A Simulation Study of Statistical Approaches to Data Analysis in the Stepped Wedge Design

Yuqi Ren, James P. Hughes () and Patrick J. Heagerty
Additional contact information
Yuqi Ren: University of Washington
James P. Hughes: University of Washington
Patrick J. Heagerty: University of Washington

Statistics in Biosciences, 2020, vol. 12, issue 3, No 9, 399-415

Abstract: Abstract This paper studies model-based and design-based approaches for the analysis of data arising from a stepped wedge randomized design. Specifically, for different scenarios we compare robustness, efficiency, Type I error rate under the null hypothesis, and power under the alternative hypothesis for the leading analytical options including generalized estimating equations (GEE) and linear mixed model (LMM)-based approaches. We find that GEE models with exchangeable correlation structures are more efficient than GEE models with independent correlation structures under all scenarios considered. The model-based GEE Type I error rate can be inflated when applied with a small number of clusters, but this problem can be solved using a design-based approach. As expected, correct model specification is more important for LMM (compared to GEE) since the model is assumed correct when standard errors are calculated. However, in contrast to the model-based results, the design-based Type I error rates for LMM models under scenarios with a random treatment effect show Type I error inflation even though the fitted models perfectly match the corresponding data-generating scenarios. Therefore, greater robustness can be realized by combining GEE and permutation testing strategies.

Keywords: Stepped wedge design; GEE; LMM; Permutation test; Simulation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-019-09259-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:12:y:2020:i:3:d:10.1007_s12561-019-09259-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-019-09259-x

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().