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REVIEW 4 major objections 4 minor 47 references

RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers Applied to Vaccinology

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A new rank-based procedure, RISE, claims that a 222-gene early-blood-expression signature is a trial-level surrogate for the neutralizing antibody response to influenza vaccination.

desk verdict RISE has a useful two-stage skeleton and honest simulations, but the adaptive margin cancels the outcome from the screening test, so the application's surrogate claim rests on a permissive, data-dependent threshold. read the letter →

arxiv 2502.03030 v3 pith:I5SVV3SL submitted 2025-02-05 stat.ME stat.AP

Arthur Hughes , Layla Parast , Rodolphe Thiébaut , Boris P. Hejblum This is my paper · ORCID
classification stat.MEstat.AP MSC 62G1062P1062G20
keywords surrogatemarkerhigh-dimensionalscreeningrank-basedinferencenonparametrictestinggeneexpressionsignaturevaccinetrialtranscriptomicsnon-inferioritytest
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes RISE, a two-stage procedure for finding, among thousands of candidate variables, a small combined marker that can stand in for a later clinical outcome, in settings where sample sizes are small and the number of candidate variables is large. RISE first tests each candidate individually with a non-parametric rank-based comparison of treatment effects, applying multiple-testing correction, then combines the candidates that pass into a weighted score and evaluates that score's treatment effect on independent data. Simulation results across two data-generating processes indicate controlled false positive rates for samples above about 30 and high power when surrogate strength is high. Applied to gene expression from an inactivated influenza vaccine study, RISE selects a 222-gene signature whose early day-1 expression tracks the day-28 neutralising antibody response among females, with a rank correlation of 0.77 and an evaluation p-value of 0.003. The signature is enriched in innate antiviral and interferon-stimulated genes, giving it a coherent biological interpretation.

What carries the argument

The load-bearing object is the rank-based effect measure U = P(value under vaccine > value under control) + 1/2 P(tie), estimated by a U-statistic, and the difference δ = UY - US, which places surrogacy on a common probability scale. RISE tests the non-inferiority hypotheses H0: δ ≥ ε and H0: δ ≤ -ε, so a surrogate is accepted only if both one-sided tests reject and the confidence interval for δ lies within [-ε, ε]. Screening uses these tests per candidate with a false-discovery-rate correction; evaluation combines selected candidates into γ_S = Σ |δ_j|^{-1} S̄_j, a standardized weighted sum in which stronger surrogates receive more weight, and then applies the same rank-based test to γ_S on split-sample data. The paired-data extension, closed-form variance from correlated U-statistic theory, and adaptive choice of ε from the desired power complete the procedure.

What would settle it

The applied claim would be settled by re-running the screening with a pre-specified ε fixed at 0.10 rather than the data-derived 0.29; the paper's own sensitivity table shows that no genes pass, so the conclusion that a reasonable surrogate exists would fail. A complementary check is external validation: applying the 222-gene combination to independent data from another trivalent influenza vaccine cohort and observing a δ confidence interval that does not fall within the pre-specified margin would falsify the trial-level surrogacy claim.

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Extended reading notes

Core claim

In the paper's own terms, RISE establishes that trial-level surrogacy can be assessed by comparing two rank-based effect measures: UY, the probability that a randomly chosen vaccinated individual has a higher outcome than a randomly chosen control, and USj, the same quantity for a candidate marker. A candidate is deemed valid when the difference δj = UY - USj lies within a non-inferiority margin ε, tested with unpaired or paired U-statistics and a two one-sided procedure that requires δj to fall inside [-ε, ε]. The screening step applies this test to every variable with multiplicity correction; the evaluation step combines the survivors into a standardized weighted sum γ_S whose weights are the inverse of the estimated |δj|, splits the data so evaluation uses independent observations, and re-tests the combined marker. In the application, the screening stage with ε = 0.29 and the strictest multiple-testing correction retained 222 genes; on the held-out evaluation data, the combined γ_S had δ = -0.038 (95% CI [-0.10, 0.025], p = 0.003). The authors conclude that γ_S is a reasonable trial-level surrogate for the neutralising antibody response to the 2008-2009 trivalent inactivated influenza vaccine in females.

Load-bearing premise

Everything rests on the non-inferiority margin ε being meaningful: RISE computes ε from the observed treatment effect and the desired power in the same data, so the set of genes declared surrogates changes with this internal choice, and a smaller externally fixed ε would leave no signature at all.

