ArticleJournal of the American Statistical Association2016
Efficient Estimation of the Cox Model With Auxiliary Subgroup Survival Information.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.Statistics in medicine · 2026Article
- 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
- Selection processes, transportability, and failure time analysis in life history studies.Biostatistics (Oxford, England) · 2025Article
- A Review of Methods for Research Synthesis.Statistics in medicine · 2025Article
- A New Inverse Probability of Selection Weighted Cox Model to Deal With Outcome-Dependent Sampling in Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 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
- Improving estimation efficiency for survival data analysis by integrating a coarsened time-to-event outcome from an external study.Biometrics · 2025Article
- Methodological challenges in studying disease processes using observational cohort data.Japanese journal of statistics and data science · 2025Article
- A Weighted Survival Regression Framework for Incorporating External Prediction Information.Journal of statistical theory and practice · 2025Article
- Likelihood adaptively incorporated external aggregate information with uncertainty for survival data.Biometrics · 2024Article
- Integrating Information from Existing Risk Prediction Models with No Model Details.The Canadian journal of statistics = Revue canadienne de statistique · 2023Article
- Noniterative adjustment to regression estimators with population-based auxiliary information for semiparametric models.Biometrics · 2023Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
- Efficient estimation of a Cox model when integrating the subgroup incidence rate information.Journal of applied statistics · 2023Article
- Risk Projection for Time-to-event Outcome Leveraging Summary Statistics With Source Individual-level Data.Journal of the American Statistical Association · 2022Article
- Combining primary cohort data with external aggregate information without assuming comparability.Biometrics · 2021Article
- Additive hazards model with auxiliary subgroup survival information.Lifetime data analysis · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
With the rapidly increasing availability of data in the public domain, combining information from different sources to infer about associations or differences of interest has become an emerging challenge to researchers. This paper presents a novel approach to improve efficiency in estimating the survival time distribution by synthesizing information from the individual-level data with
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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.