Category: Statistics

Ann Med. 2025 Dec;57(1):2516694. doi: 10.1080/07853890.2025.2516694. Epub 2025 Jun 10.

ABSTRACT

INTRODUCTION: Post COVID-19 condition is a debilitating illness with over 200 symptoms across 10 organ systems and is presently impacting millions worldwide. The National Institute for Health and Care Excellence recommends a multidisciplinary treatment approach including person-centred self-management strategies, however evidence for specific programs is lacking. The Take Charge intervention is a person-centred, self-management rehabilitation approach that has been effective in recovery after stroke, but not yet tested in post COVID-19 condition.

METHODS & ANALYSIS: A prospective, single-centre, parallel, 2 group, mixed methods, randomized controlled trial with embedded process evaluation of the Take Charge intervention in individuals living with post COVID-19 condition. Participants will be at least 18 years of age, have a confirmed diagnosis of post COVID-19 condition with ongoing symptoms, and be known to a hospital clinic for assessment and treatment of patients with post-acute sequelae of COVID-19. The primary outcomes are the Modified COVID-19 Yorkshire Rehabilitation Scale and the COVID-19 Core Outcome Measure for Recovery. The secondary outcomes include physical and self-report measures, and feasibility measures. Qualitative interviews will also be conducted to understand the clinicians’ and participants’ experiences. Statistical analysis will be performed on an intention-to-treat basis using a multivariate mixed-effect linear regression model.

ETHICS & DISSEMINATION: This study adheres to the Declaration of Helsinki. This study was approved by the Southern Adelaide Clinical Human Research Ethics Committee (approval number: 2022/SSA00695/OFR: 219.22, protocol version 3.3 19 February 2024). The results will be disseminated in peer-reviewed journals, conference presentations, and media.

PMID:40492405 | DOI:10.1080/07853890.2025.2516694

J Biopharm Stat. 2025 Jun 10:1-17. doi: 10.1080/10543406.2025.2489287. Online ahead of print.

ABSTRACT

Product ranking according to pre-specified criteria is essential for developing new technologies, allowing identification of more preferable candidates for further development. Such ranking often builds on the results of a network meta-analysis, where the relative or absolute performances of the various products are synthesized across multiple clinical studies, each of which considered only a subset of the products. Ranking involving both a negative and a positive reference enables the scientist to directly compare tested products against known benchmarks. Here, more preferable candidates are those products that approach the positive reference while remaining distant from the negative reference. We provide a new metric to quantify this multivariate distance following Bayesian meta-analysis. Our method does not simply rely on point estimates to perform the comparisons, but also accounts for their uncertainties via their posterior distributions. For each product, posterior probabilities of being comparable to the positive reference are computed, and subsequently penalized by the posterior probability of performing worse than the negative reference. Each product is then compared to a hypothetical product about which we have no knowledge, as captured by a uniform distribution. The result is a prospective metric that is directly interpretable as the improvement of any product beyond this state of ignorance. We illustrate our approach using a case study, in which the goal is to rank 16 antiperspirant products. Here, the FDA-recommended summary statistic (a measure of the relative sweat reduction between each product and no treatment) intrinsically features both positive and negative references. We then offer a brief simulation study to check our metric’s performance in less complex, idealized settings where the true ranking is known. Our results indicate that our Bayesian approach is a novel and useful addition to the statistical ranking toolkit.

PMID:40492403 | DOI:10.1080/10543406.2025.2489287

J Biopharm Stat. 2025 Jun 10:1-11. doi: 10.1080/10543406.2025.2512985. Online ahead of print.

ABSTRACT

BACKGROUND: Time to Benefit (TTB) is a critical metric in clinical practice, reflecting the duration required to achieve therapeutic goals post-treatment. Traditionally, TTB estimation has relied on Bayesian Weibull regression, which, despite its merits, can be computationally intensive. To address this, we propose and evaluate Frequentist methods as efficient alternatives to approximate Bayesian TTB estimation.

METHODS: We evaluated three Frequentist methods, parametric delta, Monte Carlo, and nonparametric bootstrap, for TTB estimation, comparing their performance with the Bayesian approach.

RESULTS: Extensive simulations demonstrated that the proposed Frequentist methods outperformed the Bayesian method in efficiency. Real-world data applications further validated these findings, with the Monte Carlo (MC) method exhibiting significantly faster computational speed compared to the nonparametric bootstrap, while the Bayesian method was the least efficient.

CONCLUSIONS: The proposed Frequentist methods offer significant advantages to approximate the Bayesian approach for TTB estimation, particularly in efficiency and practicality. The Monte Carlo method, with its median point estimate and percentile confidence intervals, is the recommended choice for its balance of efficacy and expedience.

PMID:40492388 | DOI:10.1080/10543406.2025.2512985

Acta Anaesthesiol Scand. 2025 Jul;69(6):e70073. doi: 10.1111/aas.70073.

