Surrogate markers are often employed in clinical trials to replace primary outcomes that may be difficult, expensive, or time-consuming to measure directly. These markers can accelerate the evaluation of new treatments, provided they reliably capture the causal relationship between treatment and true clinical benefit. Parast et al. (2024) recently proposed a rank-based approach for evaluating surrogate markers, characterized by its nonparametric nature and minimal assumptions. While this method is useful in small-sample model-agnostic settings, it has several limitations, including a lack of clear causal interpretation, low statistical power, and insufficient robustness to different data-generating mechanisms. In this paper, we propose a Bayesian approach that addresses these shortcomings by focusing on causal treatment effect estimands and, in doing so, improves power through covariate adjustment. We demonstrate the advantages of our proposed method through a simulation study designed to highlight gains in both accuracy and power.
Archival XMM and ROSAT X-ray data are used to investigate the structure of the Abell 548 - Abell 3367 region. Based on previous optical studies, this is a region likely to be rich in structure though studies are in disagreement regarding the connection between Abell 3367 and Abell 548. We use the available archival X-ray data together with kinematic data of counterpart galaxies to address this question and to determine the structure in this region. The region is particularly rich in X-ray structure elongated along a SW-NE axis consisting of numerous extended X-ray sources. In general, the structure consists of many galaxy groups and clusters which appear segregated in X-ray luminosity with the least luminous $\sim$ 30% toward the outer region of the clusters, possibly tracing a filament. We find evidence to suggest a supercluster of 3 clusters at redshifts: $\sim$ 0.04, 0.045, and 0.06. Some of the X-ray sources coincident with Abell 3367 have counterpart galaxy redshifts consistent with Abell 548 and others are significantly higher. This supports that Abell 548 and Abell 3667, form a supercluser and the higher redshift X-ray source is a background object. They are part of a larger
When direct measurement of a clinically relevant primary endpoint in a clinical trial is infeasible, a surrogate endpoint may be used instead to infer treatment effects. Trial-level surrogates predict the average treatment effect on the primary endpoint and may be evaluated within the meta-analytic framework. However, traditional methods are ill-suited to the complex high-dimensional data now increasingly collected in modern trials, such as omics data. Although methods for high-dimensional surrogate evaluation exist, they have largely been developed for single-trial settings and therefore cannot assess surrogate generalisability. Here, we propose RISE-Meta, an approach for evaluating trial-level surrogate markers in the multi-trial, high-dimensional setting. In the first stage, an existing nonparametric method is applied to individual participant data to derive study-level surrogacy metrics for each candidate marker. Next, random-effects meta-analysis combines these metrics across studies, and equivalence testing provides operational criteria for surrogate validity. Finally, a subset of candidates is combined into a composite signature through a weighting scheme to improve surrogac
Objectives: To develop a codebook for self-stigma across cognitive, affective, and behavioral domains, and to estimate the prevalence, co-occurrence, and temporal patterns of these indicators in Reddit posts by people who use drugs. Methods: We developed a ten-indicator codebook through consensus-based abductive coding spanning cognitive (self-labeling, pessimism/self-defeatism, deservingness/worthlessness), affective (shame, guilt/self-blame, despair/hopelessness), and behavioral (concealment, anticipated rejection, desire to quit, ambivalence) domains; two coders reached substantial agreement (Cohen's k = 0.72). We then scaled classification with a large language model validated against expert coding (k = 0.73, F1 = 0.80), analyzing 72,115 thread-initiating posts from 1,660 English-language users (2006-2025). Results: 3,838 posts (5.3%) from 1,228 users (74.0%) contained self-stigma; all ten indicators discriminated self-stigma posts (RR 3.6 to 86.2), led by self-labeling (56.0%) and despair/hopelessness (48.5%). Self-stigma was integrated: core and behavioral indicators were strongly associated at the user level (OR = 4.65, 95% CI 3.12-6.94, p < 0.001), and 87.0% of posts wit
Evaluating treatment effects is critical in clinical trials but sometimes involves lengthy, invasive, or costly follow-up procedures. In these cases, surrogate markers, which provide intermediate measures of the long-term treatment effect, allow clinicians to obtain results faster and more efficiently than would have otherwise been possible. Prior to adoption, it is vital that the utility of surrogate markers (i.e., their ability to capture the treatment effect on the primary outcome) is statistically validated. Many frameworks for evaluating surrogate markers have been proposed, but they do not account for missing data. Instead, they rely on complete cases (the subset of patients without missing data), which can be inefficient and biased. To improve on this, we propose methods to accommodate missing data in nonparametric and parametric surrogate evaluation via inverse probability weighting (IPW) and semiparametric maximum likelihood estimation (SMLE). Through simulation studies, we demonstrate that the proposed methods remain unbiased under a broader range of missing data mechanisms than complete case analysis and can help retain the statistical precision of the full trial. We ill
A surrogate marker is a biomarker or other physical measurement used to replace a primary outcome in clinical trials to evaluate a treatment effect when the primary outcome of interest is costly, invasive, or takes a long time to observe. However, replacing a primary outcome with a surrogate can lead to the "surrogate paradox," in which a treatment appears beneficial based on the surrogate but is actually harmful with respect to the primary outcome. In this paper, we propose a functional class-based method to assess resilience to the surrogate paradox in a meta-analytic setting. Our method leverages data from K completed studies in which the surrogate marker and primary outcome have been measured to make inference on a new study in which only the surrogate is measured. We do not assume direct transportability of the conditional mean function from the completed studies to the new study; instead, we consider deviations of functions from those observed in the completed studies to estimate the "resilience probability" i.e., the probability of the surrogate paradox in the new study. We investigate the performance of our proposed method through a simulation study and apply our method to
We study the Prishchepov groups $P(r,n,k,s,q)$, a unifying family of cyclically presented groups that encompasses many classical cases. For $n$ coprime to $6$, we prove a conjecture essentially characterizing when these groups are perfect: namely, $n$ divides either $2(k-1)-q$ (if $r \geq s$) or $q(r+s)$. This settles the classification of perfect Prishchepov groups under the co-primality condition.
