Covariate-based constrained randomization of group-randomized trials

Clinical Trials

Volumn 1, Issue 3, 2004, Pages 297-305

  Moulton, Lawrence h ^a  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; DECISION MAKING; GROUP PROCESS; HUMAN; METHODOLOGY; RANDOMIZATION; RANDOMIZED CONTROLLED TRIAL; REPRODUCIBILITY; STATISTICS;

ALGORITHMS; DECISION MAKING; GROUP PROCESSES; HUMANS; RANDOM ALLOCATION; RANDOMIZED CONTROLLED TRIALS; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN;

EID: 33644805193     PISSN: 17407745     EISSN: None     Source Type: Journal    
DOI: 10.1191/1740774504cn024oa     Document Type: Article

References (26)

1. Cook T.D., Campbell D.T. Quasi-experimentation: design and analysis issues for field settings. Chicago, IL: Rand-McNally, 1979, 354-356.
2. Donner A., Klar N. Statistical considerations in the design and analysis of community intervention trials. J Clin Epidemiol 1996; 49: 435-439.
3. Fortmann S.P., Flora J.A., Winkleby M.A., Schooler C., Taylor C.B., Farquhar J.W. Community intervention trials: reflections on the Stanford Five-City Project Experience. Am J Epidemiol 1995; 142: 576-586.
4. Gail M.H., Mark S.D., Carroll R.J., Green S.B., Pee D. On design considerations and randomization-based inference for community intervention trials. Stat Med 1996; 15: 1069-1092.
5. Diehr P., Martin D.C., Koepsell T., Cheadle A. Breaking the matches in a paired t-test for community interventions when the number of pairs is small. Stat Med 1995; 14: 1491-1504.
6. Donner A. Sample size requirements for stratified cluster randomization designs. Stat Med 1992; 11: 743-750.
7. Freedman L.S., Green S.B., Byar D.P. Assessing the gain in efficiency due to matching in a community intervention study. Stat Med 1990; 9: 943-952.
8. Martin D.C., Diehr P., Perrin E.B., Koepsell T.D. The effect of matching on the power of randomized community intervention studies. Stat Med 1993; 12: 329-338.
9. Pocock S.J., Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 1975; 31: 103-115.
10. Signorini D.F., Leung O., Simes R.J., Beller E., Gebski V.J., Callaghan T. Dynamic balanced randomization for clinical trials. Stat Med 1993; 12: 2343-2350.
11. Berry D.A., Eick S.G. Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. Stat Med 1995; 14: 231-246.
12. Rosenbaum P.R. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41-55.
13. Graham J.W., Flay B.R., Johnson C.A., Hansen W.B., Collins L.M. Group comparability: A multiattribute utility measurement approach to the use of random assignment with small numbers of aggregated units. Evaluation Review 1984; 8: 247-260.
14. Morris C., Hill J. The Health Insurance Experiment: design using the finite selection model. In Morton S.C., Rolph J.E. eds. Public policy and statistics: case studies from RAND. New York: Springer-Verlag, 2000, 29-53.
15. Royall R.M. Current advances in sampling theory: implications for human observational studies. Am J Epidemiol 1976; 104: 463-474.
16. Murray D.M. Design and analysis of group-randomized trials. New York: Oxford University Press, 1998.
17. Donner A., Klar N.S. Design and analysis of cluster randomisation trials in health research. London: Arnold, 2000.
18. Kalish L.A., Begg C.B. The impact of treatment allocation procedures on nominal significance levels and bias. Control Clin Trials 1987; 8: 121-135.
19. Chen H. Effect of ignoring randomization constraints in the analysis of clinical trials. PhD dissertation. Baltimore: The Johns Hopkins University, 1997.
20. Bailey R. Restricted randomization. Biometrika 1983; 70: 183-198.
21. Fisher R.A. The design of experiments, fourth edition (first 1935). New York: Hafner-Publishing Co. Inc., 1947.
22. Bailey R.A. Restricted randomization: a practical example. J Am Statist Assoc 1987; 82: 712-719.
23. Gentleman J.F. Algorithm AS 88: generation of all NCR combinations by simulating nested DO loops. Appl Statist 1975; 24: 374-376.
24. Aptech Systems Inc. The GAUSS system. Maple Valley, WA: Aptech Systems Inc., 1999.
25. Tukey J. Tightening the clinical trial. Control Clin Trials 1993; 14: 266-285.
26. Moulton L.H., O'Brien K.L., Kohberger R. et al. Design of a group-randomized Streptococcus pneumoniae vaccine trial. Control Clin Trials 2001; 22: 438-452.