The preference package is used for the design and analysis of two-stage randomized trials with a continuous outcome measure. In this study, patients are first randomized to either a random or choice arm. Patients initially randomized to the choice arm are allowed to select their preferred treatment from the available treatment options; patients initially randomized to the random arm undergo a second randomization procedure to one of the available treatment options. The design has also been extended to include important stratification variables; the functions provided in this package can accommodate both the unstratified and stratified designs.

In this study, there are three effects that may be of interest. The treatment effect captures the difference in outcome between patients randomized to treatment A and treatment B (similar to a traditional RCT). The selection effect captures the difference in outcome between patients that prefer treatment A and patients that prefer treatment B, regardless of the treatment that is actually received. Finally, the preference effect compares the outcomes of patients who receive their preferred treatment (either treatment A or treatment B) and patients who do not receive their preferred treatment.

To aid in the design of these two-stage randomized studies, sample size
functions are provided to determine the necessary sample size to
detect a particular selection, preference, and/or treatment effect. If the
sample size is fixed prior to the start of the study, functions are provided
to calculate the study power to detect each effect. Finally, the
`optimal_proportion`

function can be used to determine the optimal
proportion of patients randomized to the choice arm in the initial
randomization.

To analyze the data from the two-stage randomized trial, two analysis
functions are provided. The function `analyze_raw_data`

computes the
test statistic and p-value for each effect given provided raw study data.
The function `analyze_summary_data`

uses provided summary data (mean,
variance, and sample size) of each study group to compute the test statistic
and p-value of each effect.

Sample Size Function calls

selection_sample_size: required sample size to detect a given selection effect

preference_sample_size: required sample size to detect a given preference effect

treatment_sample_size: required sample size to detect a given treatment effect

overall_sample_size: required sample size to detect a given set of selection, preference, and treatment effects

Power Function Calls

selection_power: study power to detect a given selection effect

preference_power: study power to detect a given preference effect

treatment_power: study power to detect a given treatment effect

overall_power: study power to detect a given set of selection, preference, and treatment effects

Analysis Function Calls

analyze_raw_data: computes test statistic and p-value for observed selection, preference, and treatment effects using provided raw data

analyze_summary_data: computes test statistic and p-value for observed selection, preference, and treatment effects using provided summary data (mean, variance, sample size)

Other Function Calls

treatment_effect_size: computes the treatment effect that can be detected given a specified sample size and power

optimal_proportion: computes the optimal proportion randomized to choice arm (defined for unstratified design only)

effects_from_means: computes the treatment, selection, and preference effect sizes provided the study means in each treatment arm

Data Sets

imap: summary SF36 outcome data for the two-stage randomized IMAP study

imap_strat: summary SF36 outcome data for the two-stage randomized IMAP study stratified by high vs. low STAI score

Acknowledgements: This work was partially supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (ME-1511-32832) and Yale's CTSA Award (Ul1TR001863). We would also like to thank the IMAP team for sharing their data to demonstrate this package.

Disclaimer: All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.

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

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