The New England Journal of Statistics in Data Science logo


  • Help
Login Register

  1. Home
  2. Issues
  3. Volume 2, Issue 1 (2024)
  4. Non-inferiority Clinical Trials: Treatin ...

The New England Journal of Statistics in Data Science

Submit your article Information Become a Peer-reviewer
  • Article info
  • Full article
  • More
    Article info Full article

Non-inferiority Clinical Trials: Treating Margin as Missing Information
Volume 2, Issue 1 (2024), pp. 104–111
Yulia Sidi   Benjamin Stockton ORCID icon link to view author Benjamin Stockton details   Ofer Harel  

Authors

 
Placeholder
https://doi.org/10.51387/24-NEJSDS57
Pub. online: 1 February 2024      Type: Methodology Article      Open accessOpen Access
Area: Cancer Research

Accepted
2 January 2024
Published
1 February 2024

Abstract

Non-inferiority (NI) clinical trials’ goal is to demonstrate that a new treatment is not worse than a standard of care by a certain amount called margin. The choice of non-inferiority margin is not straightforward as it depends on historical data, and clinical experts’ opinion. Knowing the “true”, objective clinical margin would be helpful for design and analysis of non-inferiority trials, but it is not possible in practice. We propose to treat non-inferiority margin as missing information. In order to recover an objective margin, we believe it is essential to conduct a survey among a group of representative clinical experts. We introduce a novel framework, where data obtained from a survey are combined with NI trial data, so that both an estimated clinically acceptable margin and its uncertainty are accounted for when claiming non-inferiority. Through simulations, we compare several methods for implementing this framework. We believe the proposed framework would lead to better informed decisions regarding new potentially non-inferior treatments and could help resolve current practical issues related to the choice of the margin.

