DE.woert: Computes the Design Effect for a Stepped Wedge Trial

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

Computes the Design Effect for a Stepped Wedge Trial

Description

Sample size calculations for a SWT using a cross-sectional design. This is based on (the correct version) of Woertman et al (2013), as described in Baio et al (2015).

Usage

DE.woert(
  outcome = "cont",
  input,
  K,
  J,
  B = 1,
  T = 1,
  rho,
  sig.level = 0.05,
  power = 0.8
)

Arguments

outcome	String. Type of outcome. Options are cont, bin or count
input	input = a list containing the arguments. This differs depending on the type of outcome, as follows: - continuous outcome: 1) delta (treatment effect) 2) sd (standard deviation) - binary outcome: 1) p1 (baseline probability of outcome) 2) either p2 (treatment probability of outcome), or OR (treatment effect as OR) - count outcome: 1) r1 (baseline rate of outcome) 2) either r2 (treatment rate of outcome), or RR (treatment effect as RR)
K	average cluster size
J	number of time points (excluding baseline)
B	number of baseline measurement times
T	number of measurement times during each crossover
rho	ICC
sig.level	significance level (default = 0.05)
power	Power (default = 0.8)

Value

n.cls.swt	Number of clusters required to reach the pre-specified power with the given significance level.
n.pts	The total number of participants required.
DE.woert	The resulting Design Effect.
CF	The resulting Correction Factor.
n.rct	The original individual RCT sample required to reach the pre-specified power with the given significance level.

Author(s)

Gianluca Baio

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.

Examples

# Continuous outcome
input <- list(delta=-0.3875,sd=1.55)
K <- 20
J <- 5
rho <- .2
DE.woert(input=input,K=K,J=J,rho=rho)
#
# Binary outcome
input <- list(OR=.53,p1=.26)
DE.woert(outcome="bin",input=input,K=K,J=J,rho=rho)
#
# Count outcome
input <- list(RR=.8,r1=1.5)
DE.woert(outcome="count",input=input,K=K,J=J,rho=rho)

giabaio/SWSamp documentation built on Nov. 14, 2022, 2:24 p.m.