Authors:	Mildenberger, Philipp König, Jochem
Title:	Influence of cluster-period cells in stepped wedge cluster randomized trials
Online publication date:	25-Jan-2023
Year of first publication:	2022
Language:	english
Abstract:	Stepped wedge cluster randomized trials (SWCRT) are increasingly used for the evaluation of complex interventions in health services research. They randomly allocate treatments to clusters that switch to intervention under investigation at variable time points without returning to control condition. The resulting unbalanced allocation over time periods and the uncertainty about the underlying correlation structures at cluster-level renders designing and analyzing SWCRTs a challenge. Adjusting for time trends is recommended, appropriate parameterizations depend on the particular context. For sample size calculation, the covariance structure and covariance parameters are usually assumed to be known. These assumptions greatly affect the influence single cluster-period cells have on the effect estimate. Thus, it is important to understand how cluster-period cells contribute to the treatment effect estimate. We therefore discuss two measures of cell influence. These are functions of the design characteristics and covariance structure only and can thus be calculated at the planning stage: the coefficient matrix as discussed by Matthews and Forbes and information content (IC) as introduced by Kasza and Forbes. The main result is a new formula for IC that is more general and computationally more efficient. The formula applies to any generalized least squares estimator, especially for any type of time trend adjustment or nonblock diagonal matrices. We further show a functional relationship between IC and the coefficient matrix. We give two examples that tie in with current literature. All discussed tools and methods are implemented in the R package SteppedPower.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-8556
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY-NC
Information on rights of use:	https://creativecommons.org/licenses/by-nc/4.0/
Journal:	Biometrical journal Version of Record (VoR)
Publisher:	Wiley
Publisher place:	Berlin
Issue date:	2022
ISSN:	1521-4036
Publisher DOI:	10.1002/bimj.202100383
Appears in collections:	DFG-491381577-H