effects_from_means {preference} | R Documentation |
Calculate Effect Sizes from Means
Description
Calculates the preference, selection and treatment effects given the means of each treatment group in the choice and random arms for the 2-stage randomized study.
Usage
effects_from_means(mu1, mu2, mu11, mu22, phi, nstrata = 1, xi = NULL)
Arguments
mu1 |
mean response of the patients receiving treatment 1 in the random arm. For unstratified design, should be numeric value. For the stratified design, should be vector of length equal to number of strata with each entry corresponding to stratum- specific mean. |
mu2 |
mean response of the patients receiving treatment 2 in the random arm. For unstratified design, should be numeric value. For the stratified design, should be vector of length equal to number of strata with each entry corresponding to stratum- specific mean. |
mu11 |
mean response of the patients choosing treatment 1 in the choice arm. For unstratified design, should be numeric value. For the stratified design, should be vector of length equal to number of strata with each entry corresponding to stratum- specific mean. |
mu22 |
mean response of the patients choosing treatment 2 in the choice arm. For unstratified design, should be numeric value. For the stratified design, should be vector of length equal to number of strata with each entry corresponding to stratum- specific mean. |
phi |
proportion of patients preferring treatment 1. For unstratified design, should be numeric value. For the stratified design, should be vector of length equal to number of strata with each entry corresponding to stratum-specific preference rate. All elements should be numeric values between 0 and 1. |
nstrata |
number of strata. Default is 1 (unstratified design). |
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. Should only be specified for stratified design. |
References
Rucker G (1989). "A two-stage trial design for testing treatment, self-selection and treatment preference effects." Stat Med, 8(4):477-485. (PubMed)
Examples
effects_from_means(mu1=1, mu2=2, mu11=1.5, mu22=2.5, phi=0.5)