Editorial extensions

If this is right

  • In trials with 30-200 participants, RISE can screen tens of thousands of markers while keeping the false positive rate near nominal and achieving good power for strong surrogates.
  • A day-1 post-vaccination gene expression score could serve as a trial-level surrogate for the day-28 neutralizing antibody response, allowing vaccine candidates to be down-selected weeks earlier.
  • The identified 222-gene signature points to innate antiviral and interferon pathways as the biological bridge between early transcriptomic changes and later antibody production.
  • Because surrogate evaluation uses independent split-sample data, the reported strength is not an artifact of selecting genes on the same observations that evaluate them.
  • With paired designs and ordinal outcomes supported, the method can be applied beyond this vaccine study to other settings where early molecular readouts are candidates for later clinical endpoints.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The data-dependent choice of ε makes 'reasonable surrogate' a relative statement: an external standard with ε fixed at 0.10 would eliminate the signature, so RISE users should pre-register ε before screening.
  • RISE addresses trial-level surrogacy, not individual-level prediction; combining it with principal-stratification or meta-analytic surrogacy methods could connect the selected marker to individual treatment effects and cross-trial generalization.
  • A testable extension is to screen gene sets rather than single genes, aggregating co-expressed or pathway-defined modules before applying RISE; this could recover multivariate surrogates that univariate screening misses.
  • If the signature generalizes, the same two-stage rank pipeline could be repurposed to other vaccines or to correlates-of-protection searches, where the endpoint is infection rather than antibody titre.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces RISE, a two-stage rank-based procedure for screening and evaluating high-dimensional surrogate markers in small-sample settings. Stage one applies a univariate non-inferiority test to each candidate surrogate, with multiple testing correction, and stage two combines the selected markers into a weighted composite that is evaluated on an independent data split using a two one-sided test (TOST) procedure. The treatment effect measures are U-statistics (UY and US) based on comparing treated and control observations. The authors report simulation studies of false positive rate, false discovery proportion, and power under two data-generating processes, and apply RISE to a single-arm influenza vaccination study, claiming a 222-gene expression signature is a reasonable trial-level surrogate for the neutralising antibody response. The RISE package is available on CRAN and the paper includes reproducible R markdown files.

Significance. The paper targets a real gap: high-dimensional surrogate evaluation in small samples, where parametric surrogacy methods and kernel-based nonparametric alternatives struggle. The use of U-statistics, sample splitting, and multiple testing correction is sensible and the code/package availability is a strength. If the method worked as advertised, it would be a practical addition to the vaccinology toolkit. However, the current formulation of the non-inferiority margin is data-adaptive in a way that makes the surrogate test nearly tautological: it largely tests whether each candidate marker has its own detectable treatment effect rather than whether it approximates the effect on the primary outcome. The application additionally lacks a placebo arm, so the estimated 'treatment effects' are pre-post contrasts that cannot identify trial-level surrogacy. These issues are load-bearing for the paper's central claims, although they are in principle addressable through a fixed, pre-specified margin and a properly controlled study design.

major comments (4)
  1. [Section 2.2, Eq. (2)] The adaptive margin epsilon = max(0, bUY - u*) causes the primary outcome to cancel from the screening statistic. For any candidate j, the one-sided contrast is bdelta_j - epsilon = (bUY - bUS_j) - (bUY - u*) = u* - bUS_j, so the screening test reduces to testing whether the surrogate's own treatment effect exceeds the power-based threshold u*. The outcome Y enters only through the variance estimate, not through the effect comparison. This means the screening step does not evaluate whether US_j approximates UY. In the application, bUY = 0.97 yields epsilon = 0.29, and any gene with a sufficiently strong pre-post change passes; Table S2 shows that with a fixed epsilon = 0.10, zero genes are selected, and the evaluation TOST p-value for the reported bdelta = -0.038 and bsigma = 0.038 would be approximately 0.051. The 'reasonable surrogate' conclusion is therefore an artifact of the data-adaptive margin rather than evidence of closeness between US and UY.
  2. [Section 2.5] The simulation definition of a valid surrogate is circular. A variable is classified as valid if US > UY - epsilon, and epsilon is set to bUY - 0.5. Since UY* = 0.985 in DGP 1, this means any variable with US > 0.5 is valid. Consequently, the reported type I error, false discovery proportion, and power characterise the test of whether a surrogate has any positive treatment effect, not whether it captures the treatment effect on Y. The false positive rate simulation at the boundary US = 0.5 is a calibration check for a null hypothesis that is unrelated to the surrogacy question. The authors acknowledge in Section 2.5 that this choice of epsilon is 'unlikely to be useful', but the simulation results are still presented as supporting RISE as a surrogate identification tool.
  3. [Section 3.2] The application is a single-arm study without a placebo control. Pre-vaccination day 0 measurements are treated as the 'control' observations (Y0, S0), while post-vaccination measurements are taken on day 1 for the surrogates and day 28 for the outcome. Under the counterfactual notation of Section 2.1, Y0 is the outcome under no vaccine at the same follow-up time. Comparing day 0 to day 28 post-vaccine estimates the probability that a post-vaccine value exceeds a pre-vaccine value, which conflates vaccine effects with time trends, assay drift, and regression to the mean. As a result, UY and US are not identified treatment effects, and trial-level surrogacy is not identifiable in this application, even with a fixed and clinically meaningful margin.
  4. [Section 3.2, evaluation stage] The evaluation stage uses the same adaptive margin, epsilon = bUY - u* = 0.14. In the TOST, p(1) = Phi((u* - bUS)/sigma) and p(2) = 1 - Phi((2bUY - bUS - u*)/sigma). With bUY = 0.96, p(2) is essentially zero for any plausible value of bUS, so the combined test is effectively a one-sided test that bUS > u*. The reported p = 0.003 is therefore not evidence that delta is small in any fixed sense; it is evidence that the composite marker shows a strong pre-post change. The Spearman correlation of 0.77 in Figure 5 is a marginal association and does not establish trial-level surrogacy. This reinforces that the evaluation step, like the screening step, is not testing whether the surrogate's treatment effect approximates that of the primary outcome.
minor comments (4)
  1. [Section 3.1] The text uses 'empirical FDR' and 'false discovery proportion' interchangeably (e.g., Figure 3 caption and the paragraph discussing Scenario 2). The paper should use 'FDP' consistently, since FDR traditionally denotes the expected value of the FDP under a multiple testing procedure.
  2. [Table 1 and Table S1] The column header says 'Bonferroni Adjusted p-value' but the table also includes unadjusted p-values; the header should distinguish the columns more clearly. Also, several gene symbols contain typographical spacing errors (e.g., 'V AMP5' and 'TNF AIP6' should be 'VAMP5' and 'TNFAIP6').
  3. [Section 2.2] The p-value formula 'pj = P(Z < bδj) where Z ∼ N(ϵ, bσδj )' is unconventional; it is clearer to write pj = Phi((bδj - epsilon)/bσδj ) or to define the test statistic explicitly as a standard normal variate under the null.
  4. [Figure 5 caption] The caption contains a duplicated string 'ρ = 0.77ρ = 0.77ρ = 0.77'; this should be a single value.

