Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
Evaluation Review
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Cappelleri, J. C.
Right arrow Articles by Trochim, W. M.K.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Cappelleri, J. C.
Right arrow Articles by Trochim, W. M.K.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Power Analysis of Cutoff-Based Randomized Clinical Trials

Joseph C. Cappelleri

New England Medical Center

Richard B. Darlington

Cornell University

William M.K. Trochim

Cornell University

A cutoff-based randomized clinical trial couples cutoff-based assignment on an appropriate covariate with random assignment to help balance ethical and scientific concerns in certain situations. A statistical power algorithm based on the Fisher Z method is developed that is particular to and inclusive of cutoff-based random clinical trials and the single cutoff-point (regression-discontinuity) design, which has no randomization. This article quantifies power and sample size estimates for varying levels of randomization and cutoff-based assignment. Although more randomization engenders greater statistical power, less randomization requires a much larger increase in sample size for small treatment effects.

Evaluation Review, Vol. 18, No. 2, 141-152 (1994)
DOI: 10.1177/0193841X9401800202
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICSHome page
P. Z. Schochet
Statistical Power for Regression Discontinuity Designs in Education Evaluations
Journal of Educational and Behavioral Statistics, June 1, 2009; 34(2): 238 - 266.
[Abstract] [Full Text] [PDF]

Home page
 Eval RevHome page
M. B. Mandell
Having One's Cake and Eating It, Too: Combining True Experiments With Regression Discontinuity Designs
Eval Rev, October 1, 2008; 32(5): 415 - 434.
[Abstract] [PDF]

Home page
 Med Decis MakingHome page
J. C. Cappelleri and W. M.K. Trochim
Ethical and Scientific Features of Cutoff-based Designs of Clinical Trials: A Simulation Study
Med Decis Making, October 1, 1995; 15(4): 387 - 394.
[Abstract] [PDF]

Home page
 Eval RevHome page
C. S. Reichardt, W. M.K. Trochim, and J. C. Cappelleri
Reports of the Death of Regression-Discontinuity Analysis are Greatly Exaggerated
Eval Rev, February 1, 1995; 19(1): 39 - 63.
[Abstract] [PDF]