2-Stage Preference Trial Design and Analysis
Calculate Effect Sizes from Means
Fit the Preference Data Collected from a Two-stage Clinical Trial
Fit Preference Model from Summary Data
Unstratified Optimized Theta
Power Calculation from Sample Size
Power Calculation from Sample Size
Overall Sample Size
Overall Sample Size Binomial
Overall Sample Size Poisson Distribution
Plot the effect sizes of a preference trial
Design and Analysis of Two-stage Randomized Clinical Trials
Fit Preference Data Collected from a Two-stage Clinical Trial
Create a Preference Trial
Preference trial parameter accessors
Design Preference Trials with Power Constraint(s)
Design Preference Trials with Sample Size Constraint(s)
Power Calculation from Sample Size
Treatment Effect Back Calculation
Design and analyze two-stage randomized trials with a continuous outcome measure. The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data <doi:10.18637/jss.v094.c02>.
Useful links