Circularity Check

2 steps flagged · score 8.0 of 10

RISE's data-adaptive non-inferiority margin cancels the outcome from the screening and evaluation statistics, so the claimed surrogate is forced by construction; fixed margins make the signature disappear.

  1. fitted input called prediction [Section 2.2, Eq. (2); Section 2.4 Step 1; applied in Section 3.2]
    "if the estimated treatment effect is bUY , the significance level α and the desired power to detect a treatment effect based upon the candidate surrogate Sj is (1 − β), one may select ϵ as: ϵ = max(0, buY − u∗α,β) (2)"

    Under Eq. (2), whenever bUY > u∗ the margin is ϵ = bUY − u∗. The screening p-value for H0 : δj ≥ ϵ is computed from bδj = bUY − bUSj, so (bδj − ϵ)/bσ = (u∗ − bUSj)/bσ. The antibody-treatment effect bUY cancels identically; the test reduces to whether the gene's own pre/post rank effect bUSj exceeds the power threshold u∗. The outcome Y enters only through the standard error, not the effect comparison. Thus the screen for 'surrogates' is, by construction, a screen for vaccine-responsive genes, and the fitted margin (estimated from bUY in the same data) is presented as a surrogacy prediction.

  2. fitted input called prediction [Section 3.2, evaluation stage; Supporting Information Table S2]
    "In the evaluation dataset, the estimated value of UY was 0.96, corresponding to ϵ = 0.14 for a desired 0.90 powered test based on γS. The value of δ was found to be −0.038 (95% C.I. [−0.10, 0.025]), yielding a p-value of 0.003. These results suggest that the constructed γS is a reasonable trial-level surrogate for the neutralising antibody response to the 2008-2009 TIV amongst females."

    Same cancellation: in the evaluation subset ϵ = bUY − u∗ with bUY = 0.96, so the reported TOST p-value is max{Φ((−0.038−0.14)/0.038), 1−Φ((−0.038+0.14)/0.038)} ≈ 0.003. The binding component only requires bUSγ ≈ 0.998 to exceed u∗ ≈ 0.82; equality with bUY is not tested. With a fixed margin ϵ = 0.10, the same estimate gives p ≈ 0.052, and Table S2 shows fixed screening margins ϵ ≤ 0.10 select zero genes. The 'reasonable surrogate' conclusion is therefore an artifact of the data-adaptive margin—a fitted input called a prediction.

