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Nevin Manimala Statistics

A Note on Ising Network Analysis with Missing Data

Psychometrika. 2024 Jul 6. doi: 10.1007/s11336-024-09985-2. Online ahead of print.

ABSTRACT

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method’s performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

PMID:38971882 | DOI:10.1007/s11336-024-09985-2

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Nevin Manimala Statistics

A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia

Sci Rep. 2024 Jul 6;14(1):15589. doi: 10.1038/s41598-024-66596-8.

ABSTRACT

Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and treatment. The goal of this study is to develop a privacy-preserving federated learning framework for predicting candidemia in ICU patients. This approach aims to enhance the accuracy of antifungal drug prescriptions and patient outcomes. This study involved the creation of four predictive FL models for candidemia using data from ICU patients across three hospitals in China. The models were designed to prioritize patient privacy while aggregating learnings across different sites. A unique ensemble feature selection strategy was implemented, combining the strengths of XGBoost’s feature importance and statistical test p values. This strategy aimed to optimize the selection of relevant features for accurate predictions. The federated learning models demonstrated significant improvements over locally trained models, with a 9% increase in the area under the curve (AUC) and a 24% rise in true positive ratio (TPR). Notably, the FL models excelled in the combined TPR + TNR metric, which is critical for feature selection in candidemia prediction. The ensemble feature selection method proved more efficient than previous approaches, achieving comparable performance. The study successfully developed a set of federated learning models that significantly enhance the prediction of candidemia in ICU patients. By leveraging a novel feature selection method and maintaining patient privacy, the models provide a robust framework for improved clinical decision-making in the treatment of candidemia.

PMID:38971879 | DOI:10.1038/s41598-024-66596-8

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Nevin Manimala Statistics

Stage IA papillary and chromophobe renal cell carcinoma: effectiveness of cryoablation and partial nephrectomy

Insights Imaging. 2024 Jul 6;15(1):171. doi: 10.1186/s13244-024-01749-x.

ABSTRACT

OBJECTIVES: To evaluate the effectiveness of cryoablation compared to partial nephrectomy in patients with stage IA papillary and chromophobe renal cell carcinoma (pRCC; chRCC).

MATERIAL AND METHODS: The 2004-2016 National Cancer Database was queried for adult patients with stage IA pRCC or chRCC treated with cryoablation or partial nephrectomy. Patients receiving systemic therapy or radiotherapy, as well as those with bilateral RCC or prior malignant disease were excluded. Overall survival (OS) was assessed using Kaplan-Meier plots and Cox proportional hazard regression models. Nearest neighbor propensity matching (1:1 cryoablation:partial nephrectomy, stratified for pRCC and chRCC) was used to account for potential confounders.

RESULTS: A total of 11122 stage IA renal cell carcinoma patients were included (pRCC 8030; chRCC 3092). Cryoablation was performed in 607 (5.5%) patients, and partial nephrectomy in 10515 (94.5%) patients. A higher likelihood of cryoablation treatment was observed in older patients with non-private healthcare insurance, as well as in those with smaller diameter low-grade pRCC treated at non-academic centers in specific US geographic regions. After propensity score matching to account for confounders, there was no statistically significant difference in OS comparing cryoablation vs partial nephrectomy in patients with pRCC (HR = 1.3, 95% CI: 0.96-1.75, p = 0.09) and those with chRCC (HR = 1.38, 95% CI: 0.67-2.82, p = 0.38).

CONCLUSION: After accounting for confounders, cryoablation, and partial nephrectomy demonstrated comparable OS in patients with stage IA papillary and chromophobe RCC. Cryoablation is a reasonable treatment alternative to partial nephrectomy for these histological RCC subtypes when radiologically suspected or diagnosed after biopsy.

CRITICAL RELEVANCE STATEMENT: Cryoablation might be considered as an upfront treatment alternative to partial nephrectomy in patients with papillary and chromophobe stage IA renal cell carcinoma, as both treatment approaches yield comparable oncological outcomes.

KEY POINTS: The utilization of cryoablation for stage IA papillary and chromophobe RCC increases. In the National Cancer Database, we found specific patterns of use of cryoablation. Cryoablation and partial nephrectomy demonstrate comparable outcomes after accounting for confounders.

PMID:38971873 | DOI:10.1186/s13244-024-01749-x

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Nevin Manimala Statistics

Age, comorbidity burden and late presentation are significant predictors of hospitalization length and acute respiratory failure in patients with influenza

Sci Rep. 2024 Jul 6;14(1):15563. doi: 10.1038/s41598-024-66550-8.

