swdpwr (version 1.9)

swdpower: A function of power calculation for Stepped Wedge Design Studies

Description

This function performs power calculations for stepped wedge cluster randomized trials under different scenarios.

Usage

swdpower(
  K,
  design,
  family = "binomial",
  model = "conditional",
  link = "identity",
  type = "cross-sectional",
  meanresponse_start = NA,
  meanresponse_end0 = meanresponse_start,
  meanresponse_end1 = NA,
  effectsize_beta = NA,
  sigma2 = 0,
  typeIerror = 0.05,
  alpha0 = 0.1,
  alpha1 = alpha0/2,
  alpha2 = NA
)

Value

The object returned has a class of swdpower, which includes a list of the design matrix and a summary of this design (including the power)

Arguments

K

number of participants at each time period in a cluster, specified as the average clusterperiod size considering cluster-size variability

design

I*J dimensional data set that describes the study design (control 0, intervention 1), I is the number of clusters, J is the number of time periods. Unequal allocation of sequences and only complete designs with no transition periods are allowed

family

family of responses, specify family="gaussian" for continuous outcome and family="binomial" for binary outcome, with default value of "binomial"

model

choose from conditional model (model="conditional") and marginal model (model="marginal"), with default value of applying conditional model

link

choose link function from link="identity", link="log" and link="logit", with default value of identity link

type

choose the study type, specify type="cohort" for closed cohort study and type="cross-sectional" for cross-sectional study, with default value of cross-sectional study

meanresponse_start

the anticipated mean response in the control group at the start of the study

meanresponse_end0

the anticipated mean response in the control group at the end of the study, with default value equals to meanresponse_start (no time effects)

meanresponse_end1

the anticipated mean response in the intervention group at the end of the study

effectsize_beta

the anticipated effect size, just omit this parameter if you don't need to specify it. In all scenarios, you can choose to specify the three parameters about mean responses without specifying this effect size, or alternatively specify meanresponse_start, meanresponse_end0 and this effect size. For continuous outcomes, users can conduct power calculations by only specifying this parameter without the above three parameters about mean responses (as the power is dependent just on it), then calculation will be implemented assuming scenarios without time effects. If you would consider scenarios with time effects and continuous outcomes, please specify meanresponse_start, meanresponse_end0 (donot require accurate information, just make sure they are not equal) and this effectsize_beta.

sigma2

marginal variance of the outcome (only needed for continuous outcomes and should not be an input for binary outcomes), with default value of 0.

typeIerror

two-sided type I error, with default value of 0.05

alpha0

within-period correlation, with default value of 0.1

alpha1

between-period correlation, with default value of alpha0/2

alpha2

within-individual correlation, should not be an input under cross-sectional designs although it is numerically identical to alpha1 in this scenario by definition

Examples

library(swdpwr)
#a cross-sectional design with 12 clusters, 3 periods and binary outcomes applying conditional model
#alpha2 should not be specified, as the current version does not support power calculation using
#conditional models with binary outcomes in a cohort design
#create a 12*3 matrix which describes the study design,
#0 means control status, 1 means intervention status
dataset = matrix(c(rep(c(0,1,1),6),rep(c(0,0,1),6)),12,3,byrow=TRUE)

#specify meanresponse_start, meanresponse_end0 and meanresponse_end1
swdpower(K = 30, design = dataset, family = "binomial", model = "conditional", link = "logit",
type = "cross-sectional", meanresponse_start = 0.2, meanresponse_end0 = 0.3,
meanresponse_end1 = 0.4, typeIerror = 0.05, alpha0 = 0.01, alpha1 = 0.01)

#specify meanresponse_start, meanresponse_end0 and effectsize_beta
swdpower(K = 30, design = dataset, family = "binomial", model = "conditional", link = "logit",
type = "cross-sectional", meanresponse_start = 0.2, meanresponse_end0 = 0.3, effectsize_beta = 0.6,
typeIerror = 0.05, alpha0 = 0.01, alpha1 = 0.01)

#a cohort design with 8 clusters, 3 periods and continuous outcomes applying marginal model
#sigma2 should be specified, as continuous outcomes require marginal variance in calculation
#create a 8*3 matrix which describes the study design,
#0 means control status, 1 means intervention status
dataset = matrix(c(rep(c(0,1,1),4),rep(c(0,0,1),4)),8,3, byrow=TRUE)

#specify meanresponse_start, meanresponse_end0 and meanresponse_end1
swdpower(K = 24, design = dataset, family = "gaussian", model = "marginal", link = "identity",
type = "cohort", meanresponse_start = 0.1, meanresponse_end0 = 0.2,  meanresponse_end1 = 0.4,
sigma2 = 0.095, typeIerror = 0.05, alpha0 = 0.03, alpha1 = 0.015, alpha2 = 0.2)

#specify effectsize_beta only, then the program runs assuming no time effects
swdpower(K = 24, design = dataset, family = "gaussian", model = "marginal", link = "identity",
type = "cohort",effectsize_beta=0.3, sigma2 = 0.095, typeIerror = 0.05, alpha0 = 0.03,
alpha1 = 0.015, alpha2 = 0.2)