Abstract

No abstract extracted

Information

Preprint No.SS-2025-0213
Manuscript IDSS-2025-0213
Complete AuthorsOlga M Kuznetsova, Victoria P Johnson, Michael Gekhtman
Corresponding AuthorsOlga M Kuznetsova
Emailsolga_kuznetsova@merck.com

References

  1. Baldi Antognini, A. and Giovagnoli, A. (2004). A new ‘biased coin’ design for the sequential allocation to two treatments. Journal of the Royal Statistical Society 53(4), 651–664.
  2. Barbachano, Y. and Coad D.S. (2013). Inference following designs which adjust for imbalances in prognostic factors. Clinical Trials 10, 540–551.
  3. Birkett, N .J. (1985). Adaptive allocation in randomized controlled trials. Controlled Clinical Trials 6, 146–155.
  4. Bugni,F.A., Canay I.A. and A. M. Shaikh. (2018). Inference under covariate-adaptive randomization. Journal of the American Statistical Association 113, 1784–1796.
  5. Cox, D. R. (1975). Partial Likelihood. Biometrika 62, 269–276.
  6. Cox, D. R. (1972). Regression Models and Life Tables. Journal of the Royal Statistical Society, Ser. B 34, 187–220.
  7. Efron, B. (1971.) Forcing a sequential experiment to be balanced. Biometrika 58, 403–417.
  8. Forsythe, A. B. (1987). Validity and power of tests when groups have been balanced for prognostic factors. Computational Statistics and Data Analysis 5, 193–200.
  9. Freedman, L. S. and White S.J. (1976). On the Use of Pocock and Simon’s Method for Balancing Treatment Numbers over Prognostic Factors in the Controlled Clinical Trial. Biometrics 32(3), 691–694.
  10. Hu, F., Hu, Y., Ma, Z. and Rosenberger W.F. (2014). Adaptive randomization for balancing over covariates. Wiley Interdisciplinary Reviews: Computational Statistics 6(4), 288–303.
  11. Hu, F., Ye, X. and L. Zhang. (2023). Multi-arm covariate-adaptive randomization. Science China Mathematics 66(1), 163–190.
  12. Hu, Y. and Hu F. (2012). Asymptotic properties of covariate-adaptive randomization. The Annals of Statistics 40, 1794–1815.
  13. Johnson, V. P., Gekhtman, M. and Kuznetsova O.M. (2024). Validity of Tests for Time-toEvent Endpoints in Studies with the Pocock and Simon Covariate-Adaptive Randomization. Statistics in Biopharmaceutical Research 16(4), 468–482.
  14. Kahan, B. C. and Morris T.P. (2012). Improper analysis of trials randomised using stratified blocks or minimisation. Statistics in Medicine 31, 328–340.
  15. Kuznetsova, O. M. and Tymofyeyev Y. (2012). Preserving the allocation ratio at every allocation with biased coin randomization and minimization in studies with unequal allocation. Statistics in Medicine 31, 701–723.
  16. Lin, D. Y. and Wei L.J. (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association 84, 1074–1078.
  17. Luo, X., Li, M., Xu, G. and Tu, D. (2016). Survival analysis following dynamic randomization. Contemp Clinical Trials Communications 3, 39–47.
  18. Ma, W., Hu, F. and Zhang, L. (2015). Testing hypotheses of covariate-adaptive randomized clinical trials. Journal of the American Statistical Association 110(510), 669–680.
  19. Liu, H., Tu, F. and Ma, W. (2022). Lasso-adjusted treatment effect estimation under covariateadaptive randomization. Biometrika asac036. https://doi.org/10.1093/biomet/asac036
  20. Ma, W., Tu, F. and Liu, H. (2022). Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective. Statistics in Medicine 41, 5645–5661.
  21. Peto, R., Pike, M. C., Armitage, P., Breslow, N. E., Cox, D.R., Howard, S. V., Mantel, N.,
  22. McPherson, K., Peto, J. and Smith, P. G. (1976). Design and analysis of randomized clinical trials requiring prolonged observation of each patient: I. introduction and design. British Journal of Cancer 34, 585–612.
  23. Pocock, S. J. and Simon, R. (1975). Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 31, 103–115.
