Using a rank-based design in estimating prevalence of breast cancer

TL;DR

This paper introduces a cost-efficient multistage ranked set sampling method for estimating breast cancer prevalence, significantly reducing the number of observations needed compared to simple random sampling, especially in costly diagnostic scenarios.

Contribution

It develops a novel estimator using MSRSS that leverages cytological covariates for more efficient prevalence estimation in breast cancer diagnosis.

Findings

MSRSS estimator is more efficient than SRS

Potential to reduce sample size by 76%

Theoretical properties of the estimator are established

Abstract

It is highly important for governments and health organizations to monitor the prevalence of breast cancer as a leading source of cancer-related death among women. However, the accurate diagnosis of this disease is expensive, especially in developing countries. This article concerns a cost-efficient method for estimating prevalence of breast cancer, when diagnosis is based on a comprehensive biopsy procedure. Multistage ranked set sampling (MSRSS) is utilized to develop a proportion estimator. This design employs some visually assessed cytological covariates, which are pertinent to determination of breast cancer, so as to provide the experimenter with a more informative sample. Theoretical properties of the proposed estimator are explored. Evidence from numerical studies is reported. The developed procedure can be substantially more efficient than its competitor in simple random sampling (SRS). In some situations, the proportion estimation in MSRSS needs around 76% fewer observations than that in SRS, given a precision level. Thus, using MSRSS may lead to a considerable reduction in cost with respect to SRS. In many medical studies, e.g. diagnosing breast cancer based on a full biopsy procedure, exact quantification is difficult (costly and/or time-consuming), but the potential sample units can be ranked fairly accurately without actual measurements. In this setup, multistage ranked set sampling is an appropriate design for developing cost-efficient statistical methods.