ArticleStatistics in medicine2017
Evaluating surrogate marker information using censored data.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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
24 citing papers in PubMed.
- Flexible evaluation of surrogate markers with Bayesian model averaging.Statistics in medicine · 2024Trial
- Can earlier biomarker measurements explain a treatment effect on diabetes incidence? A robust comparison of five surrogate markers.BMJ open diabetes research & care · 2023Trial
- MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing.Observational studies · 2026Article
- Model-Free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival.Statistics in medicine · 2025Article
- Validation of a Longitudinal Marker as a Surrogate Using Mediation Analysis and Joint Modeling: Evolution of the PSA as a Surrogate of the Disease-Free Survival.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Robust evaluation of longitudinal surrogate markers with censored data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Avoiding the Surrogate Paradox: An Empirical Framework for Assessing Assumptions.Journal of nonparametric statistics · 2025Article
- Surrogate Marker Evaluation: A Tutorial Using R.Statistics in medicine · 2025Article
- A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker.Statistics in medicine · 2025Article
- Article
- Assessing heterogeneity in surrogacy using censored data.Statistics in medicine · 2024Article
- Surrogacy validation for time-to-event outcomes with illness-death frailty models.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Robust approach to combining multiple markers to improve surrogacy.Biometrics · 2023Article
- Using a surrogate with heterogeneous utility to test for a treatment effect.Statistics in medicine · 2023Article
- Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach.Biometrics · 2022Article
- Estimation of the proportion of treatment effect explained by a high-dimensional surrogate.Statistics in medicine · 2022Article
- Robust methods to correct for measurement error when evaluating a surrogate marker.Biometrics · 2022Article
- Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time-to-event outcomes in randomized controlled trials.Pharmaceutical statistics · 2022Article
- Quantifying the feasibility of shortening clinical trial duration using surrogate markers.Statistics in medicine · 2021Article
- Evaluating multiple surrogate markers with censored data.Biometrics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Given the long follow-up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow-up time is a very attractive goal. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying and validating surrogate markers tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. The ability to use such methods in practice when the primary outcome of interest is a time-to-event outcome is difficult because of censoring and missing surrogate information among those who experience the primary outcome before surrogate marker measurement. In this paper, we propose a novel definition of the proportion of treatment effect explained by surrogate information collected up to a specified time in the setting of a time-to-event primary outcome. Our proposed approach accommodates a setting where individuals may experience the primary outcome before the surrogate marker is measured. We propose a robust non-parametric procedure to estimate the defined quantity using censored data and use a perturbation-resampling procedure for variance estimation. Simulation studies demonstrate that the proposed procedures perform well in finite samples. We illustrate the proposed procedures by investigating two potential surrogate markers for diabetes using data from the Diabetes Prevention Program. Copyright © 2017 John Wiley & Sons, Ltd.
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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.