Self-stigma predicts treatment avoidance and disengagement among people who use drugs (PWUD), yet conversational systems aiming to provide support typically treat self-stigma expression as a uniform signal. We present a three-phase, proof-of-concept study of a persona-aware approach to LLM support. Latent Profile Analysis (LPA) on indicator-level features from 1,174 self-stigma expressors on Reddit yields a four-persona typology validated against held-out behavioral and linguistic features. Sequential Bayesian and recurrent neural classifiers recover these personas from limited posting histories, substantially outperforming batch and few-shot LLM baselines (macro-F1 = 0.74 at 30 posts). Evaluation by eight clinical experts across three contemporary LLMs revealed a misalignment: persona-matched responses successfully achieved targeted behavioral shifts, yet raters holistically preferred the generic empathy of the persona-neutral baseline. Our findings suggest that holistic empathy judgments and clinically-aligned response design can pull in opposite directions, and that evaluating LLM-based stigma support requires rubrics capable of decomposing the two.
With increasing freight demands for inner-city transport, shifting freight from road to scheduled line services such as buses, metros, trams, and barges is a sustainable solution. Public authorities typically impose economic policies, including road taxes and subsidies for scheduled line services, to achieve this modal shift. This study models such a policy using a bi-level approach: at the upper level, authorities set road taxes and scheduled line subsidies, while at the lower level, freight forwarders arrange transportation via road or a combination of road and scheduled lines. We prove that fully subsidizing the scheduled line is an optimal and budget-efficient policy. Due to its computational complexity, we solve the problem heuristically using a bi-section algorithm for the upper level and an Adaptive Large Neighbourhood Search for the lower level. Our results show that optimally setting subsidy and tax can reduce the driving distance by up to 12.5\% and substantially increase modal shift, albeit at a higher operational cost due to increased taxes. Furthermore, increased scheduled line frequency and decreased geographical scatteredness of freight orders increase modal shift. F
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 vac
In modern clinical trials, there is immense pressure to use surrogate markers in place of an expensive or long-term primary outcome to make more timely decisions about treatment effectiveness. However, using a surrogate marker to test for a treatment effect can be difficult and controversial. Existing methods tend to either rely on fully parametric methods where strict assumptions are made about the relationship between the surrogate and the outcome, or assume the surrogate marker is valid for the entire study population. In this paper, we develop a fully nonparametric method for efficient testing using surrogate information (ETSI). Our approach is specifically designed for settings where there is heterogeneity in the utility of the surrogate marker, i.e., the surrogate is valid for certain patient subgroups and not others. ETSI enables treatment effect estimation and hypothesis testing via kernel-based estimation for a setting where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully only measured in the remaining patients. In addition, we provide a framework for future study design with pow
Surrogate markers offer the potential to reduce the burden of data collection by replacing costly or invasive primary outcomes with more accessible measurements, provided that they can faithfully indicate the effectiveness of a treatment. However, appropriate evaluation of a surrogate is particularly complex in longitudinal studies, where both outcomes and surrogates can evolve dynamically over time and interest lies not only in the treatment effect at one time, but rather treatment effects that may vary along the entire trajectory. In this paper, we develop a statistical framework for surrogate evaluation when both the surrogate and primary outcome are measured over time. Specifically, within the potential outcomes framework, we propose a formal causal definition of the proportion of the treatment effect on the longitudinal primary outcome that is explained by the treatment effect on the longitudinal surrogate. For estimation, we leverage state-space models, together with the Kalman filter and smoother, enabling efficient estimation of treatment effects under realistic conditions of temporal evolution and patient-level variability. We introduce a nonparametric bootstrap strategy f
Problem definition: To mitigate excessive crowding in public transit networks, network expansion is often not feasible due to financial and time constraints. Instead, operators are required to make use of existing infrastructure more efficiently. In this regard, this paper considers the problem of determining lines and frequencies in a public transit system, factoring in the impact of crowding. Methodology: We introduce a novel formulation to address the line planning problem under crowding and propose a mixed-integer second-order cone programming (MI-SOCP) reformulation. Three variants of the cut-and-column generation algorithm with tailored acceleration techniques find near-system-optimal solutions by dynamically generating passenger routes and adding linear cutting planes to deal with the non-linearity introduced by the crowding terms. We find integral solutions using a diving heuristic. In practice, passengers may deviate from system-optimal routes. We, thus, evaluate line plans by computing a user-equilibrium routing based on Wardrop's first principle. Results and implications: We experimentally evaluate the performance of the proposed approaches on both an artificial network
We revisit Gerber's Informational Quality (IQ) framework, a data-driven approach for constructing correlation matrices from co-movement evidence, and address two obstacles that limit its use in portfolio optimization: guaranteeing positive semidefinite ness (PSD) and controlling spectral conditioning. We introduce a squeezing identity that represents IQ estimators as a convex-like combination of structured channel matrices, and propose an atomic-IQ parameterization in which each channel-class matrix is built from PSD atoms with a single class-level normalization. This yields constructive PSD guarantees over an explicit feasibility region, avoiding reliance on ex-post projection. To regulate conditioning, we develop an analytic eigen floor that targets either a minimum eigenvalue or a desired condition number and, when necessary, repairs PSD violations in closed form while remaining compatible with the squeezing identity. In long-only tangency back tests with transaction costs, atomic-IQ improves out-of-sample Sharpe ratios and delivers a more stable risk profile relative to a broad set of standard covariance estimators.