ABSTRACT

INTRODUCTION: Cardiac surgery in infants often triggers a severe inflammatory response. The role of biomarkers in predicting clinical outcomes in this group of patients has been debated in the literature. This study aimed to investigate the predictive value of 20 inflammatory biomarkers, in combination with clinical data, for acute kidney injury, ventilator support duration, and inotropic score following infant cardiac surgery by developing and comparing three models: Clinical-Data-Only, Biomarker-Only, and Combined.

METHODS: This secondary analysis of the MiLe-1 study included infants undergoing surgery with cardiopulmonary bypass. Biomarkers were measured before and after CPB. Using BIC-guided logistic regression, we developed and compared three multivariable models-Clinical-Data-Only, Biomarker-Only, and Combined-for each outcome. Model performance was assessed using c-statistics and p-contrast tests.

RESULTS: Regarding AKI risk prediction, the c-statistics for Biomarker-Only, Clinical-Data-Only, and Combined Model were 0.79, 0.60, and 0.78 respectively. The difference in performance between the Combined and Clinical-Data-Only Models was statistically significant (p < 0.001). Concerning ventilator support time prediction, the c-statistics were 0.80, 0.72, and 0.77 for the models respectively (p-contrast = 0.10). As for inotropic score prediction, the c-statistics were 0.83, 0.77, and 0.85 for the models (p-contrast = 0.007).

CONCLUSION: Inflammatory biomarkers may enhance risk stratification for postoperative outcomes in infant cardiac surgery. However, given the exploratory nature of this study, further validation in larger and more diverse cohorts is needed.

PMID:40492379 | DOI:10.1111/aas.70073

J Invest Surg. 2025 Dec;38(1):2515054. doi: 10.1080/08941939.2025.2515054. Epub 2025 Jun 10.

ABSTRACT

BACKGROUND: This study aimed to compare the operative outcomes of transvaginal natural orifice transluminal endoscopic surgery (vNOTES) and total laparoscopic hysterectomy (TLH). We also aimed to determine the feasibility of performing vNOTES hysterectomy in primary hospitals.

METHODS: This prospective cohort study enrolled 54 patients with indications for hysterectomy related to benign uterine disease without prolapse, between September 1, 2020, and November 30, 2024. The patients were categorized into two groups: vNOTES hysterectomy and TLH (n = 27 each). Surgical outcomes, including operative time, blood loss, recovery parameters, and complications, were assessed.

RESULTS: Preoperative baseline characteristics were comparable between the two groups. The vNOTES group had a longer mean operative time (187.6 vs. 154.4 min, p < 0.05) and greater median blood loss (100 vs. 30 mL, p < 0.05) compared to the TLH group. However, there were no significant differences in conversion rates, uterine weights, complications, 24-h pain scores, hospital stay, costs, or readmission rates. The vNOTES group demonstrated shorter times to postoperative ambulation, earlier return of bowel function (anal exhaust), and reduced urinary catheter insertion duration (p < 0.05).

CONCLUSION: vNOTES hysterectomy is feasible in primary hospitals, with surgical outcomes comparable to those of TLH. Patients who underwent vNOTES experienced faster recovery, indicating that it serves as a potential minimally invasive alternative to TLH. However, the small sample size warrants further studies to validate these findings.

PMID:40492370 | DOI:10.1080/08941939.2025.2515054

Acta Odontol Scand. 2025 Jun 10;84:318-331. doi: 10.2340/aos.v84.43758.

ABSTRACT

OBJECTIVE: This study aims to systematically evaluate the effects of different oral barrier membranes on bone regeneration, focusing on their clinical efficacy and safety during dental implant procedures.

METHODS: A comprehensive search was conducted in PubMed, EMBASE, ScienceDirect, Cochrane Library, CNKI, VIP, and CBM databases for case-control and cohort studies published between January 2002 and March 2025. Two independent researchers screened and extracted data, and statistical analysis was performed using RevMan 5.3. The study was registered in PROSPERO (CRD492390).

RESULTS: A total of 11 clinical controlled and cohort studies with 1,003 patients were included. The absorbable membrane group demonstrated a significantly higher success rate (p < 0.05), greater bone graft thickness (p < 0.05), and fewer adverse reactions (p < 0.05). Meta-analysis showed no significant difference in osseointegration, total mineralised tissue, and non-mineralised tissue (p > 0.05).

CONCLUSION: Absorbable oral barrier membranes exhibit superior safety and efficacy profiles, making them a preferred choice for guided bone regeneration. However, further studies with higher methodological quality and longer follow-up durations are required.

PMID:40492364 | DOI:10.2340/aos.v84.43758

Int J Circumpolar Health. 2025 Dec;84(1):2516872. doi: 10.1080/22423982.2025.2516872. Epub 2025 Jun 10.

