Extensions to the two-stage randomized trial design for testing treatment, self-selection, and treatment preference effects to binary outcomes

2018 ◽  
Vol 37 (22) ◽  
pp. 3147-3178 ◽  
Author(s):  
Briana Cameron ◽  
Peter Peduzzi ◽  
Denise Esserman
Keyword(s):  
Randomized Trial ◽  
Trial Design ◽  
Treatment Preference ◽  
Binary Outcomes ◽  
Two Stage ◽  
Self Selection

Two‐stage randomized trial design for testing treatment, preference, and self‐selection effects for count outcomes

2020 ◽  
Vol 39 (25) ◽  
pp. 3653-3683
Author(s):  
Yu Shi ◽  
Briana Cameron ◽  
Xian Gu ◽  
Michael Kane ◽  
Peter Peduzzi ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Trial Design ◽  
Treatment Preference ◽  
Selection Effects ◽  
Two Stage ◽  
Self Selection

Sample size and power for a stratified doubly randomized preference design

2016 ◽  
Vol 27 (7) ◽  
pp. 2168-2184 ◽  
Author(s):  
Briana Cameron ◽  
Denise A Esserman
Keyword(s):  
Sample Size ◽  
Trial Design ◽  
Treatment Preference ◽  
Patient Treatment ◽  
Efficient Design ◽  
Two Stage ◽  
Entire Study ◽  
Number Of Patients ◽  
Study Population

The two-stage (or doubly) randomized preference trial design is an important tool for researchers seeking to disentangle the role of patient treatment preference on treatment response through estimation of selection and preference effects. Up until now, these designs have been limited by their assumption of equal preference rates and effect sizes across the entire study population. We propose a stratified two-stage randomized trial design that addresses this limitation. We begin by deriving stratified test statistics for the treatment, preference, and selection effects. Next, we develop a sample size formula for the number of patients required to detect each effect. The properties of the model and the efficiency of the design are established using a series of simulation studies. We demonstrate the applicability of the design using a study of Hepatitis C treatment modality, specialty clinic versus mobile medical clinic. In this example, a stratified preference design (stratified by alcohol/drug use) may more closely capture the true distribution of patient preferences and allow for a more efficient design than a design which ignores these differences (unstratified version).

scholarly journals The appropriateness of coronary investigation in myocardial injury and type 2 myocardial infarction (ACT-2): A randomized trial design

2019 ◽  
Vol 208 ◽  
pp. 11-20 ◽  
Author(s):  
Kristina Lambrakis ◽  
John K. French ◽  
Ian A. Scott ◽  
Tom Briffa ◽  
David Brieger ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Randomized Trial ◽  
Trial Design ◽  
Myocardial Injury

scholarly journals The Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) instrument was useful for refining a randomized trial design: Experiences from an investigative team

2010 ◽  
Vol 63 (11) ◽  
pp. 1271-1275 ◽  
Author(s):  
Daniel L. Riddle ◽  
Robert E. Johnson ◽  
Mark P. Jensen ◽  
Francis J. Keefe ◽  
Kurt Kroenke ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Trial Design ◽  
Investigative Team

scholarly journals Understanding randomized trial design in vascular surgery

2020 ◽  
Vol 72 (2) ◽  
pp. 771-772
Author(s):  
Graham R. McClure ◽  
William F. McIntyre ◽  
Richard P. Whitlock ◽  
Emilie P. Belley-Cote
Keyword(s):  
Vascular Surgery ◽  
Randomized Trial ◽  
Trial Design

scholarly journals A randomized trial of brief web-based prevention of unhealthy alcohol use: Participant self-selection compared to a male young adult source population

2020 ◽  
Vol 19 ◽  
pp. 100298
Author(s):  
Nicolas Bertholet ◽  
Jean-Bernard Daeppen ◽  
Joseph Studer ◽  
Emily C. Williams ◽  
John A. Cunningham ◽  
...  
Keyword(s):  
Young Adult ◽  
Alcohol Use ◽  
Randomized Trial ◽  
Source Population ◽  
Web Based ◽  
Unhealthy Alcohol Use ◽  
Unhealthy Alcohol ◽  
Self Selection
Sign in / Sign up

Export Citation Format

Share Document