ArticleStatistics in medicine2015
Comparing and combining biomarkers as principal surrogates for time-to-event clinical endpoints.
Article in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Surrogate Marker Evaluation: A Tutorial Using R.Statistics in medicine · 2025Article
- Standardized statistical framework for comparison of biomarkers: Techniques from ADNI.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Surrogacy validation for time-to-event outcomes with illness-death frailty models.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Estimation of the proportion of treatment effect explained by a high-dimensional surrogate.Statistics in medicine · 2022Article
- Evaluation of Surrogate Endpoints Using Information-Theoretic Measure of Association Based on Havrda and Charvat Entropy.Mathematics (Basel, Switzerland) · 2022Article
- Evaluating multiple surrogate markers with censored data.Biometrics · 2021Article
- Inference on treatment effect modification by biomarker response in a three-phase sampling design.Biostatistics (Oxford, England) · 2020Article
- Assessing the value of a censored surrogate outcome.Lifetime data analysis · 2020Article
- Predictive cluster level surrogacy in the presence of interference.Biostatistics (Oxford, England) · 2020Article
- Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial.Journal of causal inference · 2020Article
- Ongoing Vaccine and Monoclonal Antibody HIV Prevention Efficacy Trials and Considerations for Sequel Efficacy Trial Designs.Statistical communications in infectious diseases · 2019Article
- Evaluation and comparison of predictive individual-level general surrogates.Biostatistics (Oxford, England) · 2018Article
- Evaluating surrogate marker information using censored data.Statistics in medicine · 2017Article
- An Introduction to Principal Surrogate Evaluation with the pseval Package.The R journal · 2016Article
- Predicting Overall Vaccine Efficacy in a New Setting by Re-Calibrating Baseline Covariate and Intermediate Response Endpoint Effect Modifiers of Type-Specific Vaccine Efficacy.Epidemiologic methods · 2016Article
- Modeling HIV vaccine trials of the future.Current opinion in HIV and AIDS · 2016Review
- Augmented trial designs for evaluation of principal surrogates.Biostatistics (Oxford, England) · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial.
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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.