Webinar Minimization a Flexible Randomized Method
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Randomization is the key element of comparative clinical trials and has thankfully evolved substantially from the early days of randomization lists and sealed envelopes at the sites for emergency code breaks. A commonly used method is stratified permuted block randomization, but covariate-adaptive randomization (also known as minimization) can be better in many situations. In this webinar, the minimization method will first be explained, including demonstration of some of the advantages of minimization over permuted blocks, discussion of the implementation of the method and closing with some challenges related to minimization.
Minimization was originally suggested by Taves (1974) and by Pocock and Simon (1975) so the method has been used for several decades. Its popularity has increased as web-based Randomization and Trial Supply Management (RTSM) systems have been developed and have become more sophisticated. Minimization is often used in multi-center studies when two objectives are simultaneously sought: to maintain a good balance between all treatment groups with respect to important prognostic factors and to maintain the unpredictability of the next treatment assignment. Minimization attempts to achieve optimum treatment balance between several factors simultaneously rather than within the separate strata defined cross-classification of these factors. The major advantage of minimization, as compared to a stratified treatment allocation, is that good treatment balance can be achieved across a large number of “minimization” factors whereas for stratified permuted block, the number of stratification factors is limited.In this webinar, simulations that help identify which methods give the best results in terms of the size and power of the test, as well as the precision of the estimation, will be presented.
There is discussion among the statistical community regarding which methods of randomization and analyses are preferred. This webinar will discuss some challenges that might come from using minimization. For example, some regulatory agencies may not be comfortable and/or they will require the use of re-randomization tests. In this presentation, it will be demonstrated how such tests are easy to implement and usually do not differ from tests based on asymptotic theory.