cpa.sw.count: Power calculations for stepped-wedge trials with a count...

In nickreich/clusterPower: Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials

Description

This function uses the SWSamp package by Gianluca Baio for estimating power based on analytic formula of Hussey and Hughes (2007) where sample size calculations are based on an assumption of a normally-distributed outcome.

Usage

cpa.sw.count(
  lambda1,
  RR,
  nclusters,
  steps,
  nsubjects,
  ICC = 0.01,
  alpha = 0.05,
  which.var = "within",
  X = NULL,
  all.returned.objects = FALSE
)

Arguments

lambda1	Baseline rate for outcome of interest
RR	Estimated relative risk of the intervention
nclusters	Number of clusters
steps	Number of time steps. Baseline is assumed.
nsubjects	Average size of each cluster
ICC	Intra-class correlation coefficient (default = 0.01)
alpha	Significance level (default=0.05)
which.var	String character specifying which variance to report. Options are the default value 'within' or 'total'.
X	A design matrix indicating the time at which each of the clusters should switch to the intervention arm. Default is NULL and this matrix is automatically computed, but can it can be passed as a user-defined matrix with (nclusters) rows and (steps + 1) columns.
all.returned.objects	Logical. Default = FALSE, indicating that only the estimated power should be returned. When TRUE, all objects (listed below) are returned.

Value

power

The resulting power

When all.returned.objects = TRUE, returned items also include:

sigma.y	The estimated total (marginal) sd for the outcome
sigma.e	The estimated residual sd
sigma.a	The resulting cluster-level sd
setting	A list including the following values: - n.clusters = The number of clusters (nclusters) - n.time.points = The number of steps in the SW design (steps) - avg.cluster.size = The average cluster size (nsubjects) - design.matrix = The design matrix for the SWT under consideration

Author(s)

Alexandria C. Sakrejda ([e-mail])

Ken Kleinman ([e-mail])

References

Baio, G; Copas, A; Ambler, G; Hargreaves, J; Beard, E; and Omar, RZ Sample size calculation for a stepped wedge trial. Trials, 16:354. Aug 2015.

Hussey M and Hughes J. Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials. 28(2):182-91. Epub 2006 Jul 7. Feb 2007

Examples

cpa.sw.count(lambda1 = 1.75, RR = 0.9, nclusters = 21, steps = 6, nsubjects = 30, ICC = 0.01)

nickreich/clusterPower documentation built on Feb. 3, 2021, 6:54 p.m.