Optimal sampling for design-based estimators of regression models

TL;DR

This paper investigates optimal two-phase sampling designs for regression analysis using design-based estimators, particularly generalized raking, to improve efficiency under resource constraints.

Contribution

It derives a closed-form solution for the optimal design for generalized raking estimators and compares it with the IPW estimator's optimal design.

Findings

Optimal designs differ significantly between IPW and generalized raking estimators.

IPW-based optimal design is nearly optimal for generalized raking in most cases.

Potential for efficiency improvement exists with tailored designs for each estimator.

Abstract

Two-phase designs measure variables of interest on a subcohort where the outcome and covariates are readily available or cheap to collect on all individuals in the cohort. Given limited resource availability, it is of interest to find an optimal design that includes more informative individuals in the final sample. We explore the optimal designs and efficiencies for analysis by design-based estimators. Generalized raking is an efficient design-based estimator that improves on the inverse-probability weighted (IPW) estimator by adjusting weights based on the auxiliary information. We derive a closed-form solution of the optimal design for estimating regression coefficients from generalized raking estimators. We compare it with the optimal design for analysis via the IPW estimator and other two-phase designs in measurement-error settings. We consider general two-phase designs where the outcome variable and variables of interest can be continuous or discrete. Our results show that the optimal designs for analysis by the two design-based estimators can be very different. The optimal design for IPW estimation is optimal for analysis via the IPW estimator and typically gives near-optimal efficiency for generalized raking, though we show there is potential improvement in some settings.