Optimum Allocation for Adaptive Multi-Wave Sampling in R: The R Package optimall

TL;DR

The paper introduces the R package optimall, which streamlines the design and implementation of adaptive multi-wave sampling surveys, especially in epidemiological studies, by providing tools for stratification, optimal allocation, and sample selection.

Contribution

The paper presents a new R package that simplifies adaptive multi-wave sampling design, incorporating interactive stratification and optimal allocation methods for complex survey sampling.

Findings

Demonstrates efficient survey design workflows using real epidemiological data

Shows the package's flexibility for multi-phase and multi-wave sampling

Facilitates adaptive sampling adjustments in R

Abstract

The R package optimall offers a collection of functions that efficiently streamline the design process of sampling in surveys ranging from simple to complex. The package's main functions allow users to interactively define and adjust strata cut points based on values or quantiles of auxiliary covariates, adaptively calculate the optimum number of samples to allocate to each stratum using Neyman or Wright allocation, and select specific IDs to sample based on a stratified sampling design. Using real-life epidemiological study examples, we demonstrate how optimall facilitates an efficient workflow for the design and implementation of surveys in R. Although tailored towards multi-wave sampling under two- or three-phase designs, the R package optimall may be useful for any sampling survey.