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Results for “Bayesian analysis”

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PubMed2000

A Bayesian framework for the analysis of cospeciation.

Information on the history of cospeciation and host switching for a group of host and parasite species is contained in the DNA sequences sampled from each. Here, we develop a Bayesian framework for the analysis of cospeciation. We suggest a simple model of host switching by a parasite on a host phylogeny in which host switching events are assumed to occur at a constant rate over the entire evolutionary history of associated hosts and parasites. The posterior probability density of the parameters of the model of host switching are evaluated numerically using Markov chain Monte Carlo. In particular, the method generates the probability density of the number of host switches and of the host switching rate. Moreover, the method provides information on the probability that an event of host switching is associated with a particular pair of branches. A Bayesian approach has several advantages over other methods for the analysis of cospeciation. In particular, it does not assume that the host or parasite phylogenies are known without error; many alternative phylogenies are sampled in proportion to their probability of being correct.

Animals↗

PubMed2004

Development of a clinical pathways analysis system with adaptive Bayesian nets and data mining techniques.

The use and development of software in the medical field offers tremendous opportunities for making health care delivery more efficient, more effective, and less error-prone. We discuss and explore the use of clinical pathways analysis with Adaptive Bayesian Networks and Data Mining Techniques to perform such analyses. The computation of "lift" (a measure of completed pathways improvement potential) leads us to optimism regarding the potential for this approach.

Bayes Theorem↗

PubMedDate not supplied

Bayesian small area cluster analysis of neural tube defects in Newfoundland.

BACKGROUND: The incidence of neural tube defects (NTDs) is declining worldwide due to the implementation of folic acid supplementation programs. Such a program was implemented over 1996-97 in Newfoundland and Labrador, Canada. The geographical distribution of birth incidence was studied prior to and after the implementation of the program to identify regions of residual high incidence. Excess residual cases may potentially be due to genetic causes or incomplete supplementation program implementation. METHODS: Maternal place of residence for all provincial live birth and stillbirth notifications, provincial maternal-fetal medicine referrals, provincial rehabilitation referrals, and all provincial hospitals with NTDs or terminations for NTDs was obtained from 1975 to 2002 for near complete case ascertainment. Bayesian small area analysis was separately performed on cases from 1975-1996 and 1997-2002. The two time periods were compared. RESULTS: Birth incidence of NTDs was noted to decline after 1996, from 5.54/1000 live births to 1.08/1000 live births. 592 cases were found from 1975-1996 and 34 cases from 1997-2002. Relative risk of birth incidence was 0.93-1.18 (95% CI) for 1975-1996 and 0.97-1.02 for 1997-2002 after Bayesian smoothing. One region had an excess of residual cases greater than 34%. CONCLUSIONS: The implications of this observation to the management of the public health initiative imply that overall response to the decrease in cases tends to be uniform across the province, with potentially one area of interest where extra efforts may be devoted.

Bayes Theorem↗

PubMed2005

Is anoxic depolarisation associated with an ADC threshold? A Markov chain Monte Carlo analysis.

A Bayesian nonlinear hierarchical random coefficients model was used in a reanalysis of a previously published longitudinal study of the extracellular direct current (DC)-potential and apparent diffusion coefficient (ADC) responses to focal ischaemia. The main purpose was to examine the data for evidence of an ADC threshold for anoxic depolarisation. A Markov chain Monte Carlo simulation approach was adopted. The Metropolis algorithm was used to generate three parallel Markov chains and thus obtain a sampled posterior probability distribution for each of the DC-potential and ADC model parameters, together with a number of derived parameters. The latter were used in a subsequent threshold analysis. The analysis provided no evidence indicating a consistent and reproducible ADC threshold for anoxic depolarisation.

Algorithms↗

PubMed2026

Comparative effectiveness of game-based learning modalities in nursing and medical education: a systematic review and Bayesian network meta-analysis.

