Date of Award
Open Access Thesis
The application of a method of randomization for a clinical trial frequently summarizes to using Simple Randomization. Even though the latter method provides favorable characteristics, if the collected sample is not large enough, it still presents the highest chance of imbalance both marginally in the treatment groups and locally in terms of the covariates. Methods of Permuted Block Randomization, Urn Randomization, Stratified Permuted Block Randomization, and Minimization represent popular alternative methods that one should consider depending on the goal of the study. A comparison of the previously mentioned methods is carried to evaluate their performance with samples that are not considered large. Additional goals of our study are to also assess the performance of our newly implemented methods of Minimizations based on the performances of the established methods.
The found results show that the existing Minimization had the lowest imbalance amongst the previously established methods. Our newly implemented methods of Kolmogorov-Smirnov Minimization and Minimization with increasing Factor showed to be superior to the already established methods when the objective is to randomize on subjects’ variables. The found results also served the purpose of additional reference to build a free software that any user may employ to appropriately randomize subjects.
Louis, S.(2019). Randomization Analysis Driven Software. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/5116