crt.match: Cluster numbers determination for cluster randomized trails...
In designsize: Sample Size Calculation of Various Study Designs

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/crtmatch.R

Determine the number of clusters need per group for matched cluster randomized trails

1	crt.match(type, mu1, mu2, alpha, beta, sig.w, sig.bm, m, k)

`type`	There are three types of comparison i.e. type = c("M", "P", "IR"), "M" stands for comparison of means "P" stands for comparison of proportions "IR" stands for comparison of incidence rates
`mu1`	The mean/proportion/incidence rate value of the 1st group
`mu2`	The mean/proportion/incidence rate value of the 2nd group
`alpha`	Level of significance
`beta`	The probability of type-II error
`sig.w`	Standard deviation of within cluster
`sig.bm`	Standard deviation of between cluster
`m`	Number of subject in each cluster which is person-years in the case of incidence rates
`k`	Common value of the coefficient of variation for each group

In cluster-randomized trials (CRTs), matching is a technique that can be used to improve covariate balance. Matching protects against chance imbalances in baseline covariate distributions and is thought to improve study credibility. Matching is also implemented to increase study power. Pairs of similar clusters are formed and then one cluster from the pair is randomized to group 1 while the other is assigned to group 2. Now we are going to determine the number of cluster in each group.

crt.match returns a value indicating the number of clusters needed per group

Atanu Bhattacharjee, Rajashree Dey ,Soutik Halder and Akash Pawar

ABdesign crt.unmatch expsize phsize precsize prsize crsize

# (a) Comparison of means:

# This is a matched cluster randomized trials. The type ="M" indicates the comparison of
# means. The mean responses of a test group is mu1 = 0.06 and a reference group is mur =
# 0.4. The standard deviationof within cluster and between  cluster are sig.w = 0.69 and
# sig.bm = 0.224 respectively, m = 20 indicates number of subject in each cluster. alpha
# =.05 is the level of significance and the probability of type-II error is beta = 0.10.


crt.match(type="M",mu1=0.6,mu2=0.4,alpha=0.05,beta=0.20,sig.w=0.69,sig.bm=0.224,m=20)


# (b) Comparison of proportions:

# This is a matched  cluster randomized trials. Where type = "P" indicates the tests for
# comparison of proportions. The proportion of a test group is mu1 =0.01 and a reference
# group is mur = 0.0075. The Standard deviation of between cluster is sig.bm=0.224 and m
# = 2750 indicates number of subject in each cluster, alpha = 0.05 is the level of signi
# -ficance and probability of type-II error is beta = 0.10.


crt.match(type="P",mu1=0.01,mu2=0.0075,alpha=0.05,beta=0.10,sig.bm=0.0075,m=2750)


#(c) Comparison of incidence rates:

# This is a matched cluster randomized trials. Where type = "IR" indicates the tests the
# comparison of incidence rates. The incidence rate of a test group is mu1 = 4.5 and for
# reference group is mur = 3.6. A total of m = 50 person years is considered, alpha =.05
# is the level ofsignificance and the probability of type-II error is beta = 0.10. k=0.3
# indicates the common value of the coefficient of variation for each group.


crt.match(type="IR",mu1=4.5,mu2=3.6,alpha=0.05,beta=0.20,m=50,k=0.3)