preference (version 1.1.6)

preference.trial: Create a Preference Trial

Description

Create a Preference Trial

Usage

preference.trial(
  pref_ss,
  pref_effect,
  selection_ss,
  selection_effect,
  treatment_ss,
  treatment_effect,
  sigma2,
  pref_prop,
  choice_prop = 0.5,
  stratum_prop = 1,
  alpha = 0.05,
  k = 1
)

Arguments

pref_ss

the sample size of the preference arm.

pref_effect

the effect size of the preference arm (delta_pi).

selection_ss

the sample size of the selection arm.

selection_effect

the effect size of selection arm (delta_nu).

treatment_ss

the sample size of the treatment arm .

treatment_effect

the sample size of the treatment arm (delta_tau)

sigma2

the variance estimate of the outcome of interest. This value 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.

pref_prop

the proportion of patients preferring treatment 1. This value should be between 0 and 1 (phi).

choice_prop

the proportion of patients assigned to choice arm in the initial randomization. Should be numeric value between 0 and 1 (default=0.5) (theta).

stratum_prop

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) (xi).

alpha

the desired type I error rate (default 0.05).

k

the ratio of treatment A to treatment B in the random arm (default 1)..

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

# NOT RUN {
# Unstratified single trial.
preference.trial(pref_ss=100, pref_effect=1, selection_ss=100, 
  selection_effect=1, treatment_ss=100, treatment_effect=1,
  sigma2=1, pref_prop=0.6)

# Stratified single trial.
preference.trial(pref_ss=100, pref_effect=1, selection_ss=100,
  selection_effect=1, treatment_ss=100, treatment_effect=1,
  sigma2=list(c(1, 0.8)), pref_prop=list(c(0.6, 0.3)),
  choice_prop=0.5, stratum_prop=list(c(0.3, 0.7)))

# Multiple trials unstratified.
preference.trial(pref_ss=100, pref_effect=seq(0.1, 2, by=0.5), 
  selection_ss=100, selection_effect=1, treatment_ss=100, 
  treatment_effect=1, sigma2=1, pref_prop=0.6)

# Multiple, stratified trials.
preference.trial(pref_ss=100, pref_effect=seq(0.1, 2, by=0.5), 
  selection_ss=100, selection_effect=1, treatment_ss=100, 
  treatment_effect=1, sigma2=list(c(1, 0.8)), pref_prop=list(c(0.6, 0.3)), 
  choice_prop=0.5, stratum_prop=list(c(0.3, 0.7)))

# }