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Optimal-design domain-adaptation for exposure prediction in two-stage epidemiological studies

Journal of Exposure Science & Environmental Epidemiology volume 33, pages 963–970 (2023)Cite this article

Abstract

Background

In the first stage of a two-stage study, the researcher uses a statistical model to impute the unobserved exposures. In the second stage, imputed exposures serve as covariates in epidemiological models. Imputation error in the first stage operate as measurement errors in the second stage, and thus bias exposure effect estimates.

Objective

This study aims to improve the estimation of exposure effects by sharing information between the first and second stages.

Methods

At the heart of our estimator is the observation that not all second-stage observations are equally important to impute. We thus borrow ideas from the optimal-experimental-design theory, to identify individuals of higher importance. We then improve the imputation of these individuals using ideas from the machine-learning literature of domain adaptation.

Results

Our simulations confirm that the exposure effect estimates are more accurate than the current best practice. An empirical demonstration yields smaller estimates of PM effect on hyperglycemia risk, with tighter confidence bands.

Significance

Sharing information between environmental scientist and epidemiologist improves health effect estimates. Our estimator is a principled approach for harnessing this information exchange, and may be applied to any two stage study.

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Fig. 1: Simulation results.
Fig. 2: \(\hat \beta _{\hat x} - \beta _x\) for the Naïve (green) and ODIWI with L = 10 iterations (red) estimators.
Fig. 3: \(\hat \beta _{\hat x}\) estimates over the number of iterations.
Fig. 4: Densities of \(\hat \beta _{\hat x} - \beta _x\) over 200 simulation repetitions of ODIWI (red lines) and Naïve (green lines) estimators.
Fig. 5

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Data availability

The data are not available for replication because of privacy issues.

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Acknowledgements

The authors wish to thank Dr. Raanan Raz, and Dr. Lena Novack, for their comments and ideas.

Funding

The results reported herein correspond to specific aims of grant no. 900/16 to JDR from the Israel Science Foundation.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Ben Gurion University of the Negev, Be’er Sheva, Israel

    Ron Sarafian & Jonathan D. Rosenblatt

  2. Department of Geography and Environmental Development, Ben Gurion University of the Negev, Be’er Sheva, Israel

    Itai Kloog

Authors
  1. Ron Sarafian
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  2. Itai Kloog
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  3. Jonathan D. Rosenblatt
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Contributions

RS conceived of the presented idea. RS developed the theory with support from JDR, and performed the computations. JDR and IK verified the analytical methods, and supervised the findings of this work. RS wrote the manuscript with support from JDR. and IK. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Ron Sarafian.

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The authors declare no competing interests.

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Sarafian, R., Kloog, I. & Rosenblatt, J.D. Optimal-design domain-adaptation for exposure prediction in two-stage epidemiological studies. J Expo Sci Environ Epidemiol 33, 963–970 (2023). https://doi.org/10.1038/s41370-022-00438-5

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