ArticleBiometrics2021
Combining primary cohort data with external aggregate information without assuming comparability.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
The trial behind it
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
11 citing papers in PubMed.
- Incorporating external risk information with the Cox model under population heterogeneity: applications to trans-ancestry polygenic hazard scores.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- survkl: an R package for transfer-learning-based integrated Cox models.Bioinformatics advances · 2026Article
- Characteristics of cohort data management systems (CDMS): a scoping review.BMC medical research methodology · 2025Article
- KULLBACK-LEIBLER-BASED DISCRETE FAILURE TIME MODELS FOR INTEGRATION OF PUBLISHED PREDICTION MODELS WITH NEW TIME-TO-EVENT DATASET.The annals of applied statistics · 2025Article
- A Weighted Survival Regression Framework for Incorporating External Prediction Information.Journal of statistical theory and practice · 2025Article
- Article
- 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
- Likelihood adaptively incorporated external aggregate information with uncertainty for survival data.Biometrics · 2024Article
- Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.Biometrics · 2024Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In comparative effectiveness research (CER) for rare types of cancer, it is appealing to combine primary cohort data containing detailed tumor profiles together with aggregate information derived from cancer registry databases. Such integration of data may improve statistical efficiency in CER. A major challenge in combining information from different resources, however, is that the aggregate information from the cancer registry databases could be incomparable with the primary cohort data, which are often collected from a single cancer center or a clinical trial. We develop an adaptive estimation procedure, which uses the combined information to determine the degree of information borrowing from the aggregate data of the external resource. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. The proposed method yields a substantial gain in statistical efficiency over the conventional method using the primary cohort only, and avoids undesirable biases when the given external information is incomparable to the primary cohort. We apply the proposed method to evaluate the long-term effect of trimodality treatment to inflammatory breast cancer (IBC) by tumor subtypes, while combining the IBC patient cohort at The University of Texas MD Anderson Cancer Center and the external aggregate information from the National Cancer Data Base.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.