Notes on testing noninferiority in ordinal data under the parallel groups design.

Abstract

When testing the noninferiority of an experimental treatment to a standard (or control) treatment in a randomized clinical trial (RCT), we may come across the outcomes of patient response on an ordinal scale. We focus our discussion on testing noninferiority in ordinal data for an RCT under the parallel groups design. We develop simple test procedures based on the generalized odds ratio (GOR). We note that these test procedures not only can account for the information on the order of ordinal responses without assuming any specific parametric structural model, but also can be independent of any arbitrarily subjective scoring system. We further develop sample size determination based on the test procedure using the GOR. We apply Monte Carlo simulation to evaluate the performance of these test procedures and the accuracy of sample size calculation formula proposed here in a variety of situations. Finally, we employ the data taken from a trial comparing once-daily gatifloxican with three-times-daily co-amoxiclav in the treatment of community-acquired pneumonia to illustrate the use of these test procedures and sample size calculation formula.

• Authors: Kung-Jong Lui, Kuang-Chao Chang
• Journal: Journal of biopharmaceutical statistics (J Biopharm Stat) Vol. 23 Issue 6 Pg. 1294-307 ( 2013) ISSN: 1520-5711 [Electronic] England
• PMID: 24138433 (Publication Type: Journal Article)

Chemical References

• Anti-Bacterial Agents
• Fluoroquinolones
• Amoxicillin-Potassium Clavulanate Combination
• Gatifloxacin

Topics

• Amoxicillin-Potassium Clavulanate Combination (therapeutic use)
• Anti-Bacterial Agents (therapeutic use)
• Community-Acquired Infections (drug therapy, microbiology)
• Data Interpretation, Statistical
• Fluoroquinolones (therapeutic use)
• Gatifloxacin
• Humans
• Models, Statistical
• Monte Carlo Method
• Odds Ratio
• Pneumonia, Bacterial (drug therapy, microbiology)
• Randomized Controlled Trials as Topic (statistics & numerical data)
• Sample Size
• Treatment Outcome