Literature DB >> 12520558

Some fundamental issues with non-inferiority testing in active controlled trials.

H M James Hung1, Sue-Jane Wang, Yi Tsong, John Lawrence, Robert T O'Neil.   

Abstract

In an active controlled non-inferiority trial without a placebo arm, it is often not entirely clear what the primary objective is. In many cases the considered goal is to demonstrate that the experimental treatment preserves at least some fraction of the effect of the active control. The active control effect is a parameter, the value of which is unknown. To test the hypothesis of effect preservation, the classical confidence interval approach requires specification of a non-inferiority margin which is a function of the unknown active control effect. When the margin is estimated, it is also not clear what is the relevant type I error of making a false assertion about preservation of the active control effect. The statistical uncertainty of the estimated margin arguably needs to be incorporated in evaluation of the type I error. In this paper we discuss these fundamental issues. We show that the classical confidence interval approach cannot attain the target type I error exactly since this error varies as the sample size or as the values of the nuisance parameters in the active controlled trial change. In contrast, the preservation tests, as proposed in literature, can attain the target type I error rate exactly, regardless of the sample size and the values of the nuisance parameters, but can do so only at the price of several strong assumptions holding that may not be directly verifiable. One assumption is the constancy condition holding whereby the effect of the active control in the historical trial populations is assumed to carry to the population of the active control trial. When this condition is violated, both the confidence interval approach and the preservation test method may be problematic. Copyright 2003 John Wiley & Sons, Ltd.

Mesh:

Year:  2003        PMID: 12520558     DOI: 10.1002/sim.1315

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  22 in total

1.  A valid formulation of the analysis of noninferiority trials under random effects meta-analysis.

Authors:  Erica H Brittain; Michael P Fay; Dean A Follmann
Journal:  Biostatistics       Date:  2012-03-30       Impact factor: 5.899

2.  Some essential considerations in the design and conduct of non-inferiority trials.

Authors:  Thomas R Fleming; Katherine Odem-Davis; Mark D Rothmann; Yuan Li Shen
Journal:  Clin Trials       Date:  2011-08       Impact factor: 2.486

3.  Noninferiority trials: clinical understandings and misunderstandings.

Authors:  John H Powers; Thomas R Fleming
Journal:  Clin Investig (Lond)       Date:  2013-03-01

4.  Including Non-inferiority Trials in Contemporary Meta-analyses of Chronic Medical Conditions: a Meta-epidemiological Study.

Authors:  Zhen Wang; Tarek Nayfeh; Nigar Sofiyeva; Oscar J Ponte; Rami Rajjoub; Konstantinos Malandris; Mohamed Seisa; Haitao Chu; Mohammad Hassan Murad
Journal:  J Gen Intern Med       Date:  2020-04-21       Impact factor: 5.128

5.  Bayesian design of noninferiority trials for medical devices using historical data.

Authors:  Ming-Hui Chen; Joseph G Ibrahim; Peter Lam; Alan Yu; Yuanye Zhang
Journal:  Biometrics       Date:  2011-03-01       Impact factor: 2.571

6.  Denosumab versus zoledronic acid for treatment of bone metastases in men with castration-resistant prostate cancer: a randomised, double-blind study.

Authors:  Karim Fizazi; Michael Carducci; Matthew Smith; Ronaldo Damião; Janet Brown; Lawrence Karsh; Piotr Milecki; Neal Shore; Michael Rader; Huei Wang; Qi Jiang; Sylvia Tadros; Roger Dansey; Carsten Goessl
Journal:  Lancet       Date:  2011-02-25       Impact factor: 79.321

7.  Noninferiority trial design and analysis with an ordered three-level categorical endpoint.

Authors:  Erica Brittain; Zonghui Hu
Journal:  J Biopharm Stat       Date:  2009-07       Impact factor: 1.051

8.  Modelling of the outcome of non-inferiority trials by integration of historical data.

Authors:  Alberto Russu; Erik van Zwet; Giuseppe De Nicolao; Oscar Della Pasqua
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-08-21       Impact factor: 2.745

Review 9.  Balancing risk and benefit in venous thromboembolism trials: concept for a bivariate endpoint trial design and analytic approach.

Authors:  J M Kittelson; A C Spyropoulos; J L Halperin; C M Kessler; S Schulman; G Steg; A G G Turpie; N R Cutler; W R Hiatt; N A Goldenberg
Journal:  J Thromb Haemost       Date:  2013-08       Impact factor: 5.824

10.  On robustness of noninferiority clinical trial designs against bias, variability, and nonconstancy.

Authors:  Qing Liu; Yulan Li; Katherine Odem-Davis
Journal:  J Biopharm Stat       Date:  2015       Impact factor: 1.051

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