Nevin Manimala

Eur Heart J. 2026 May 21:ehag336. doi: 10.1093/eurheartj/ehag336. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Myocardial infarction (MI) stands as a prominent manifestation of cardiovascular events. Most of the regenerative effects of stem cell therapies for MI are paracrine. A clinically translatable strategy that harnesses regenerative secretome while enabling minimally invasive delivery is needed. This study evaluated Regenerative Encapsulated Secretome as Cardiac Acellular Therapy (RESCAT), a formulation composed of cardiac stromal cell-derived secretome encapsulated in microparticles and embedded within a hyaluronic acid hydrogel, delivered via intrapericardial injection in a porcine model of MI.

METHODS: MI was induced using minimally invasive techniques. RESCAT was administered through clinically feasible intrapericardial delivery. Cardiac structure and function were assessed longitudinally in vivo. After the endpoint, infarct size and cardiomyocyte cell-cycle activity were assessed with histology. Single-nucleus RNA sequencing was performed to characterize cardiomyocyte transcriptional states and identify molecular pathways associated with therapeutic response.

RESULTS: RESCAT-treated pigs showed improved cardiac function and reduced infarct size compared with control groups. Enhanced cardiomyocyte cell-cycle activity and alterations in cardiomyocyte functional state were also observed. Single-nucleus transcriptomic analysis identified an FN1-expressing cardiomyocyte subtype linked to the activation of the PI3K-Akt pathway, which plays a role in cell survival and growth.

CONCLUSIONS: Intrapericardial delivery of RESCAT promotes functional and structural cardiac recovery in a clinically relevant porcine MI model. These findings support a minimally invasive, off-the-shelf acellular therapeutic strategy that enhances endogenous repair mechanisms and provides a foundation for translational development in ischaemic heart disease.

PMID:42166700 | DOI:10.1093/eurheartj/ehag336

Biometrics. 2026 Apr 9;82(2):ujag086. doi: 10.1093/biomtc/ujag086.

ABSTRACT

Survivorship (or selection) bias arises within statistical analyses where the observed data are subject to some underlying selection process prior to entry into the sampled data. For example, within capture-recapture studies, a primary selection mechanism is the survival until initial capture time. The common Cormack-Jolly-Seber model conditions on the first time an individual is observed, leading to potential survivorship bias. However, while the issue of survivorship bias has been well studied in many fields, there has been little exploration within the capture-recapture framework. In particular, we focus on individual (continuous) random effect Cormack-Jolly-Seber models, where it is assumed that individuals have different survival probabilities, specified to be from some common underlying distribution. We discuss the implications of the survivorship bias within the data collection process, and describe a novel modeling approach that accounts for the survivorship bias within an ecologically sensible manner. Using simulated data, we demonstrate the significant impact of ignoring the survivorship bias present in the data. We fit the corrected model to a guillemot data set and demonstrate that even with relatively mild selection bias, the individual heterogeneity variability is substantially underestimated when ignoring this survivorship bias.

PMID:42166193 | DOI:10.1093/biomtc/ujag086

Biometrics. 2026 Apr 9;82(2):ujag091. doi: 10.1093/biomtc/ujag091.

ABSTRACT

This paper considers survival analysis of large-scale data distributed across heterogeneous network nodes. We propose a novel method, the Distributed Spanning-Tree-Based Fused Lasso (DSTFL), for Cox regression in distributed settings. By employing a minimum spanning tree-based fusion framework, the method can reduce computational and communication burdens, facilitating scalability to large datasets. Additionally, we develop an efficient alternating direction method of multipliers algorithm for the optimization with privacy protection. We establish large-sample properties and clustering consistency for the proposed estimator. Simulation studies demonstrate that DSTFL improves computational efficiency, clustering performance, and robustness compared to existing methods. An application to Surveillance, Epidemiology, and End Results gastric cancer data illustrates how DSTFL identifies geographically structured survival heterogeneity and heterogeneous covariate effects across regions.