ABSTRACT

Influenza viruses are responsible for a high number of infections and hospitalizations every year. In this study, we aimed to identify clinical and host-specific factors that influence the duration of hospitalization and the progression to acute respiratory failure (ARF) in influenza. We performed an analysis of data from a prospective active influenza surveillance study that was conducted over five seasons (2018/19 to 2022/23). A total of 1402 patients with influenza were included in the analysis, the majority of which (64.5%) were children (under 18 years), and 9.1% were elderly. At least one chronic condition was present in 29.2% of patients, and 9.9% of patients developed ARF. The median hospital stay was 4 days (IQR: 3, 6 days). The most important predictors of prolonged hospital stay and development of ARF were extremes of age (infants and elderly), presence of chronic diseases, particularly the cumulus of at least 3 chronic diseases, and late presentation to hospital. Among the chronic diseases, chronic obstructive pulmonary disease, cardiovascular disease, cancer, diabetes, obesity, and chronic kidney disease were strongly associated with a longer duration of hospitalization and occurrence of ARF. In this context, interventions aimed at chronic disease management, promoting influenza vaccination, and improving awareness and access to health services may contribute to reducing the impact of influenza not only in Romania but globally. In addition, continued monitoring of the circulation of influenza viruses is essential to limit their spread among vulnerable populations.

PMID:38971866 | DOI:10.1038/s41598-024-66550-8

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Nevin Manimala Statistics

Validation of the Hungarian version of the 6-item turnover intention scale among elderly care workers

Sci Rep. 2024 Jul 6;14(1):15593. doi: 10.1038/s41598-024-66671-0.

ABSTRACT

This research examines the psychometric characteristics and reliability of the 6-item turnover intention scale (TIS-6) by Bothma and Roodt (SA J Hum Resour Manag 11:a507, 2013) on a Hungarian sample. The internal validity of the TIS-6 was assessed using data from 269 Hungarian elderly care institution workers. Confirmatory factor analysis was performed to analyse the structural validity. Convergent and discriminant validity were examined with questions on job characteristics and using the Maslach Burnout Inventory and Effort-Reward Imbalance Scale. IBM SPSS 28.0 software was used for the statistical analysis, and the results were considered significant at p < 0.05. The internal consistency of the questionnaire’s scale proved to be acceptable (α = 0.826). Convergent validity was confirmed by the relationships between the components of the questionnaire and burnout (rs = 0.512; p < 0.001; rs = 0.419; p < 0.001) and workplace stress (rs = 0.565; p < 0.001; rs = 0.310; p < 0.001). There were significant differences between the TIS-6 scores among the groups with different degrees of burnout (p < 0.001), which indicated adequate discriminant validity of the questionnaire. The structural validity of the questionnaire was acceptable, and the scale questions fit well. The Hungarian version of the TIS-6 scale is a valid and reliable tool for assessing turnover intention among elderly care institution workers in Hungary.

PMID:38971853 | DOI:10.1038/s41598-024-66671-0

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Nevin Manimala Statistics

Modeling of scour hole characteristics under turbulent wall jets using machine learning

Sci Rep. 2024 Jul 6;14(1):15567. doi: 10.1038/s41598-024-66291-8.

ABSTRACT

The novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R2 = 0.605), (RMSE = 6.644, R2 = 0.681), (RMSE = 6.386, R2 = 0.727) and (RMSE = 1.754, R2 = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.

PMID:38971824 | DOI:10.1038/s41598-024-66291-8

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Nevin Manimala Statistics

Directional integration and pathway enrichment analysis for multi-omics data

Nat Commun. 2024 Jul 7;15(1):5690. doi: 10.1038/s41467-024-49986-4.

ABSTRACT

Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.

PMID:38971800 | DOI:10.1038/s41467-024-49986-4

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Nevin Manimala Statistics

Faith and vaccination: a scoping review of the relationships between religious beliefs and vaccine hesitancy

BMC Public Health. 2024 Jul 6;24(1):1806. doi: 10.1186/s12889-024-18873-4.

ABSTRACT

BACKGROUND: Throughout history, vaccines have proven effective in addressing and preventing widespread outbreaks, leading to a decrease in the spread and fatality rates of infectious diseases. In a time where vaccine hesitancy poses a significant challenge to public health, it is important to identify the intricate interplay of factors exemplified at the individual and societal levels which influence vaccination behaviours. Through this analysis, we aim to shed new light on the dynamics of vaccine hesitancy among religious groups, contributing to the broader effort to promote vaccine uptake, dispel misunderstandings, and encourage constructive dialogue with these groups.

METHODS: We used the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) using the 20-point checklist to guide this review. The inclusion criteria for our study were that the literature should be in English, concerned with vaccine hesitancy as the focus of study, study the impact religiosity or religious beliefs as either an outcome or control variable, concerning population levels, and be peer-reviewed.

RESULTS: We analysed 14 peer-reviewed articles that included components related to religiosity or religious beliefs and their impact on vaccine hesitancy published until September 2023. All the articles were published in approximately the last decade between 2012 and 2023, with only 4 of the articles published before 2020. Out of the 14 studies included in our review, twelve utilized quantitative methods, while the remaining two employed qualitative approaches. Among the studies included in our analysis, we found various approaches to categorizing religious belief and identity. In most studies when religion is uniformly regarded as the sole determinant of vaccine hesitancy, it consistently emerges as a significant factor in contributing to vaccine hesitancy. All studies in our review reported sociodemographic factors to some degree related to vaccine hesitancy within their sample populations. Our analysis underscored the need for nuanced approaches to addressing vaccine hesitancy among religious groups.

