Permutation test following covariate-adaptive randomization in randomized controlled trials

Journal of Biopharmaceutical Statistics

Volumn 19, Issue 1, 2009, Pages 106-119

  Hasegawa, Takahiro ^a   Tango, Toshiro ^b  

^a SHIONOGI AND CO LTD   (Japan)

^b NATIONAL INSTITUTE OF PUBLIC HEALTH   (Japan)

Author keywords

Covariate adaptive randomization; Invoked population model; Linear models; Monte Carlo method; Permutation tests; Randomization model

Indexed keywords

PIRFENIDONE; PLACEBO;

ANALYSIS OF COVARIANCE; ARTICLE; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DOUBLE BLIND PROCEDURE; DRUG EFFICACY; FIBROSING ALVEOLITIS; HUMAN; MAJOR CLINICAL STUDY; MATHEMATICAL MODEL; MEDICAL RESEARCH; MONTE CARLO METHOD; PRIORITY JOURNAL; PROGNOSIS; RANDOMIZATION; RANDOMIZED CONTROLLED TRIAL; STATISTICAL MODEL;

ALGORITHMS; ANALYSIS OF VARIANCE; ANTI-INFLAMMATORY AGENTS, NON-STEROIDAL; COMPUTER SIMULATION; FEMALE; HUMANS; IDIOPATHIC PULMONARY FIBROSIS; MALE; MODELS, STATISTICAL; MONTE CARLO METHOD; PYRIDONES; RANDOMIZED CONTROLLED TRIALS AS TOPIC; TREATMENT OUTCOME; VITAL CAPACITY;

EID: 60749094498     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543400802527908     Document Type: Article

References (22)

1. Aickin, M. (2001). Randomization, balance, and the validity and efficiency of designadaptive allocation methods. Journal of Statistical Planning and Inference 94:97-119.
2. Azuma, A., Nukiwa, T., Tsuboi, E. et al. (2005). Double-blind, placebo-controlled trial of pirfenidone in patients with idiopathic pulmonary fibrosis. American Journal of Respiratory and Critical Care Medicine 171:1040-1047.
3. Berger, V. W. (2000). Pros and cons of permutation tests in clinical trials. Statistics in Medicine 19:1319-1328.
4. Edgington, E. S. (1987). Randomization Tests. New York: Marcel Dekker.
5. Endo, A., Nagatani, F., Hamada, C., Yoshimura, I. (2006). Minimization method for balancing continuous prognostic variables between treatment and control groups using Kullback-Leibler divergence. Contemporary Clinical Trials 27:420-431.
6. Ernst, M. D. (2004). Permutation methods: a basis for exact inference. Statistical Science 19(4):676-685.
7. Garthwaite, P. H. (1996). Confidence intervals from randomization tests. Biometrics 52:1387-1393.
8. Hagino, A., Hamada, C., Yoshimura, I., Ohashi, Y., Sakamoto, J., Nakazato, H. (2004). Statistical comparison of random allocation methods in cancer clinical trials. Controlled Clinical Trials 25:572-584.
9. Hasegawa, T., Tango, T., Nishikawa, M. (2006). Randomization-based inference in randomized controlled trials. Technical Report No. TAB-06-001. Department of Technical Assessment and Biostatistics. National Institute of Public Health.
10. Huitema, B. E. (1980). The Analysis of Covariance and Alternatives. New York: John Wiley & Sons.
11. Ihaka, R., Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics 5:299-314. Available from: http://www.R-project.org.
12. Kalish, L. A., Begg, C. B. (1987). The impact of treatment allocation procedures on nominal significance levels and bias. Controlled Clinical Trials 8:121-135.
13. Lachin, J. M. (1988). Statistical properties of randomization in clinical trials. Controlled Clinical Trials 9:289-311.
14. Manly, B. F. J. (1991). Randomization and Monte Carlo Methods in Biology. London: Chapman and Hall.
15. McEntegart, D. J. (2004). Letter to the editor. Statistics in Medicine 23:3719-3720.
16. Nishi, T., Takaichi, A. (2003). An extended minimization method to assure similar means of continuous prognostic variables between treatment groups. Japanese Journal of Biometrics 24:43-55.
17. Ohashi Y. (1990). Randomization in cancer clinical trials: permutation test and development of a computer program. Environmental Health Perspectives 87:13-17.
18. Pocock, S. J., Simon, R. (1975). Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 31:103-115.
19. Rosenberger, W. F., Lachin, J. M. (2002). Randomization in Clinical Trials, Theory and Practice. New York: John Wiley & Sons.
20. Sullivan, L. M., D'agostino, R. B. (1996). Robustness and power of analysis of covariance applied to data distorted from normality by floor effects: homogeneous regression slopes. Statistics in Medicine 15:477-496.
21. Taves, D. R. (1974). Minimization: a new method of assigning patients to treatment and control groups. Clinical Pharmacology and Therapeutics 15:443-453.
22. The European Agency for the Evaluation of Medicinal Products [homepage on the Internet]. London. Points to consider on adjustment for baseline covariates. CPMP/EWP/2863/99 [updated 2003 May 22; cited 2007 Nov. 12]. Available from: http://www.emea.europa.eu/pdfs/human/ewp/286399en.pdf.