References

[1] 
Akande, O., Li, F. and Reiter, J. (2017). An Empirical Comparison of Multiple Imputation Methods for Categorical Data. The American Statistician 71(2) 162–170. https://doi.org/10.1080/00031305.2016.1277158. MR3668704
[2] 
Althunian, T. A., de Boer, A., Klungel, O. H., Insani, W. N. and Groenwold, R. H. (2017). Methods of Defining the Non-Inferiority Margin in Randomized, Double-Blind Controlled Trials: A Systematic Review. Trials 18(1) 107.
[3] 
Blackwelder, W. C. (1982). “Proving the Null Hypothesis” in Clinical Trials. Controlled Clinical Trials 3(4) 345–353.
[4] 
Burgette, L. F. and Reiter, J. P. (2010). Multiple Imputation for Missing Data via Sequential Regression Trees. American Journal of Epidemiology 172(9) 1070–1076.
[5] 
CHMP (2006). Committee for Medicinal Products for Human Use (CHMP) Guideline on the Choice of the Non-Inferiority Margin. Statistics in Medicine 25(10) 1628. https://doi.org/10.1002/sim.3367. MR2542359
[6] 
Daniels, M. J. and Hogan, J. W. (2008) Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Chapman and Hall/CRC. MR2656068
[7] 
Dann, R. S. and Koch, G. G. (2008). Methods for One-Sided Testing of the Difference between Proportions and Sample Size Considerations Related to Non-Inferiority Clinical Trials. Pharmaceutical Statistics 7(2) 130–141.
[8] 
Eriksson, B. I., Dahl, O. E., Rosencher, N., Kurth, A. A., van Dijk, C. N., Frostick, S. P., Prins, M. H., Hettiarachchi, R., Hantel, S., Schnee, J. et al. (2007). Dabigatran Etexilate versus Enoxaparin for Prevention of Venous Thromboembolism after Total Hip Replacement: A Randomised, Double-Blind, Non-Inferiority Trial. The Lancet 370(9591) 949–956.
[9] 
Eriksson, B. I., Dahl, O. E., Huo, M. H., Kurth, A. A., Hantel, S., Hermansson, K., Schnee, J. M., Friedman, R. J., Group, R. q. N. I. S. et al. (2011). Oral Dabigatran versus Enoxaparin for Thromboprophylaxis after Primary Total Hip Arthroplasty (RE-NOVATE II). Thrombosis and Haemostasis 105(04) 721–729.
[10] 
FDA (2016). Non-Inferiority Clinical Trials.
[11] 
Harel, O. (2007). Inferences on Missing Information under Multiple Imputation and Two-Stage Multiple Imputation. Statistical Methodology 4(1) 75–89. https://doi.org/10.1016/j.stamet.2006.03.002. MR2339010
[12] 
Harel, O. and Zhou, X.-H. (2007). Multiple Imputation: Review of Theory, Implementation and Software. Statistics in Medicine 26(16) 3057–3077. https://doi.org/10.1002/sim.2787. MR2380504
[13] 
Hung, H. J. and Wang, S.-J. (2013). Statistical Considerations for Noninferiority Trial Designs without Placebo. Statistics in Biopharmaceutical Research 5(3) 239–247.
[14] 
Hung, H. J., Wang, S.-J. and O’Neill, R. (2005). A Regulatory Perspective on Choice of Margin and Statistical Inference Issue in Non-Inferiority Trials. Biometrical Journal: Journal of Mathematical Methods in Biosciences 47(1) 28–36. https://doi.org/10.1002/bimj.200410084. MR2135887
[15] 
Hung, H. J., Wang, S.-J. and O’Neill, R. (2007). Issues with Statistical Risks for Testing Methods in Noninferiority Trial without a Placebo Arm. Journal of Biopharmaceutical Statistics 17(2) 201–213. https://doi.org/10.1080/10543400601177343. MR2345704
[16] 
Hung, H. J., Wang, S.-J. and O’Neill, R. (2009). Challenges and Regulatory Experiences with Non-Inferiority Trial Design without Placebo Arm. Biometrical Journal: Journal of Mathematical Methods in Biosciences 51(2) 324–334. https://doi.org/10.1002/bimj.200800219. MR2668686
[17] 
Hung, H. J., Wang, S.-J., Tsong, Y., Lawrence, J. and O’Neil, R. T. (2003). Some Fundamental Issues with Non-Inferiority Testing in Active Controlled Trials. Statistics in Medicine 22(2) 213–225.
[18] 
ICH (2000). International Conference on Harmonisation. Choice of Control Group and Related Issues in Clinical Trials E10.
[19] 
Julious, S. A. and Owen, R. J. (2011). A Comparison of Methods for Sample Size Estimation for Non-Inferiority Studies with Binary Outcomes. Statistical Methods in Medical Research 20(6) 595–612. https://doi.org/10.1177/0962280210378945. MR2866347
[20] 
Kohl, M., Ruckdeschel, P. and Stabla, T. (2005). General Purpose Convolution Algorithm for Distributions in S4-Classes by Means of FFT. Technical Report, Citeseer.
[21] 
Little, R. J. and Rubin, D. B. (2014) Statistical Analysis with Missing Data 333. John Wiley & Sons. https://doi.org/10.1002/9781119013563. MR1925014
[22] 
Liu, Q., Li, Y. and Odem- Davis, K. (2015). On Robustness of Noninferiority Clinical Trial Designs against Bias, Variability, and Nonconstancy. Journal of Biopharmaceutical Statistics 25(1) 206–225. https://doi.org/10.1080/10543406.2014.923738. MR3301347
