preference-package: Design and Analysis of Two-stage Randomized Clinical Trials
In kaneplusplus/preference: 2-Stage Preference Trial Design and Analysis
Description
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 preference computes the
test statistic and p-value for each effect given provided raw study data.
The function fit_preference_summary uses provided summary data (mean,
variance, and sample size) of each study group to compute the test statistic
and p-value of each effect. The test statistics can be accessed from
the models using the summary() function.
Preference Trial Function Calls:
preference.trial: construct a preferene.trial based
on effect and sample sizes.
pt_from_power: construct a preference.trial based on
power and effect size.
pt_from_ss: construct a preference.trial based on
sample size
Analysis Function Calls
preference and fit_preference: computes test statistic and
p-value for observed #' selection, preference, and treatment effects
using provided raw data
fit_preference_summary: 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
Acknowledgments: 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.
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)
McCaffery et al. (2010) "Psychosocial outcomes of three triage
methods for the management of borderline abnormal cervical smears: an open
randomised trial." BMJ, 340:b4491.
(PubMed)
Walter et. al. (2011). "Optimal allocation of participants for
the estimation of selection, preference and treatment effects in the
two-stage randomised trial design." Stat Med,
31(13):1307-1322.
(PubMed)
McCaffery et al. (2011) "Determining the Impact of Informed
Choice: Separating Treatment Effects from the Effects of Choice and Selection
in Randomized Trials." Med Decis Making, 31(2):229-236.
(PubMed)
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)
kaneplusplus/preference documentation built on Sept. 12, 2020, 12:37 p.m.
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Description
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 preference computes the
test statistic and p-value for each effect given provided raw study data.
The function fit_preference_summary uses provided summary data (mean,
variance, and sample size) of each study group to compute the test statistic
and p-value of each effect. The test statistics can be accessed from
the models using the summary() function.
Preference Trial Function Calls:
preference.trial: construct a
preferene.trialbased on effect and sample sizes.pt_from_power: construct a
preference.trialbased on power and effect size.pt_from_ss: construct a
preference.trialbased on sample size
Analysis Function Calls
preference and fit_preference: computes test statistic and p-value for observed #' selection, preference, and treatment effects using provided raw data
fit_preference_summary: 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
Acknowledgments: 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.
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)
McCaffery et al. (2010) "Psychosocial outcomes of three triage methods for the management of borderline abnormal cervical smears: an open randomised trial." BMJ, 340:b4491. (PubMed)
Walter et. al. (2011). "Optimal allocation of participants for the estimation of selection, preference and treatment effects in the two-stage randomised trial design." Stat Med, 31(13):1307-1322. (PubMed)
McCaffery et al. (2011) "Determining the Impact of Informed Choice: Separating Treatment Effects from the Effects of Choice and Selection in Randomized Trials." Med Decis Making, 31(2):229-236. (PubMed)
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)
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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