  24. Rosenberger, W. F. and Sverdlov, O. (2008). Handling covariates in the design of clinical trials. Statistical Science 23(3), 404–419.
  25. Rosenberger, W. F. and Lachin, J.M. (2016). Randomization in Clinical Trials: Theory and Practice (2nd ed.). John Wiley & Sons, New York.
  26. Scott, N. W., McPherson, G. C., Craig, R., Ramsay, C. R. and Campbell, M. K. (2002). The method of minimization for allocation to clinical trials: a review. Controlled Clinical Trials 23, 662–674.
  27. Shao, J. and Tu, D. (2012). The jackknife and bootstrap. Springer Science & Business Media.
  28. Shao, J. and Yu, X. (2010). A theory for testing hypotheses under covariate-adaptive randomization. Biometrika 97, 347–360.
  29. Shao, J. and Yu, X. (2013). Validity of tests under covariate-adaptive biased coin randomization and generalized linear models. Biometrics 69, 960–969.
  30. Shao, J. (2021). Inference after covariate-adaptive randomisation: aspects of methodology and theory. Statistical Theory and Related Fields 5(3), 172–186.
  31. Taves, D. (1974). Minimization: a new method of assigning subjects to treatment and control groups. Clinical Pharmacology and Therapeutics 15, 443–453.
  32. Taves, D. (2010). The use of minimization in clinical trials. Contemporary Clinical Trials 31, 180–184.
  33. Therneau, T. M. and Grambsch, P. M. (2000). Modelling survival data: extending the Cox model. Spring-Verlag, New York.
  34. Therneau, T. M. (1973). How many stratification factors are ‘too many’ to use in a randomization plan. Controlled Clinical Trials 14, 98–108.
  35. Wei, L. J. (1978). An application of an urn model to the design of sequential controlled clinical trials. Journal of American Statistical Association 73, 559–563.
  36. Wei, L. J. (1978). The adaptive biased coin design for sequential experiments. The Annals of Statistics 6, 92–100.
  37. Weir, C. J. and Lees, K. R. (2003). Comparison of stratification and adaptive methods for treatment allocation in an acute stroke clinical trial. Statistics in Medicine 22, 705–726.
  38. Ye, T., Shao, J., Yi, Y and Zhao, Q. (2023). Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials. Journal of the American Statistical Association 118(544), 2370–2382.
  39. Ye, T., Shao, J. and Yi, Y. (2024). Covariate-adjusted log-rank test: guaranteed efficiency gain and universal applicability. Biometrika 111(2), 691–705.
  40. Ye, T. and Shao, J. (2020). Robust tests for treatment effect in survival analysis under covariateadaptive randomization. Journal of the Royal Statistical Society, Series B, Statistical Methodology 82(5), 1301–1323.
  41. Ye, T. (2018). Testing hypotheses under covariate-adaptive randomisation and additive models. Statistical Theory and Related Fields 2, 96–101.
  42. Zelen, M. (1974). The randomization and stratification of patients to clinical trials. Journal of Chronic Disease 27, 365–375.
  43. Zhao, Z., Song, Y., Jiang, W and Tu, D. (2022). Consistent covariances estimation for stratum imbalances under marginal design for covariate-adaptive randomization. Scandinavian Journal of Statistics 51(2), 861–890. First author affiliation: Merck & Co., Inc., Rahway, NJ, USA

Acknowledgments

The authors are grateful to the anonymous reviewers and the Associate

Editor for constructive recommendations that helped substantially improve

the paper.

M.G.’s work was supported in part by the Merck & Co., Inc., Rahway,

NJ, USA, BARDS grant “Demonstration that Type I error is preserved or

reduced with the score test and the log-rank test in studies with Pocock

and Simon covariate-adaptive randomization”.

Supplementary Materials

The Supplementary Materials document contains tables A1 – A14, the

derivation of (3.5), and the proof of Theorem 1.

Supplementary materials are available for download.