BACKGROUND: Game-based learning (GBL) is increasingly used in healthcare education, but educators must choose among diverse modalities (e.g., quiz platforms, apps, serious games and metaverse environments). Comparative evidence on which modalities perform best across learning domains (knowledge, attitudes, and practice) remains limited. AIM: To compare the effects of distinct GBL modalities on knowledge, attitudes, and practice outcomes in nursing and medical education and to explore whether comparative effects differ by learner group (pre-licensure students and in-service professionals). DESIGN: PRISMA-NMA-aligned systematic review and Bayesian network meta-analysis. METHODS: We searched eight databases and trial registries through September 2, 2024, for randomized controlled trials comparing GBL with traditional teaching (TT). Outcomes were transformed to a 0-100 scale and analysed as change from baseline in Bayesian consistency models; random-effects models were selected using deviance information criterion (DIC). Risk of bias was assessed using RoB 2. We report mean differences (MDs) with 95% credible intervals (CrIs) versus TT, ranking probabilities, and subgroup NMAs by learner group. RESULTS: Thirty-one RCTs (n = 3439) were included; 15 contributed complete data to the network. Risk of bias was low in 15 trials and raised some concerns in 16. The network was modest for knowledge (11 trials) and sparse for attitudes (3) and practice (4). Compared with TT, metaverse-based learning showed improved attitudes (MD 15; 95% CrI 12 to 18), based on a single trial. For knowledge and practice, Kahoot-based quizzes (MD 9.1; 95% CrI -8.9 to 27) and app-based learning (MD 4.6; 95% CrI -4.4 to 14) had the highest estimated mean improvements, but credible intervals were wide and included the null for most comparisons. Subgroup rankings differed by learner group, but several comparisons were imprecise and uncertainty was substantial, particularly in sparse networks. CONCLUSIONS: GBL modalities may improve learning outcomes compared with TT, but relative effects appear domain-specific and the certainty of rankings is limited by sparse evidence and imprecision. Future trials should prioritise head-to-head comparisons, robust outcome measurement, and longer-term retention and transfer outcomes in both student and in-service populations.

Humans↗

PubMedDate not supplied

Bayesian model based clustering analysis: application to a molecular dynamics trajectory of the HIV-1 integrase catalytic core.

This work describes the application of a Bayesian method for clustering protein conformations sampled during a molecular dynamics simulation of the HIV-1 integrase catalytic core. A clustering analysis is carried out under the assumption of normal distribution without fixing the number of clusters in advance. Some performance measures, such as posterior probability and class cross entropy, are used to determine the most probable set of clusters. The Bayesian clustering method results in meaningful groups identifying transitions between conformational ensembles. The dihedral angles involved in such transitions are also examined in detail. The conformations in high dimensional space are projected into 3D space employing a multidimensional scaling technique to provide a visual inspection.

Algorithms↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2004

Aligned 18S and insect phylogeny.

The nuclear small subunit rRNA (18S) has played a dominant role in the estimation of relationships among insect orders from molecular data. In previous studies, 18S sequences have been aligned by unadjusted automated approaches (computer alignments that are not manually readjusted), most recently with direct optimization (simultaneous alignment and tree building using a program called "POY"). Parsimony has been the principal optimality criterion. Given the problems associated with the alignment of rRNA, and the recent availability of the doublet model for the analysis of covarying sites using Bayesian MCMC analysis, a different approach is called for in the analysis of these data. In this paper, nucleotide sequence data from the 18S small subunit rRNA gene of insects are aligned manually with reference to secondary structure, and analyzed under Bayesian phylogenetic methods with both GTR+I+G and doublet models in MrBayes. A credible phylogeny of Insecta is recovered that is independent of the morphological data and (unlike many other analyses of 18S in insects) not contradictory to traditional ideas of insect ordinal relationships based on morphology. Hexapoda, including Collembola, are monophyletic. Paraneoptera are the sister taxon to a monophyletic Holometabola but weakly supported. Ephemeroptera are supported as the sister taxon of Neoptera, and this result is interpreted with respect to the evolution of direct sperm transfer and the evolution of flight. Many other relationships are well-supported but several taxa remain problematic, e.g., there is virtually no support for relationships among orthopteroid orders. A website is made available that provides aligned 18S data in formats that include structural symbols and Nexus formats.

Animals↗

PubMed2004

Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects.

