Treatment Allocation Methods Using Ordered Statistics
Location
Room 2908
Session Format
Paper Presentation
Research Area Topic:
Public Health & Well Being - Pharmaceutical/Clinical Trials Research
Abstract
Random controlled experiment is the gold standard of the study designs which focus at treatment comparisons in health related field[7]. The validity of statistical inference only underwrite from proper randomization process. In a random controlled experiment, subjects are randomly assigned to one of the treatment groups. Several approach has been studies to restrict treatment allocation process to ensure the validity of statistical inference in those studies, such as complete randomization, semi-randomization and non-randomization method. We proposed the method of treatment allocation based on ordered statistic. It is inspired by ranked set sampling. Ranked set sampling (RSS) was first introduced by McIntyre [12]. The principle of RSS is to select sample systematically from a population. It was originally introduced as an efficient alternative to simple random sampling for estimating the field of pastures. We introduce a method based on ranked auxiliary variables for treatment allocation in crossover designs using Latin square models. Simulation study shows improved efficiency using ranked auxiliary variable in crossover design.
Presentation Type and Release Option
Presentation (Open Access)
Start Date
4-16-2016 1:30 PM
End Date
4-16-2016 2:30 PM
Recommended Citation
Esong, Huang, "Treatment Allocation Methods Using Ordered Statistics" (2016). GS4 Georgia Southern Student Scholars Symposium. 166.
https://digitalcommons.georgiasouthern.edu/research_symposium/2016/2016/166
Treatment Allocation Methods Using Ordered Statistics
Room 2908
Random controlled experiment is the gold standard of the study designs which focus at treatment comparisons in health related field[7]. The validity of statistical inference only underwrite from proper randomization process. In a random controlled experiment, subjects are randomly assigned to one of the treatment groups. Several approach has been studies to restrict treatment allocation process to ensure the validity of statistical inference in those studies, such as complete randomization, semi-randomization and non-randomization method. We proposed the method of treatment allocation based on ordered statistic. It is inspired by ranked set sampling. Ranked set sampling (RSS) was first introduced by McIntyre [12]. The principle of RSS is to select sample systematically from a population. It was originally introduced as an efficient alternative to simple random sampling for estimating the field of pastures. We introduce a method based on ranked auxiliary variables for treatment allocation in crossover designs using Latin square models. Simulation study shows improved efficiency using ranked auxiliary variable in crossover design.
