ArticleJournal of nonparametric statistics2025
Avoiding the Surrogate Paradox: An Empirical Framework for Assessing Assumptions.
Article in Journal of nonparametric statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Assessing surrogate heterogeneity in real world data using meta-learners.Journal of causal inference · 2026Article
- 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
- Surrogate Marker Evaluation: A Tutorial Using R.Statistics in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The use of surrogate markers to replace a primary outcome in clinical trials has the potential to allow earlier decisions about the effectiveness of a treatment when a direct measurement of the primary outcome is difficult to obtain. However, the surrogate paradox, which occurs when a treatment has a positive effect on the surrogate marker but a negative effect on the primary outcome, may lead researchers to make incorrect conclusions about the treatment benefit. In this paper, we propose a formal nonparametric framework to empirically examine and test assumptions that ensure avoidance of the surrogate paradox. For each assumption, we propose a nonparametric hypothesis test, formally derive the properties of the test, and analyze its performance in finite samples in a variety of simulation settings. We apply our proposed testing framework to data from the the Diabetes Prevention Program clinical trial.
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.