[23] 
Mauri, L. and D’Agostino Sr, R. B. (2017). Challenges in the Design and Interpretation of Noninferiority Trials. New England Journal of Medicine 377(14) 1357–1367.
[24] 
Ng, T.-H. (2008). Noninferiority Hypotheses and Choice of Noninferiority Margin. Statistics in Medicine 27(26) 5392–5406. https://doi.org/10.1002/sim.3367. MR2542359
[25] 
Rabe, B. A., Day, S., Fiero, M. H. and Bell, M. L. (2018). Missing Data Handling in Non-Inferiority and Equivalence Trials: A Systematic Review. Pharmaceutical Statistics 41(4) 815–830. https://doi.org/10.1002/sim.9251. MR4386982
[26] 
Radford, J., Illidge, T., Counsell, N., Hancock, B., Pettengell, R., Johnson, P., Wimperis, J., Culligan, D., Popova, B., Smith, P., McMillan, A., Brownell, A., Kruger, A., Lister, A., Hoskin, P., O’Doherty, M. and Barrington, S. (2015). Results of a Trial of PET-Directed Therapy for Early-Stage Hodgkin’s Lymphoma. New England Journal of Medicine 372(17) 1598–1607. https://doi.org/10.1056/NEJMoa1408648.
[27] 
Raghunathan, T., Berglund, P. A. and Solenberger, P. W. (2018) Multiple Imputation in Practice: With Examples Using IVEware. Chapman and Hall/CRC.
[28] 
Rehal, S., Morris, T. P., Fielding, K., Carpenter, J. R. and Phillips, P. P. (2016). Non-Inferiority Trials: Are They Inferior? A Systematic Review of Reporting in Major Medical Journals. BMJ Open 6(10) 012594.
[29] 
Rubin, D. B. (1976). Inference and Missing Data. Biometrika 63(3) 581–592. https://doi.org/10.1093/biomet/63.3.581. MR0455196
[30] 
Rubin, D. B. (2004) Multiple Imputation for Nonresponse in Surveys 81. John Wiley & Sons. MR2117498
[31] 
Ruckdeschel, P., Kohl, M., Stabla, T. and Camphausen, F. (2006). S4 Classes for Distributions. R News 6(2) 2–6.
[32] 
Schafer, J. L. (1997) Analysis of Incomplete Multivariate Data. Chapman and Hall/CRC. https://doi.org/10.1201/9781439821862. MR1692799
[33] 
Schafer, J. L. (1999). Multiple Imputation: A Primer. Statistical Methods in Medical Research 8(1) 3–15.
[34] 
Schiller, P., Burchardi, N., Niestroj, M. and Kieser, M. (2012). Quality of Reporting of Clinical Non-Inferiority and Equivalence Randomised Trials – Update and Extension. Trials 13 214. https://doi.org/10.1186/1745-6215-13-214.
[35] 
Sidi, Y. and Harel, O. (2018). The Treatment of Incomplete Data: Reporting, Analysis, Reproducibility, and Replicability. Social Science & Medicine 209 169–173.
[36] 
Sidi, Y. and Harel, O. (2021). Noninferiority Clinical Trials With Binary Outcome: Statistical Methods Used in Practice. Statistics in Biopharmaceutical Research 13(4) 476–482. https://doi.org/10.1080/19466315.2020.1796780.
[37] 
van Buuren, S. and Groothuis- Oudshoorn, K. (2010). Mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software 1–68.
[38] 
VanderBeek, B. L., Ying, G.-S. and Hubbard, R. A. (2021). Survival Analysis vs Longitudinal Modeling With Multiple Imputation—A False Dichotomy. JAMA Ophthalmology 139(5) 588. https://doi.org/10.1001/jamaophthalmol.2021.0508.
[39] 
Wangge, G., Klungel, O. H., Roes, K. C., De Boer, A., Hoes, A. W. and Knol, M. J. (2010). Room for Improvement in Conducting and Reporting Non-Inferiority Randomized Controlled Trials on Drugs: A Systematic Review. PLoS One 5(10) 13550.
[40] 
White, I. R. and Royston, P. (2009). Imputing Missing Covariate Values for the Cox Model. Statistics in Medicine 28(15) 1982–1998. https://doi.org/10.1002/sim.3618. https://doi.org/10.1002/sim.3618. MR2750806
[41] 
Zhao, Y., Herring, A. H., Zhou, H., Ali, M. W. and Koch, G. G. (2014). A Multiple Imputation Method for Sensitivity Analysis of Time-to-Event Data with Possibly Informative Censoring. Journal of Biopharmaceutical Statistics 24(2) 229–253. https://doi.org/10.1080/10543406.2013.860769. MR3196139

Full article PDF XML
Full article PDF XML

Copyright
© 2024 New England Statistical Society
by logo by logo
Open access article under the CC BY license.

Keywords
Incomplete data Margin justification Multiple imputation Non-inferiority Survey

Funding
This project was partially supported by Award Number DMS-2015320 from the National Science Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation.

Metrics
since December 2021
272

Article info
views

136

Full article
views

193

PDF
downloads

40

XML
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

The New England Journal of Statistics in Data Science

  • ISSN: 2693-7166
  • Copyright © 2021 New England Statistical Society

About

  • About journal

For contributors

  • Submit
  • OA Policy
  • Become a Peer-reviewer
Powered by PubliMill  •  Privacy policy