The use of minimization in clinical trials.
Since its introduction in 1974 the use of the term Minimization has been broadened to include other algorithms. All algorithms use patient characteristics to determine the assignment that produces the best overall balance between treatment groups. They differ in whether or not they use all of the data from each previously assigned subject to assign subsequent subjects so the methods are classified as complete or partial minimization. PubMed, Citation Index and Cochrane searches determined the frequency of articles using these types of minimization and a subset was selected for detailed review regarding the adequacy of the usage and reporting of minimization. In the past 10 years usage has increased three fold over the previous decade but is still less than 2% of clinical trials. None of the studies makes maximum use of minimization and they are not following good reporting practices. Concerns about the use of minimization have involved selection bias and statistical analysis. Several modifications to minimization are suggested to reduce the possibility of selection bias so that adding randomization will rarely be required. Separating primary and secondary analyses can avoid the statistical problems that minimization poses. The two types of analyses are distinguished by opposite limiting signs, providing reliable, simplified statistical results. This will improve data utilization and make clinical trials more reproducible. Minimization should be the method of choice in assigning subjects in all clinical trials.
Taves
Donald R
DR
Department of Dental Public Health Sciences, University of Washington, 1959 NE Pacific Street, Box 357475, Seattle, WA 98195, United States. dtaves@pickatime.com
D000465:Algorithms; D002986:Clinical Trials as Topic / Q000379:methods*; D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D011897:Random Allocation; D015203:Reproducibility of Results; D012107:Research Design; D015983:Selection Bias