Comparisons of minimization and Atkinson's algorithm.
Some general points regarding efficiency in clinical trials are made. Reasons as to why fitting many covariates to adjust the estimate of the treatment effect may be less problematic than commonly supposed are given. Two methods of dynamic allocation of patients based on covariates, minimization and Atkinson's approach, are compared and contrasted for the particular case where all covariates are binary. The results of Monte Carlo simulations are also presented. It is concluded that in the cases considered, Atkinson's approach is slightly more efficient than minimization although the difference is unlikely to be very important in practice. Both are more efficient than simple randomization, although it is concluded that fitting covariates may make a more valuable and instructive contribution to inferences about treatment effects than only balancing them.
Senn
Stephen
S
Department of Statistics, University of Glasgow, Glasgow G12 8QW, U.K. stephen@stats.gla.ac.uk
Anisimov
Vladimir V
VV
Fedorov
Valerii V
VV
D000305:Adrenal Cortex Hormones / Q000009:adverse effects / Q000627:therapeutic use; D000465:Algorithms; D001249:Asthma / Q000188:drug therapy; D056808:Biostatistics / Q000379:methods*; D003198:Computer Simulation / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D009010:Monte Carlo Method; D018579:Patient Selection; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*
D000305:Adrenal Cortex Hormones