Abstract
The stepped wedge (SW) cluster randomized design has been increasingly employed by pragmatic trials in health services research. In this study, based on the GEE approach, I present a closed-form sample size that is applicable to both closed-cohort and cross-sectional SW trials with outcomes from the exponential family. On the other hand, I proposed a Bayesian adaptive design for cross-sectional SW cluster randomized trials. It is more adaptable than traditional designs because it allows early termination of the trial when interim data indicate that the intervention is sufficient efficacious or inefficacious. A decision to terminate or continue the trial will be made on the basis of the predictive probability. This probability is the chance of getting a conclusive result at the end of the study based on the interim data collected so far.
Degree Date
Fall 2019
Document Type
Dissertation
Degree Name
Ph.D.
Department
Statistical Science
Advisor
Song Zhang
Second Advisor
Chul Ahn
Subject Area
Biostatistics
Number of Pages
71
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Wang, Jijia, "Robust And Adaptive Design Approaches For Stepped Wedge Cluster Randomized Trials" (2019). Statistical Science Theses and Dissertations. 7.
https://scholar.smu.edu/hum_sci_statisticalscience_etds/7
