Open Access
2023 Improving estimation efficiency for two-phase, outcome-dependent sampling studies
Menglu Che, Peisong Han, Jerald F. Lawless
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Electron. J. Statist. 17(1): 1043-1073 (2023). DOI: 10.1214/23-EJS2124

Abstract

Two-phase outcome dependent sampling (ODS) is widely used in many fields, especially when certain covariates are expensive and/or difficult to measure. For two-phase ODS, the conditional maximum likelihood (CML) method is very attractive because it can handle zero Phase 2 selection probabilities and avoids modeling the covariate distribution. However, most existing CML-based methods use only the Phase 2 sample and thus may be less efficient than other methods. We propose a general empirical likelihood method that uses CML augmented with additional information in the whole Phase 1 sample to improve estimation efficiency. The proposed method maintains the ability to handle zero selection probabilities and avoids modeling the covariate distribution, but can lead to substantial efficiency gains over CML in the inexpensive covariates, or in the influential covariate when a surrogate is available, because of an effective use of the Phase 1 data. Simulations and a real data illustration using NHANES data are presented.

Citation

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Menglu Che. Peisong Han. Jerald F. Lawless. "Improving estimation efficiency for two-phase, outcome-dependent sampling studies." Electron. J. Statist. 17 (1) 1043 - 1073, 2023. https://doi.org/10.1214/23-EJS2124

Information

Received: 1 November 2022; Published: 2023
First available in Project Euclid: 5 April 2023

MathSciNet: MR4571186
zbMATH: 07690319
Digital Object Identifier: 10.1214/23-EJS2124

Keywords: conditional likelihood , empirical likelihood , expensive covariate , missing at random , surrogate covariate , two-phase study

Vol.17 • No. 1 • 2023
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