PMID:42166192 | DOI:10.1093/biomtc/ujag091

Biometrics. 2026 Apr 9;82(2):ujag089. doi: 10.1093/biomtc/ujag089.

ABSTRACT

Mixed membership models are frequently utilized to capture complex individual heterogeneity in multivariate and longitudinal data. A key aspect of mixed membership modeling involves determining the number of extreme profiles (classes), a task traditionally managed through inefficient criterion-based methods. This task is particularly challenging when the predictors within the models are latent and derived from multiple observed variables using exploratory factor analysis. In this paper, we consider an innovative mixed membership latent variable model, which consists of an exploratory factor model to identify latent factors and a mixed membership model with latent predictors. We develop an efficient approach that integrates parameter estimation and model selection for the number of factors, extreme profiles, and the structure of the factor loading matrix. Our approach comprises a modified stochastic search item selection algorithm to automatically determine the number of latent factors and their associated manifest variables and a Bayesian penalized method to select the number of extreme profiles. We validate our methodology through extensive simulation studies, demonstrating its accuracy and efficiency in both parameter estimation and model selection. Applying this method to data from the Parkinson’s Progression Markers Initiative, we identify clinically important latent traits and distinct disease profiles. The results underscore our model’s enhanced ability to depict the intricate individual heterogeneity present in Parkinson’s disease patients.

PMID:42166191 | DOI:10.1093/biomtc/ujag089

Biometrics. 2026 Apr 9;82(2):ujag080. doi: 10.1093/biomtc/ujag080.

ABSTRACT

Longitudinal biomarker data and health outcomes are routinely collected in many studies to assess how biomarker trajectories predict health outcomes. Existing methods primarily focus on mean biomarker profiles, treating variability as a nuisance. However, excess variability may indicate system dysregulations that may be associated with poor outcomes. In this paper, we address the long-standing problem of using variability information of multiple longitudinal biomarkers in time-to-event analyses by formulating and studying a Bayesian joint model. We first model multiple longitudinal biomarkers, some of which are subject to limit-of-detection censoring. We then model the survival times by incorporating random effects and variances from the longitudinal component as predictors through threshold regression that admits nonproportional hazards. We demonstrate the operating characteristics of the proposed joint model through simulations and apply it to data from the Study of Women’s Health Across the Nation to investigate the impact of the mean and variability of follicle-stimulating hormone and anti-Müllerian hormone on age at the final menstrual period.

PMID:42166190 | DOI:10.1093/biomtc/ujag080

Biometrics. 2026 Apr 9;82(2):ujag078. doi: 10.1093/biomtc/ujag078.

ABSTRACT

A clustered adaptive intervention (cAI) is a prespecified sequence of decision rules that guides practitioners on how best-and based on which measures-to tailor cluster-level intervention to improve outcomes at the level of individuals within the clusters. A clustered sequential multiple assignment randomized trial (cSMART) is a type of trial that is used to inform the empirical development of a cAI. A common analytic goal in a cSMART focuses on assessing causal effect moderation by candidate tailoring variables. We introduce a clustered Q-learning framework with the M-out-of-N cluster bootstrap using data from a cSMART to evaluate whether a set of candidate tailoring variables may be useful in defining an optimal cAI. This approach could construct confidence intervals (CIs) with near-nominal coverage to assess parameters indexing the causal effect moderation function. Specifically, it allows reliable inferences concerning the utility of candidate tailoring variables in constructing a cAI that maximizes a mean end-of-study outcome even when “non-regularity,” a well-known challenge, exists. Simulations demonstrate the numerical performance of the proposed method across varying non-regularity conditions and investigate the impact of varying numbers of clusters and intra-cluster correlation coefficients on CI coverage. Methods are applied on the ADEPT dataset to inform the construction of a clinic-level cAI for improving evidence-based practice in treating mood disorders.

