DOI QR코드

DOI QR Code

Confidence Bounds following Adaptive Group Sequential Tests with Repeated Measures in Clinical Trials

반복측정자료를 가지는 적응적 집단축차검정에서의 신뢰구간 추정

  • 조숙정 (아이콘클리니컬 리서치 임상통계팀) ;
  • 이재원 (고려대학교 통계학과)
  • Received : 2013.02.19
  • Accepted : 2013.08.05
  • Published : 2013.08.31

Abstract

A group sequential design can end a clinical trial early if a confirmed efficacy or a futility of study medication is found during clinical trials. Adaptation can adjust the design of clinical trials based on accumulated data. The key to this methodology is considered to control the overall type 1 error rate while maintaining the integrity of clinical trials. The estimation would be more complex and the sample size calculation will be more difficult if the clinical trials have repeated measurement data. Lee et al. (2002) suggested a repeated observation case by using the independent increments properties of the interim test statistics and investigated the properties of the proposed confidence interval based on the stage-wise ordering. This study extend Lee et al. (2002) to adaptive group sequential design. We suggest test statistics for the adaptation as redesigning the second stage of clinical trials and induce the stage-wise confidence interval of parameter of interests. The simulation will help to confirm the suggested method.

집단축차설계법은 중간분석을 실시하여 임상시험용 의약품의 유효성 또는 무용성이 조기에 발견되면 임상시험을 조기에 종료할 수 있는 시험설계법이다. 적응적 설계법은 중간분석 결과를 이용하여 시험설계를 변경하거나, 확률적으로 독립인 두개의 임상시험 결과를 결합하는 등 다양한 적응법으로 임상시험의 설계를 수정할 수 있는 시험설계법이다. 집단축차설계법과 적응적 설계법에서 주요하게 고려할 점은, 시험 전체적으로 제1종 오류를 적절히 분배하고 통제하여 임상시험 전체의 일관성을 유지하도록 하는 것이다. 반복측정자료 또는 경시적자료의 통계적 모형이 고려되는 경우에는 통계적 추론이 더욱 복잡하고 어려워진다. Lee 등 (2002)에서는 반복측정치를 가지는 임상시험에서 집단축차설계에서 미리 정한 기준에 의하여 임상시험이 종료된 후, 독립증분과 단계적 순서관계를 고려한 신뢰구간 추정법을 제안한 바 있다. 본 연구는 Lee 등 (2002)를 적응적 설계로 확장하였다. 적응법을 실시한 전과 후의 임상시험을 확률적으로 독립인 관계로 정의하는 검정통계량을 유도하여 적응적 집단축차검정법이 가능하게 하였다. 또한, 임상시험이 종료된 후 단계적 순서관계를 고려한 신뢰구간 추정법을 제안하였으며, 모의실험을 통하여 그 안정성을 확인하였다.

Keywords

References

  1. Bauer, P. and Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses, Biometrics, 50, 1029-1041. Correcion Biometrics, 52, (1996), 380.
  2. Brannath, W., Mehta, C. R. and Posch, M. (2009). Exact confidence bounds following adaptive group sequential tests, Biometrics, 65, 539-546. https://doi.org/10.1111/j.1541-0420.2008.01101.x
  3. Cheng, Y. and Shen, Y. (2004). Estimation of a parameter and its exact confidence interval following sequential sample size reestimation trials, Biometrics, 60, 910-918. https://doi.org/10.1111/j.0006-341X.2004.00246.x
  4. Cui, L., Hung, H. M. J. and Wang, S. (1999). Modification of sample size in group sequential clinical trials, Biometrics, 55, 853-857. https://doi.org/10.1111/j.0006-341X.1999.00853.x
  5. Denne, J. S. (2001). Sample size recalculation using conditional power, Statistics in Medicine, 20, 2645-2660. https://doi.org/10.1002/sim.734
  6. Fisher, L. D. (1998). Self-designing clinical trials, Statistics in Medicine, 17, 1551-1562. https://doi.org/10.1002/(SICI)1097-0258(19980730)17:14<1551::AID-SIM868>3.0.CO;2-E
  7. Hwang, I. K., Shih, W. J. and DeCani, J. S. (1990). Group sequential design using a family of type I error probability spending functions, Statistics in Medicine, 9, 1439-1445. https://doi.org/10.1002/sim.4780091207
  8. Jennison, C. and Turnbull, B. W. (2000). Group Sequential Methods with Applications to Clinical Trials, Boca Raton, Chapman and Hall/CRC, Florida.
  9. Kim, K. and DeMets, D. L. (1987). Confidence intervals following group sequential tests in clinical trials, Biometrics, 43, 857-864. https://doi.org/10.2307/2531539
  10. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data, Biometrics, 38, 963-974. https://doi.org/10.2307/2529876
  11. Lee, J. W. and DeMets, D. L. (1991). Sequential comparison of changes with repeated measurements data, Journal of the American Statistical Association, 86, 757-762. https://doi.org/10.1080/01621459.1991.10475106
  12. Lee, J. W., Jo, S. J., DeMets, D. L. and Kim, K. (2002). Confidence intervals following group sequential tests in clinical trials with multivariate observations, Journal of Statistical Computation and Simulations, 72, 247-259. https://doi.org/10.1080/00949650212386
  13. Lehmacher, W. and Wassmer, G. (1999). Adaptive sample size calculations in group sequential trials, Biometrics, 55, 1286-1290. https://doi.org/10.1111/j.0006-341X.1999.01286.x
  14. Lindstorm, M. J. and Bates, D. M. (1988). Newton-Raphson and EM algorithms for linear mixed effects models for repeated measures data, Journal of the American Statistical Association, 83, 1014-1022.
  15. Mehta, R. C., Bauer, P., Posch, M. and Brannath, W. (2007). Repeated confidence intervals for adaptive group sequential trials, Statistics in Medicine, 26, 5422-5433 https://doi.org/10.1002/sim.3062
  16. Muller, H. H. and Schafer, H. (2001). Adaptive group sequential designs for clinical trials: Combining the advantages of adaptive and of classical group sequential approaches, Biometrics, 57, 886-891. https://doi.org/10.1111/j.0006-341X.2001.00886.x
  17. Muller, H. H. and Schafer, H. (2004). A general statistical principle for changing a design any time during the course of a trials, Statistics in Medicine, 23, 2497-2508. https://doi.org/10.1002/sim.1852
  18. Posch, M., Bauer, P. and Brannath, W. (2003). Issues in designing flexible trials, Statistics in Medicine, 23, 953-969.
  19. Proschan, M. A. and Hunsberger, S. A. (1995). Designed extension of studies based on conditional power, Biometrics, 51, 1315-1324. https://doi.org/10.2307/2533262
  20. Schervish, M. J. (1984). Multivariate normal probabilities with error bound(with corrections in 1985), Applied Statistics, 33, 81-94. https://doi.org/10.2307/2347670
  21. Shen, Y. and Fisher, L. D. (1999). Statistical inference for self-designing clinical trials with a one sided hypothesis, Biometrics, 55, 190-197. https://doi.org/10.1111/j.0006-341X.1999.00190.x
  22. Tsiatis, A. A., Rosner, G. L. and Mehta, C. R. (1984). Exact confidence intervals following a group sequential test, Biometrics, 40, 797-803. https://doi.org/10.2307/2530924