ArticleBiometrika2023
Efficient Estimation under Data Fusion.
Article in Biometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Combining Observational Studies to Reduce Multiple Biases.Epidemiology (Cambridge, Mass.) · 2026Article
- Towards a Unified Theory for Semiparametric Data Fusion with Individual-Level Data.Annals of statistics · 2026Article
- Efficient collaborative learning of the average treatment effect.Biometrics · 2026Article
- Comparing HIV Vaccine Immunogenicity Across Trials With Different Populations and Study Designs.Statistics in medicine · 2026Article
- Confidence Interval Construction for Causally Generalized Estimates With Target Sample Summary Information.Statistics in medicine · 2026Article
- A surrogate endpoint-based provisional approval causal roadmap, illustrated by vaccine development.Biostatistics (Oxford, England) · 2025Article
- A Review of Methods for Research Synthesis.Statistics in medicine · 2025Article
- AAdvances in neural information processing systems · 2025Article
- Data fusion using weakly aligned sources.Journal of the American Statistical Association · 2025Article
- Keep asking: What do I want? What do I have? What do I do? : An approach for combining information to learn about a target population.European journal of epidemiology · 2025Article
- EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.Annals of statistics · 2024Article
- Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.American journal of epidemiology · 2024Article
- Testing for treatment effect twice using internal and external controls in clinical trials.Journal of causal inference · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including in the estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions, and rewards and one data source of the same covariates. In this work, we consider the general case where one or more data sources align with each part of the distribution of the target population, for example, the conditional distribution of the reward given actions and covariates. We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical simulations, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials.
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Registered trials
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