Multi-center clinical trials: randomization and ancillary statistics. (English) Zbl 1400.62303

Summary: The purpose of this paper is to investigate and develop methods for analysis of multi-center randomized clinical trials which only rely on the randomization process as a basis of inference. Our motivation is prompted by the fact that most current statistical procedures used in the analysis of randomized multi-center studies are model based. The randomization feature of the trials is usually ignored. An important characteristic of model based analysis is that it is straightforward to model covariates. Nevertheless, in nearly all model based analyses, the effects due to different centers and, in general, the design of the clinical trials are ignored. An alternative to a model based analysis is to have analyses guided by the design of the trial. Our development of design based methods allows the incorporation of centers as well as other features of the trial design. The methods make use of conditioning on the ancillary statistics in the sample space generated by the randomization process. We have investigated the power of the methods and have found that, in the presence of center variation, there is a significant increase in power. The methods have been extended to group sequential trials with similar increases in power.


62P10 Applications of statistics to biology and medical sciences; meta analysis


Full Text: DOI arXiv


[1] Andersen, P. K., Klein, J. and Zhang, M.-J. (1999). Testing for centre effects in multi-centre survival studies: A Monte Carlo comparison of fixed and random effects tests., Statistics in Medicine 18 1489-1500.
[2] Boos, D. D. and Brownie, C. (1992). A rank-based mixed model approach to multisite clinical trials., Biometrics 48 61-72.
[3] Brunner, E., Domhof, S. and Puri, M. (2002). Weighted rank statistics in factorial designs with fixed effects., Statist. Neerlandica 56 179-194. · Zbl 1076.62541 · doi:10.1111/1467-9574.00192
[4] Davis, C. and Chung, Y. (1995). Randomization model methods for evaluating treatment efficacy in multicenter clinical trials., Biometrics 51 1163-1174. · Zbl 0875.62492 · doi:10.2307/2533016
[5] Fisher, R. A. (1971)., The Design of Experiments . Oliver and Boyd, Edinburgh, UK. · Zbl 0213.13104
[6] Garthwaite, P. (1996). Confidence intervals from randomization tests., Biometrics 52 1387-1393. · Zbl 0925.62122 · doi:10.2307/2532852
[7] Gray, R. J. (1994). A Bayesian analysis of institutional effects in a multicenter cancer clinical trial., Biometrics 50 244-253. JSTOR: · Zbl 0825.62781 · doi:10.2307/2532779
[8] Harville, D. A. (1997)., Matrix Algebra from a Statistician ’ s Perspective . Springer, New York. · Zbl 0881.15001
[9] Jennison, C. and Turnbull, B. W. (1997). Group-sequential analysis incorporating covariate information., J. Amer. Statist. Assoc. 92 1330-1341. JSTOR: · Zbl 0913.62074 · doi:10.2307/2965403
[10] Kalbfleisch, J. and Prentice, R. (2002)., The Statistical Analysis of Failure Time Data . Wiley, Hoboken, NJ. · Zbl 1012.62104
[11] Lachin, J. M. (1988). Properties of simple randomization in clinical trials., Controlled Clinical Trials 9 312-326.
[12] Lachin, J. M., Matts, J. P. and Wei, L. (1988). Randomization in clinical trials: Conclusions and recommendations., Controlled Clinical Trials 9 365-374.
[13] Lan, G. K. K. and DeMets, D. L. (1983). Discrete sequential boundaries for clinical trials., Biometrika 70 659-663. JSTOR: · Zbl 0543.62059 · doi:10.2307/2336502
[14] Localio, R. A., Berlin, J. A., Ten Have, T. R. and Kimmel, S. E. (2001). Adjustments for center in multicenter studies: An overview., Ann. Inter. Med. 135 112-123.
[15] Ludbrook, J. and Dudley, H. (1998). Why permutation tests are superior to, t and f tests in biomedical research. The American Statistician 52 127-132.
[16] Matsuyama, Y., Sakamoto, J. and Ohashi, Y. (1998). A Bayesian hierarchical survival model for the institutional effects in a multi-centre cancer clinical trial., Statistics in Medicine 17 1893-1908.
[17] Matts, J. P. and Lachin, J. M. (1988). Properties of permuted-block randomization in clinical trials., Controlled Clinical Trials 9 327-344.
[18] O’Brien, P. C. and Fleming, T. R. (1979). A multiple testing procedure for clinical trials., Biometrics 35 549-556.
[19] Pesarin, F. (2001)., Multivariate Permutation Tests. With Applications to Biostatistics . Wiley, Chichester. · Zbl 0972.62037
[20] Potthoff, R., Peterson, B. and George, S. (2001). Detecting treatment-by-centre interaction in multi-centre clinical trials., Statistics in Medicine 20 193-213.
[21] R Development Core Team (2007)., R : A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, Vienna, Austria.
[22] Rosenberger, W. F. and Lachin, J. M. (2002)., Randomization in Clinical Trials. Theory and Practice . Wiley, New York. · Zbl 1007.62091
[23] Rothwell, P., Eliasziw, M., Gutnikov, S., Warlow, C., Barnett, H. and Collaboration, C. E. T. (2004). Endarterectomy for symptomatic carotid stenosis in relation to clinical subgroups and timing of surgery., Lancet 363 915-924.
[24] Skene, A. M. and Wakefield, J. C. (1990). Hierarchical models for multicentre binary response studies., Statistics in Medicine 9 919-929.
[25] Wei, L. J. and Lachin, J. M. (1988). Properties of the urn randomization in clinical trials., Controlled Clinical Trials 9 345-364. · Zbl 0389.62067 · doi:10.2307/2286600
[26] Yamaguchi, T. and Ohashi, Y. (1999). Investing centre effects in a multi-centre clinical trial of superficial bladder cancer., Statistics in Medicine 18 1961-1971.
[27] Yamaguchi, T., Ohashi, Y. and Matsuyama, Y. (2002). Proportional hazards models with random effects to examine centre effects in multicentre cancer clinical trials., Statistics Methods in Medical Research 11 221-236. · Zbl 1121.62679 · doi:10.1191/0962280202sm284ra
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.