Cited by 18Statistical Methods and Bayesian InferenceAdvanced Causal Inference TechniquesStatistical Methods in Epidemiology

Improving main analysis by borrowing information from auxiliary data

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Chen, Chixiang, Han, Peisong, He, Fan·2022·Statistics in Medicine

Abstract

In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.

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Research works printed but not linked: 2

  1. Newey WK, McFadden D. Large sample estimation and hypothesis testing. Engle RF, McFadden DL, eds. Handbook of Econometrics. Vol 4; Elsevier; Amsterdam, Netherlands: 1994:2111-2245.
  2. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Stat Sin. 2004;14(3):809-834.

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Authors
Chen, C., Han, P., & He, F.
Affiliations
  • University of Maryland, Baltimore (US)
  • University of Michigan (US)
  • Pennsylvania State University (US)
Corresponding author
Chen, Chixiang
Venue
Statistics in Medicine
Publisher
Wiley
Published
2021-11-18
Volume
41
Issue
3
Pages
567-579
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
OpenAlex
W3212764778
Cited by
18
References
36
Citations by year
2026: 3 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 1
FWCI
2.04
Type
journal article
Language
en
Open access
Locations
  • Statistics in Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Deep Blue (University of Michigan) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
auxiliary information, weighting, working models, estimation precision, information index, repeated measures, surrogate variables, Atherosclerosis Risk in Communities Study
MeSH terms
Computer Simulation, Humans, Research Design

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