CONCLUSION: Vaccine hesitancy is a complex issue and driven by a myriad of individual and societal factors among which religious beliefs is commonly associated to be a driver of higher levels among populations.

PMID:38971784 | DOI:10.1186/s12889-024-18873-4

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Nevin Manimala Statistics

DNA methylation profile of inflammatory breast cancer and its impact on prognosis and outcome

Clin Epigenetics. 2024 Jul 6;16(1):89. doi: 10.1186/s13148-024-01695-x.

ABSTRACT

BACKGROUND: Inflammatory breast cancer (IBC) is a rare disease characterized by rapid progression, early metastasis, and a high mortality rate.

METHODS: Genome-wide DNA methylation analysis (EPIC BeadChip platform, Illumina) and somatic gene variants (105 cancer-related genes) were performed in 24 IBCs selected from a cohort of 140 cases.

RESULTS: We identified 46,908 DMPs (differentially methylated positions) (66% hypomethylated); CpG islands were predominantly hypermethylated (39.9%). Unsupervised clustering analysis revealed three clusters of DMPs characterized by an enrichment of specific gene mutations and hormone receptor status. The comparison among DNA methylation findings and external datasets (TCGA-BRCA stages III-IV) resulted in 385 shared DMPs mapped in 333 genes (264 hypermethylated). 151 DMPs were associated with 110 genes previously detected as differentially expressed in IBC (GSE45581), and 68 DMPs were negatively correlated with gene expression. We also identified 4369 DMRs (differentially methylated regions) mapped on known genes (2392 hypomethylated). BCAT1, CXCL12, and TBX15 loci were selected and evaluated by bisulfite pyrosequencing in 31 IBC samples. BCAT1 and TBX15 had higher methylation levels in triple-negative compared to non-triple-negative, while CXCL12 had lower methylation levels in triple-negative than non-triple-negative IBC cases. TBX15 methylation level was associated with obesity.

CONCLUSIONS: Our findings revealed a heterogeneous DNA methylation profile with potentially functional DMPs and DMRs. The DNA methylation data provided valuable insights for prognostic stratification and therapy selection to improve patient outcomes.

PMID:38971778 | DOI:10.1186/s13148-024-01695-x

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Nevin Manimala Statistics

Cost-effectiveness of single-pill and separate-pill administration of antihypertensive triple combination therapy: a population-based microsimulation study

BMC Public Health. 2024 Jul 6;24(1):1808. doi: 10.1186/s12889-024-19346-4.

ABSTRACT

BACKGROUND: Single-pill combination (SPC) of three antihypertensive drugs has been shown to improve adherence to therapy compared with free combinations, but little is known about its long-term costs and health consequences. This study aimed to evaluate the lifetime cost-effectiveness profile of a three-drug SPC of an angiotensin-converting enzyme inhibitor, a calcium-channel blocker, and a diuretic vs the corresponding two-pill administration (a two-drug SPC plus a third drug separately) from the Italian payer perspective.

METHODS: A cost-effectiveness analysis was conducted using multi-state semi-Markov modeling and microsimulation. Using the healthcare utilization database of the Lombardy Region (Italy), 30,172 and 65,817 patients aged ≥ 40 years who initiated SPC and two-pill combination, respectively, between 2015 and 2018 were identified. The observation period extended from the date of the first drug dispensation until death, emigration, or December 31, 2019. Disease and cost models were parametrized using the study cohort, and a lifetime microsimulation was applied to project costs and life expectancy for the compared strategies, assigning each of them to each cohort member. Costs and life-years gained were discounted by 3%. Probabilistic sensitivity analysis with 1,000 samples was performed to address parameter uncertainty.

RESULTS: Compared with the two-pill combination, the SPC increased life expectancy by 0.86 years (95% confidence interval [CI] 0.61-1.14), with a mean cost differential of -€12 (95% CI -9,719-8,131), making it the dominant strategy (ICER = -14, 95% CI -€15,871-€7,113). The cost reduction associated with the SPC was primarily driven by savings in hospitalization costs, amounting to €1,850 (95% CI 17-7,813) and €2,027 (95% CI 19-8,603) for patients treated with the SPC and two-pill combination, respectively. Conversely, drug costs were higher for the SPC (€3,848, 95% CI 574-10,640 vs. €3,710, 95% CI 263-11,955). The cost-effectiveness profile did not significantly change according to age, sex, and clinical status.

CONCLUSIONS: The SPC was projected to be cost-effective compared with the two-pill combination at almost all reasonable willingness-to-pay thresholds. As it is currently prescribed to only a few patients, the widespread use of this strategy could result in benefits for both patients and the healthcare system.

PMID:38971775 | DOI:10.1186/s12889-024-19346-4