full rationale

The central reduction is algebraic and appears twice. Eq. (2) defines the non-inferiority margin from the same bUY used in the test statistic; substituting ϵ = bUY − u∗ into bδj = bUY − bUSj cancels bUY. So both stages of RISE test bUS > u∗, not closeness of S to Y. The application's p = 0.003 is the same cancellation: with ϵ = 0.14 it passes, with a fixed ϵ = 0.10 it would fail (p ≈ 0.052), and Table S2 shows no genes survive screening at ϵ ≤ 0.10. The paper itself concedes in the Discussion that 'the selection and interpretation of the non-inferiority margin ϵ ... remains ad-hoc and challenging to interpret' and recommends sensitivity analyses. The simulation studies are not circular in themselves: they calibrate type I error and power of the U-statistic testing procedure under a fixed data-generating process. But the headline application claim that γS is a 'reasonable trial-level surrogate' is forced by the data-dependent margin, so the derivation is substantially circular. The cited prior work [23] supplies the very adaptive margin that causes the cancellation, but the circularity is algebraic rather than merely citational; no external benchmark rescues the application claim because the TIV data are single-arm and paired.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim depends on a data-dependent equivalence margin (two free parameters), on a normal approximation for the paired U-statistic difference at small n, and on an untested causal interpretation of pre/post changes without a control group.

free parameters (3)
  • epsilon_screening = 0.29 in application; UY - 0.5 in simulations
    Non-inferiority margin in Step 1, computed from the observed UY via Eq. (2). Controls how many surrogates pass screening; the 222-gene signature disappears for epsilon <= 0.10 (Table S2).
  • epsilon_evaluation = 0.14 in application
    Non-inferiority margin in Step 2, computed from the evaluation-subset UY. A delta of -0.038 is declared equivalent because the CI lies within +/- 0.14.
  • desired_power = 0.90 (1-beta)
    User-specified power used to derive epsilon; arbitrary, and has a direct effect on the set of selected surrogates.
assumptions (3)
  • standard math Mann-Whitney U-statistics bUY and bUS are asymptotically normal, and bdelta is asymptotically normal with variance sigma_d^2/n in the paired design.
    Used throughout for p-values and CIs; the normality of bdelta is assumed for evaluation with n approx 26 without simulation support (Supporting Information S1).
  • domain assumption Conditions C1-C3 (monotone surrogate-response association, non-negative treatment effect on surrogate, non-negative residual treatment effect) hold for each candidate surrogate.
    Invoked from Parast et al. [23] to rule out the surrogate paradox; not tested in the data application (Section 2.2).
  • ad hoc to paper The pre-post change in gene expression and antibody titre within a single arm estimates the vaccine treatment effect.
    The application has no placebo group; the paired design treats day-0 vs day-1/28 differences as causal vaccine effects (Section 3.2).

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Cite this review

Pith. "Pith review of RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers Applied to Vaccinology." pith.science (2026). https://pith.science/paper/I5SVV3SL

@misc{pith2026250203030,
  author       = {Pith},
  title        = {Pith review of: RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers Applied to Vaccinology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I5SVV3SL}},
  note         = {Machine review of arXiv:2502.03030}
}
read the original abstract

In vaccine trials with long-term participant follow-up, it is of great importance to identify surrogate markers that accurately infer long-term immune responses. These markers offer practical advantages such as providing early, indirect evidence of vaccine efficacy, and can accelerate vaccine development while identifying potential biomarkers. High-throughput technologies like RNA-sequencing have emerged as promising tools for understanding complex biological systems and informing new treatment strategies. However, these data are high-dimensional, presenting unique statistical challenges for existing surrogate marker identification methods. We introduce Rank-based Identification of high-dimensional SurrogatE Markers (RISE), a novel approach designed for small sample, high-dimensional settings typical in modern vaccine experiments. RISE employs a non-parametric univariate test to screen variables for promising candidates, followed by surrogate evaluation on independent data. Our simulation studies demonstrate RISE's desirable properties, including type one error rate control and empirical power under various conditions. Applying RISE to a clinical trial for inactivated influenza vaccination, we sought to identify genes whose expression could serve as a surrogate for the induced immune response. This analysis revealed a signature of genes appearing to function as a reasonable surrogate for the neutralising antibody response. Pathways related to innate antiviral signalling and interferon stimulation were strongly represented in this derived surrogate, providing a clear immunological interpretation.

Figures

Figures reproduced from arXiv: 2502.03030 by the authors.

Figure 1
Figure 1. Data generation process 1, scenario 1: boxplots of observed false positive rates against different [PITH_FULL_IMAGE:figures/full_fig_p020_1.png] view at source ↗
Figure 2
Figure 2. Data generation process 1, scenario 1: violin plots of observed false positive rates against [PITH_FULL_IMAGE:figures/full_fig_p021_2.png] view at source ↗
Figure 3
Figure 3. Data generation process 1, scenario 2: empirical power (left) and false discovery proportion [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Data generation process 1: the distributions of the p-values in the evaluation step are examined [PITH_FULL_IMAGE:figures/full_fig_p022_4.png]
Figure 5
Figure 5. Figure 5: Ranks of the mean cross-strain neutralising antibodies against the ranks of the constructed 222- [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.