# treatment_power: Treatment Effect Power Calculation In preference: 2-Stage Clinical Trial Design and Analysis

## Description

Calculates the study power to detect the treatment effect given a particular sample size in a two-stage randomized clinical trial

## Usage

 ```1 2``` ```treatment_power(N, sigma2, delta_tau, alpha = 0.05, theta = 0.5, xi = 1, nstrata = 1) ```

## Arguments

 `N` overall study sample size. `sigma2` variance estimate. Should be positive numeric values. If study is stratified, should be vector of within-stratum variances with length equal to the number of strata in the study. `delta_tau` overall study treatment effect. `alpha` desired type I error rate.. `theta` proportion of patients assigned to choice arm in the initial randomization. Should be numeric value between 0 and 1 (default=0.5). `xi` a numeric vector of the proportion of patients in each stratum. Length of vector should equal the number of strata in the study and sum of vector should be 1. All vector elements should be numeric values between 0 and 1. Default is 1 (i.e. unstratified design). `nstrata` number of strata. Default is 1 (i.e. unstratified design).

## References

Turner RM, et al. (2014). "Sample Size and Power When Designing a Randomized Trial for the Estimation of Treatment, Selection, and Preference Effects." Medical Decision Making, 34:711-719. (PubMed)

Cameron B, Esserman D (2016). "Sample Size and Power for a Stratified Doubly Randomized Preference Design." Stat Methods Med Res. (PubMed)

## Examples

 ```1 2 3 4``` ```# Unstratified treatment_power(N=300, sigma2=1, delta_tau=0.5) # Stratified treatment_power(N=300, sigma2=c(1,1), delta_tau=0.5, xi=c(0.5,0.5), nstrata=2) ```

preference documentation built on Nov. 29, 2017, 1:01 a.m.