Haphazard Intentional Sampling in Survey and Allocation Studies on COVID-19 Prevalence and Vaccine Efficacy

Abstract

Haphazard intentional sampling is a method developed by our research group for two main purposes: (i) sampling design, where the interest is to select small samples that accurately represent the general population regarding a set of covariates of interest; or (ii) experimental design, where the interest is to assemble treatment groups that are similar to each other regarding a set of covariates of interest. Rerandomization is a similar method proposed by K. Morgan and D. Rubin. Both methods intentionally select good samples but, in slightly different ways, also introduce some noise in the selection procedure aiming to obtain a decoupling effect that avoids systematic bias or other confounding effects. This paper compares the performance of the aforementioned methods and the standard randomization method in two benchmark problems concerning SARS-CoV-2 prevalence and vaccine efficacy. Numerical simulation studies show that haphazard intentional sampling can either reduce operating costs in up to 80% to achieve the same estimation errors yielded by the standard randomization method or, the other way around, reduce estimation errors in up to 80% using the same sample sizes.