Improving Some Clinical Studies Inference by Using Ranked Auxiliary Covariate

The main objective in a randomized clinical trial or studies such as in cancer, AIDS, etc. is to compare the outcome of interest between two or more groups. Clinical trials are considered the "gold standard" of biomedical research and of its strengths are the ability to measure changes and/or evaluate of treatments over time with maximizing power of statistics and validity. Clinical trials are expensive, and the cost of clinical trials on developing new drugs, medical treatments and devices, public health investigators are increasing with each phase and continue to escalate, especially in phase III. The idea proposed in this project is to use auxiliary covariates by adopting Ranked Set Sampling (RSS) technique to select the subjects for each treatment-arms, to utilize inexpensive auxiliary covariates information into a randomized clinical trials. Our goal is to provide a more precise estimator of the population mean (µ) of the outcome of interest (Y) to recover the difficult to obtain information, without making any additional assumptions other than those already necessary for (RSS) and the ordinary least square estimators from a regression model to hold.