ArticleBiometrics2010
Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials.
Article in Biometrics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Can earlier biomarker measurements explain a treatment effect on diabetes incidence? A robust comparison of five surrogate markers.BMJ open diabetes research & care · 2023Trial
- Fold rise in antibody titers by measured by glycoprotein-based enzyme-linked immunosorbent assay is an excellent correlate of protection for a herpes zoster vaccine, demonstrated via the vaccine efficacy curve.The Journal of infectious diseases · 2014Trial
- Bayesian inference for the causal effect of mediation.Biometrics · 2012Trial
- Review
- SENSITIVITY ANALYSIS FOR EVALUATING PRINCIPAL SURROGATE ENDPOINTS RELAXING THE EQUAL EARLY CLINICAL RISK ASSUMPTION.The annals of applied statistics · 2022Article
- Translating questions to estimands in randomized clinical trials with intercurrent events.Statistics in medicine · 2022Article
- Informed decision-making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments.Pharmaceutical statistics · 2022Review
- Evaluation of treatment effect modification by biomarkers measured pre- and post-randomization in the presence of non-monotone missingness.Biostatistics (Oxford, England) · 2022Article
- Evaluating the surrogacy of multiple vaccine-induced immune response biomarkers in HIV vaccine trials.Biostatistics (Oxford, England) · 2021Article
- Predictive cluster level surrogacy in the presence of interference.Biostatistics (Oxford, England) · 2020Article
- Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials.Statistics in biosciences · 2019Article
- Simultaneous Inference of Treatment Effect Modification by Intermediate Response Endpoint Principal Strata with Application to Vaccine Trials.The international journal of biostatistics · 2019Article
- Evaluation and comparison of predictive individual-level general surrogates.Biostatistics (Oxford, England) · 2018Article
- Evaluating principal surrogate markers in vaccine trials in the presence of multiphase sampling.Biometrics · 2018Article
- An Introduction to Principal Surrogate Evaluation with the pseval Package.The R journal · 2016Article
- Augmented trial designs for evaluation of principal surrogates.Biostatistics (Oxford, England) · 2016Article
- Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition.Journal of causal inference · 2015Article
- Comparing and combining biomarkers as principal surrogates for time-to-event clinical endpoints.Statistics in medicine · 2015Article
- Evaluating principal surrogate endpoints with time-to-event data accounting for time-varying treatment efficacy.Biostatistics (Oxford, England) · 2014Article
- Hard, harder, hardest: principal stratification, statistical identifiability, and the inherent difficulty of finding surrogate endpoints.Emerging themes in epidemiology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratification. In this article, we consider two principal stratification estimands: joint risks and marginal risks. Joint risks measure causal associations (CAs) of treatment effects on S and Y, providing insight into the surrogate value of the biomarker, but are not statistically identifiable from vaccine trial data. Although marginal risks do not measure CAs of treatment effects, they nevertheless provide guidance for future research, and we describe a data collection scheme and assumptions under which the marginal risks are statistically identifiable. We show how different sets of assumptions affect the identifiability of these estimands; in particular, we depart from previous work by considering the consequences of relaxing the assumption of no individual treatment effects on Y before S is measured. Based on algebraic relationships between joint and marginal risks, we propose a sensitivity analysis approach for assessment of surrogate value, and show that in many cases the surrogate value of a biomarker may be hard to establish, even when the sample size is large.
Indexed as
Identifiers
What OpenQuestion holds
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.