Author

Moyer JC, Li F, Cook AJ, Heagerty PJ, Pals SL, Turner EL, Wang R, Zhou Y, Yu Q, Wang X, and Murray DM

Subjects
Humans, Cluster Analysis, Research Design, Randomized Controlled Trials as Topic statistics & numerical data, Models, Statistical, Computer Simulation
Abstract

Many individually randomized group treatment (IRGT) trials randomly assign individuals to study arms but deliver treatments via shared agents, such as therapists, surgeons, or trainers. Post-randomization interactions induce correlations in outcome measures between participants sharing the same agent. Agents can be nested in or crossed with trial arm, and participants may interact with a single agent or with multiple agents. These complications have led to ambiguity in choice of models but there have been no systematic efforts to identify appropriate analytic models for these study designs. To address this gap, we undertook a simulation study to examine the performance of candidate analytic models in the presence of complex clustering arising from multiple membership, single membership, and single agent settings, in both nested and crossed designs and for a continuous outcome. With nested designs, substantial type I error rate inflation was observed when analytic models did not account for multiple membership and when analytic model weights characterizing the association with multiple agents did not match the data generating mechanism. Conversely, analytic models for crossed designs generally maintained nominal type I error rates unless there was notable imbalance in the number of participants that interact with each agent., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Published
2024
Full Text
View/download PDF

21. Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations.

Author

Wang X, Turner EL, and Li F

Subjects
Humans, Sample Size, Bias, Cluster Analysis, Computer Simulation, Models, Statistical, Research Design
Abstract

Individually randomized group treatment (IRGT) trials, in which the clustering of outcome is induced by group-based treatment delivery, are increasingly popular in public health research. IRGT trials frequently incorporate longitudinal measurements, of which the proper sample size calculations should account for correlation structures reflecting both the treatment-induced clustering and repeated outcome measurements. Given the relatively sparse literature on designing longitudinal IRGT trials, we propose sample size procedures for continuous and binary outcomes based on the generalized estimating equations approach, employing the block exchangeable correlation structures with different correlation parameters for the treatment arm and for the control arm, and surveying five marginal mean models with different assumptions of time effect: no-time constant treatment effect, linear-time constant treatment effect, categorical-time constant treatment effect, linear time by treatment interaction, and categorical time by treatment interaction. Closed-form sample size formulas are derived for continuous outcomes, which depends on the eigenvalues of the correlation matrices; detailed numerical sample size procedures are proposed for binary outcomes. Through simulations, we demonstrate that the empirical power agrees well with the predicted power, for as few as eight groups formed in the treatment arm, when data are analyzed using the matrix-adjusted estimating equations for the correlation parameters with a bias-corrected sandwich variance estimator., (© 2023 John Wiley & Sons Ltd.)

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results