ArticleBiometrika2023
Data integration: exploiting ratios of parameter estimates from a reduced external model.
Article in Biometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Robust angle-based transfer learning in high dimensions.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Data fusion using weakly aligned sources.Journal of the American Statistical Association · 2025Article
- A comparison of some existing and novel methods for integrating historical models to improve estimation of coefficients in logistic regression.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Integrating external summary information in the presence of prior probability shift: an application to assessing essential hypertension.Biometrics · 2024Article
- Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.Biometrics · 2024Article
- Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.American journal of respiratory and critical care medicine · 2024Article
- Robust data integration from multiple external sources for generalized linear models with binary outcomes.Biometrics · 2024Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable [Formula: see text] is binary and there are two sets of covariates, [Formula: see text] and [Formula: see text]. We have information from an external study that provides parameter estimates for a generalized linear model of [Formula: see text] on [Formula: see text]. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations. The method involves orthogonalizing the [Formula: see text] variables and then borrowing information about the ratio of the coefficients from the external model. The method is justified based on a new result relating the parameters in a generalized linear model to the parameters in a generalized linear model with omitted covariates. The method is applicable if the regression coefficients in the [Formula: see text] given [Formula: see text] model are similar in the two populations, up to an unknown scalar constant. This type of transportability between populations is something that can be checked from the available data. The asymptotic variance of the proposed method is derived. The method is evaluated in a simulation study and shown to gain efficiency compared to simple analysis of the internal dataset, and is robust compared to an alternative method of incorporating external information.
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