Multi-objective Bayesian optimization (MOBO) provides a principled framework for navigating trade-offs in molecular design. However, its empirical advantages over scalarized alternatives remain underexplored. We benchmark a simple Pareto-based MOBO strategy - Expected Hypervolume Improvement (EHVI) - against a simple fixed-weight scalarized baseline using Expected Improvement (EI), under a tightly controlled setup with identical Gaussian Process surrogates and molecular representations. Across three molecular optimization tasks, EHVI consistently outperforms scalarized EI in terms of Pareto front coverage, convergence speed, and chemical diversity. While scalarization encompasses flexible variants - including random or adaptive schemes - our results show that even strong deterministic instantiations can underperform in low-data regimes. These findings offer concrete evidence for the practical advantages of Pareto-aware acquisition in de novo molecular optimization, especially when evaluation budgets are limited and trade-offs are nontrivial.
Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes extends beyond these settings and may be more pronounced in real-world public health and social science research, where randomized trials are often impractical. Research on identifying surrogates in real-world non-randomized data is scarce, as available statistical approaches for evaluating surrogate markers tend to rely on the assumption that treatment is randomized. While the few methods that allow for non-randomized treatment/exposure appropriately handle confounding individual characteristics, they do not offer a way to examine surrogate heterogeneity with respect to patient characteristics. In this paper, we propose a framework to assess surrogate heterogeneity in real-world, i.e., non-randomized, data and implement this framework using various meta-learners. Our approach allows us to quantify heterogeneity in surrogate strength with respect to patient characteristics while accommodating confounders through the use of flexible, off-the-shelf machine learning methods. In addition, we use our framework to identify individuals for whom the surro
TikTok has emerged as a major source of information and social interaction for youth, raising urgent questions about how substance use discourse manifests and circulates on the platform. This paper presents the first comprehensive analysis of publicly visible, algorithmically surfaced substance-related content on TikTok, drawing on hashtags spanning all major substance categories. Using a mixed-methods approach that combines social network analysis with qualitative content coding, we examined 2,333 substance-related hashtags, identifying 16 distinct hashtag communities and characterizing their structural and thematic relationships. Our network analysis reveals a highly interconnected small-world structure in which recovery-focused hashtags such as \textit{\#addiction}, \textit{\#recovery}, and \textit{\#sober} serve as central bridges between communities. Qualitative analysis of 351 representative videos shows that Recovery Advocacy content (33.9\%) and Satirical content (28.2\%) dominate, while direct substance depiction appears in only 26\% of videos, with active use shown in just 6.5\% of them. These findings suggest that the algorithmically surfaced layer of substance-related d
Surrogate markers are often used in clinical trials to evaluate treatment effects when primary outcomes are costly, invasive, or take a long time to observe. However, reliance on surrogates can lead to the surrogate paradox, where a treatment appears beneficial based on the surrogate but is actually harmful with respect to the primary outcome. In this paper, we propose formal measures to assess resilience against the surrogate paradox. Our setting assumes an existing study in which the surrogate marker and primary outcome have been measured (Study A) and a new study (Study B) in which only the surrogate is measured. Rather than assuming transportability of the conditional mean functions across studies, we consider a class of functions for Study B that deviate from those in Study A. Using these, we estimate the distribution of potential treatment effects on the unmeasured primary outcome and define resilience measures including a resilience probability, resilience bound, and resilience set. Our approach complements traditional surrogate validation methods by quantifying the plausibility of the surrogate paradox under controlled deviations from what is known from Study A. We investig
In this paper we finish our classification of nilpotent symplectic alternating algebras of dimension 10 over any field F.