PMID:42166189 | DOI:10.1093/biomtc/ujag078

Biometrics. 2026 Apr 9;82(2):ujag081. doi: 10.1093/biomtc/ujag081.

ABSTRACT

A dynamic treatment regime is a sequence of medical decisions that adapts to the evolving clinical status of a patient over time. To facilitate personalized care, it is crucial to assess the probability of each available treatment option being optimal for a specific patient, while also identifying the key prognostic factors that determine the optimal sequence of treatments. This task has become increasingly challenging due to the growing number of individual prognostic factors typically available. In response to these challenges, we propose a Bayesian model for optimizing dynamic treatment regimes that addresses the uncertainty in identifying optimal decision sequences and incorporates dimensionality reduction to manage high-dimensional individual covariates. The first task is achieved through a suitable augmentation of the model to handle counterfactual variables. For the second, we introduce a novel class of spike-and-slab priors for the multi-stage selection of significant factors, to favor the sharing of information across stages. The effectiveness of the proposed approach is demonstrated through an extensive simulation study and illustrated using clinical trial data on severe acute arterial hypertension.

PMID:42166188 | DOI:10.1093/biomtc/ujag081

Biometrics. 2026 Apr 9;82(2):ujag088. doi: 10.1093/biomtc/ujag088.

ABSTRACT

Multistate models offer an appealing framework for studying the onset and progression of chronic diseases in large cohort studies. Such studies often involve the collection and storage of biospecimens at an initial assessment, and intermittent observation of the disease process at future assessment times. We consider the design of two-phase biomarker studies in such settings where budgetary constraints prohibit assaying all biospecimens. A subsample of individuals is instead chosen to have their biospecimens assayed to facilitate examination of the association between a biomarker of interest and the disease process. Analyses based on likelihood, conditional likelihood, and estimating functions are considered, with the efficiency gains from various subsampling strategies investigated. Pseudo-score residual-dependent sampling strategies are shown to yield highly efficient maximum likelihood estimates of biomarker effects on disease progression. This sampling strategy along with competing methods are empirically studied and applied to a motivating study of the relationship between the HLA-B27 marker and joint damage in patients with psoriatic arthritis.

PMID:42166187 | DOI:10.1093/biomtc/ujag088

Biometrics. 2026 Apr 9;82(2):ujag053. doi: 10.1093/biomtc/ujag053.

ABSTRACT

As standards of care advance, patients are living longer and once-fatal diseases are becoming manageable. Clinical trials increasingly focus on reducing disease burden, which can be quantified by the timing and occurrence of multiple non-fatal clinical events. Most existing methods for the analysis of multiple event-time data require stringent modeling assumptions that can be difficult to verify empirically, leading to treatment efficacy estimates that forego interpretability when the underlying assumptions are not met. Moreover, many methods do not appropriately account for informative terminal events, such as premature treatment discontinuation or death, which prevent the occurrence of subsequent events. To address these limitations, we derive and validate estimation and inference procedures for the area under the mean cumulative function (AUMCF), an extension of the restricted mean survival time to the multiple event-time setting. The AUMCF is clinically interpretable, properly accounts for terminal competing risks, and can be estimated nonparametrically. To enable covariate adjustment, we also develop an augmentation estimator that provides efficiency at least equaling, and often exceeding, the unadjusted estimator. The utility and interpretability of the AUMCF are illustrated with extensive simulation studies and through an analysis of multiple heart-failure-related endpoints using data from the Beta-Blocker Evaluation of Survival Trial. Our open-source R package MCC makes conducting AUMCF analyses straightforward and accessible.

PMID:42166186 | DOI:10.1093/biomtc/ujag053

JAMA Netw Open. 2026 May 1;9(5):e2614557. doi: 10.1001/jamanetworkopen.2026.14557.

NO ABSTRACT

PMID:42166161 | DOI:10.1001/jamanetworkopen.2026.14557