cluster.search: Finds the optimum number of clusters or time points

In giabaio/SWSamp: Computes Sample Size for a Stepped Wedge Design, using Simulation-Based Calculations

Finds the optimum number of clusters or time points

Description

Given some inputs determines the optimal combination of clusters/time points to get a set level of power.

Usage

cluster.search(
  target.power = NULL,
  I = NULL,
  J = NULL,
  H = NULL,
  K,
  design = "cross-sec",
  mu = 0,
  b.trt,
  b.time = NULL,
  sigma.y = NULL,
  sigma.e = NULL,
  rho = NULL,
  sigma.a = NULL,
  rho.ind = NULL,
  sigma.v = NULL,
  n.sims = 1000,
  formula = NULL,
  family = "gaussian",
  natural.scale = TRUE,
  sig.level = 0.05,
  n.cores = NULL,
  ...
)

Arguments

target.power	The target power (eg 0.8)
I	A vector specifying the range in which to search for the optimal number of clusters, eg I=c(1,10)
J	Number of time points
H	Number of units randomised at each time point
K	Average size of each cluster
design	type of design. Can be 'cross-sec' (default) or 'cohort' (repeated measurements)
mu	baseline outcome value
b.trt	Treatment effect
b.time	Time effect
sigma.y	total standard deviation
sigma.e	individual standard deviation
rho	Intra-class correlation coefficient
sigma.a	the sd of the the cluster-level intercept (default at NULL)
rho.ind	individual-level ICC (for cohorts)
sigma.v	the sd of the cluster-level slope (by intervention, default at NULL)
n.sims	Number of simulations to be used (default=1000)
formula	Formula describing the model to be used
family	The model family to be used. Default value is 'gaussian' and other possibile choices are 'binomial' or 'poisson'
natural.scale	Indicator for whether the input is passed on the natural scale or on the scale of the linear predictor. By default is set to TRUE. In the case of family='gaussian' it does not have any effect, since the link for the linear predictor is the identity. But for family='binomial' or family='poisson', the user has to specify when the input is given on the logit or log scale
sig.level	Significance level (default=0.05)
n.cores	Specifies the number of processors to be used for the computation (default=NULL, which means that R will try and figure out)
...	Additional arguments

Value

Optimum_I	The value of the optimal number of clusters
power	The estimated power in correspondence of the optimal I
time2run	Computational time

Author(s)

Rosie Leach

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

#cluster.search(I=c(4,10),target.power=.8,J=6,K=30,mu=1.5,b.trt=.8,rho=0,
#family="poisson",n.sims=10)

giabaio/SWSamp documentation built on June 12, 2025, 8:48 p.m.