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

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

 `1` ```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.

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# 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 July 15, 2017, 4:22 a.m.