ABSTRACT

The Northwest Territories, Canada, has high rates of alcohol- and drug-related hospitalisations and deaths. There is considerable debate over how to provide substance use recovery services in this region, due to its small, culturally diverse population. The aim of this study was to examine demographic differences in ethnicity, gender and sex for individuals in the barriers to accessing services, supports to stay in recovery, and reasons they struggled to stay in recovery. A total of 439 respondents completed online and paper-based surveys on their experiences accessing recovery services in the Northwest Territories. A mixed methods approach was applied, in which Fisher’s exact test was applied to test for statistically significant demographic differences in quantitative responses, and themed analysis was performed using deductive coding using written survey responses. Several statistically significant demographic differences were identified in barriers to services, supports to recovery, and barriers to staying in recovery. Cultural incongruity, and the importance of social support to substance use disorder recovery, were identified as key themes that emerged in qualitative analysis. There is a need for community-based, culturally safe, and family-inclusive holistic supports at the community level to address substance use issues in the NT, including more informal confidential supports and efforts to reduce stigma and normalise and celebrate recovery.

PMID:40492355 | DOI:10.1080/22423982.2025.2516872

Genes Brain Behav. 2025 Jun;24(3):e70025. doi: 10.1111/gbb.70025.

ABSTRACT

Posttraumatic stress disorder (PTSD) may develop following trauma exposure; however, not all trauma-exposed individuals develop PTSD, suggesting the presence of susceptibility and resilience factors. The gut microbiome and host genome, which are interconnected, have been implicated in the aetiology of PTSD. However, their interaction has yet to be investigated in a South African population. Using genome-wide genotype data and 16S rRNA (V4) gene amplicon sequencing data from 53 trauma-exposed controls and 74 PTSD cases, we observed no significant association between the host genome and summed abundance of Mitsuokella, Odoribacter, Catenibacterium and Olsenella, previously reported as associated with PTSD status in this cohort. However, PROM2 rs2278067 T-allele was significantly positively associated with the summed relative abundance of these genera, but only in individuals with PTSD and not trauma-exposed controls (p < 0.014). Polygenic risk scores generated using genome-wide association study summary statistics from the PGC-PTSD Overall Freeze 2 were not predictive of gut microbial composition in this cohort. These preliminary results suggest a potential role for the interaction between genetic variation and gut microbial composition in the context of PTSD, underscoring the need for further investigation.

PMID:40492293 | DOI:10.1111/gbb.70025

Congenit Anom (Kyoto). 2025 Jan-Dec;65(1):e70013. doi: 10.1111/cga.70013.

ABSTRACT

The Fukushima Daiichi Nuclear Disaster (FDND) occurred in 2011, which occurred after the Great East Japan Earthquake. However, how the incidence of radiation-induced malformations in Fukushima has been affected by FDND remains to be elucidated. To address this, we analyzed birth data from Fukushima and other areas in Japan from the International Clearinghouse for Birth Defects Surveillance and Research Japan Center, including information on birth defects between January 2010 and December 2022. Among the registered birth defects, microcephaly, microphthalmia, and neural tube defects were classified as radiation-induced malformations. Our study included 90 433 births in Fukushima, accounting for 52.6% of all births. Among these, birth defects were observed in 1376 (1.52%) births, of which 28 (0.031%) were diagnosed with radiation-induced malformations. With regard to other areas in Japan, 1 323 391 births, which accounted for 10.9% of all births, were registered; births with birth defects and radiation-induced malformations were observed in 37 490 (3.67%) and 889 (0.067%), respectively. Because sampling bias was suspected, we compared the rates of radiation-induced malformations in Fukushima and other areas in Japan by adjusting the incidence in Fukushima with the incidences of ventricular septal defects in both areas. However, there was no statistically significant difference between them. Our results, which covered the largest number of births in Fukushima, did not find a significant increase in the incidence of radiation-induced malformations in Fukushima since FDND.

PMID:40492283 | DOI:10.1111/cga.70013

ArXiv [Preprint]. 2025 May 30:arXiv:2504.00459v2.

ABSTRACT

Multivariate phase relationships are important to characterize and understand numerous physical, biological, and chemical systems, from electromagnetic waves to neural oscillations. These systems exhibit complex spatiotemporal dynamics and intricate interdependencies among their constituent elements. While classical models of multivariate phase relationships, such as the wave equation and Kuramoto model, give theoretical models to describe phenomena, the development of statistical tools for hypothesis testing and inference for multivariate phase relationships in complex systems remains limited. This paper introduces a novel probabilistic modeling framework to characterize multivariate phase relationships, with wave-like phenomena serving as a key example. This approach describes spatial patterns and interactions between oscillators through a pairwise exponential family distribution. Building upon the literature of graphical model inference, including methods like Ising models, graphical lasso, and interaction screening, this work bridges the gap between classical wave dynamics and modern statistical approaches. Efficient inference methods are introduced, leveraging the Chow-Liu algorithm for directed tree approximations and interaction screening for general graphical models. Simulated experiments demonstrate the utility of these methods for uncovering wave properties and sparse interaction structures, highlighting their applicability to diverse scientific domains. This framework establishes a new paradigm for statistical modeling of multivariate phase relationships, providing a powerful toolset for exploring the complexity of these systems.

PMID:40492251 | PMC:PMC12148093