UK-Cubic Complete Randomized Design of Experiment: A Complete Three Way Layout for Large Multi-Center Clinical Trials
Journal: International Journal of Science and Research (IJSR) (Vol.6, No. 12)Publication Date: 2017-12-05
Authors : N. Uthayakumaran;
Page : 104-110
Keywords : Design of experiment with a complete three way layout; Include larger number of participants; Different geographic locations; Wider range of population groups; Compare results among centers; Simulated linear model; Increase the generalizablity of the larg;
Abstract
In clinical trial research, if health researcher wants to know whether particular drug which has been invented is beneficial or not in the treatment of particular disease, there are many experimental designs to approach, in which most important one is Latin Square Design (LSD). The LSD has its own advantages and disadvantages. LSD is assumed to be a three factor experiment but actually it has only two- way stratification. Analysis is done with the missing blocks since it is an incomplete three way layout. In LSD, the experimental material is divided into rows and columns, each having the same number of experimental units which is equal to the number of treatments. A new design of experiment described in this paper is an attempt to extend LSD and produce a complete three way layout so that analysis is made without missing blocks and number of experimental units in blocks is equal. The number of experimental units is not equal to the number of treatments, in turn, it is possible to handle large sample size. It also controls more variation and results in a smaller mean square error. In this design, it is possible to include larger number of participants, different geographic locations, wider range of population groups and the ability to compare results among centers, all of which increase the generalizablity of the multi-center clinical trial study. This design will be useful for large multi-center clinical trials. To reveal the efficiency of this design and its good randomization, it reflects the results as per the data simulated through linear model. In many cases, efficacy will vary significantly between population groups with different backgrounds, like, genetic, environmental, ethnic, etc. This design can properly evaluate efficacy by adopting these groups in dispersed trial approach.
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