|Title:||Urn models for response-adaptive randomized designs: a simulation study based on a non-adaptive randomized trial|
|Date:||Thursday 2nd March 2017|
|Author(s) :||Ghiglietti, A.; Scarale, M.g.; Miceli, R.; Ieva, F.; Mariani, L.; Gavazzi, C.; Paganoni, A.m.; Edefonti, V.|
|Abstract:|| Recently, response-adaptive designs have been proposed in randomized clinical trials to achieve ethical and/or cost advantages by using sequential accrual information collected during the trial to dynamically update the probabilities of treatment assignments. In this context, urn models - where the probability to assign patients to treatments is interpreted as the proportion of balls of different colors available in a virtual urn - have
been used as response-adaptive randomization rules. We propose the use of Randomly Reinforced Urn (RRU) models in a simulation study based on a published randomized clinical trial on the efficacy of home enteral nutrition in cancer patients after major gastrointestinal surgery.
We compare results with the RRU design with those previously published with the non-adaptive approach.
We also provide a code written with the R software to implement the RRU design in practice. In detail, we simulate 10,000 trials based on the RRU model in three setups of different total sample sizes. We report information on the number of patients allocated to the inferior treatment and on the empirical power of the t-test for the treatment coefficient in the ANOVA model. We carry out a sensitivity analysis to assess the effect of different urn compositions. For each sample size, in approximately 75% of the simulation runs, the number of patients allocated to the inferior treatment by the RRU design is lower, as compared to the non-adaptive design. The empirical power of the t-test for the treatment effect is similar in the two designs. Accettato per la pubblicazione su Journal of Biopharmaceutical Statistics|
This report, or a modified version of it, has been also submitted to, or published on
Journal of Biopharmaceutical Statistics (https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1452024), doi: 10.1080/10543406.2018.1452024