PURPOSE: To determine whether an unsupervised machine learning classifier can identify patterns of visual field loss in standard visual fields consistent with typical patterns learned by decades of human experience. METHODS: Standard perimetry thresholds for 52 locations plus age from one eye of each of 156 patients with glaucomatous optic neuropathy (GON) and 189 eyes of healthy subjects were clustered with an unsupervised machine classifier, variational Bayesian mixture of factor analysis (vbMFA). RESULTS: The vbMFA formed five distinct clusters. Cluster 5 held 186 of 189 fields from normal eyes plus 46 from eyes with GON. These fields were then judged within normal limits by several traditional methods. Each of the other four clusters could be described by the pattern of loss found within it. Cluster 1 (71 GON + 3 normal optic discs) included early, localized defects. A purely diffuse component was rare. Cluster 2 (26 GON) exhibited primarily deep superior hemifield defects, and cluster 3 (10 GON) held deep inferior hemifield defects only or in combination with lesser superior field defects. Cluster 4 (6 GON) showed deep defects in both hemifields. In other words, visual fields within a given cluster had similar patterns of loss that differed from the predominant pattern found in other clusters. The classifier separated the data based solely on the patterns of loss within the fields, without being guided by the diagnosis, placing 98.4% of the healthy eyes within the same cluster and spreading 70.5% of the eyes with GON across the other four clusters, in good agreement with a glaucoma expert and pattern standard deviation. CONCLUSIONS: Without training-based diagnosis (unsupervised learning), the vbMFA identified four important patterns of field loss in eyes with GON in a manner consistent with years of clinical experience.

Algorithms↗

PubMed1987

Estimates of the population pharmacokinetic parameters and performance of Bayesian feedback: a sensitivity analysis.

We investigated the influence of bias in the estimates of the population pharmacokinetic parameters on the performance of Bayesian feedback in achieving a desired drug serum concentration. Three specific cases were considered (i) steady-state case, (ii) lidocaine example, and (iii) mexiletine example. Whereas in the first case both the feedback and the desired concentration represented steady-state values, in the lidocaine and mexiletine examples the feedback concentration was assumed to be sampled shortly after starting therapy. RMSE was used as a measure of predictive performance. For the simple steady-state case the relationship between RMSE and bias in the parameter estimates describing the prior distribution could be derived analytically. Monte Carlo simulations were used to explore the two non-steady-state situations. In general, the performance of Bayesian feedback to predict serum concentrations was relatively insensitive to bad population parameter estimates. However, large changes in RMSE could be observed with small changes in the true variance component parameters in particular in the intraindividual residual variance, sigma 2 epsilon, indicating that the prediction interval, in contrast to point prediction, is sensitive to bias in the estimates of the population parameters.

Bayes Theorem↗

PubMed1989

Prospective use of optimal sampling theory: steady-state ciprofloxacin pharmacokinetics in critically ill trauma patients.

We examined the use of optimal sampling theory to determine a sparse sampling design to estimate pharmacokinetic parameters of ciprofloxacin in patients who had sustained trauma. Two serum sampling strategies, consisting of six sampling times each, were derived on the basis of the patient's renal function (patients with creatinine clearance greater than or equal to 6 L/hr/1.73 m2 and patients with creatinine clearances less than 6 L/hr/1.73 m2). Two additional serum samples were obtained for other aspects to the study. A timed urine collection was also obtained. Pharmacokinetic parameter estimates were determined by comodeling the serum and urine data with a three-compartment open model (parameterized as microconstants) with a bayesian algorithm and by noncompartmental analysis. Bayesian-derived parameter estimates were total body clearance of drug from plasma, 29.8 L/hr/1.73 m2; renal clearance, 17.0 L/hr/1.73 m2; and nonrenal clearance, 12.7 L/hr/1.73 m2 and were not significantly different from noncompartmentally derived parameters (p = 0.80, p = 0.65 and p = 0.333, respectively). The study demonstrates the use of optimal sampling theory to determine an informative yet relatively sparse sampling strategy for a drug with a complex pharmacokinetic model.

Adult↗

PubMed2005

Bayesian solutions and performance analysis in bioelectric inverse problems.

In bioelectric inverse problems, one seeks to recover bioelectric sources from remote measurements using a mathematical model that relates the sources to the measurements. Due to attenuation and spatial smoothing in the medium between the sources and the measurements, bioelectric inverse problems are generally ill-posed. Bayesian methodology has received increasing attention recently to combat this ill-posedness, since it offers a general formulation of regularization constraints and additionally provides statistical performance analysis tools. These tools include the estimation error covariance and the marginal probability density of the measurements (known as the "evidence") that allow one to predictively quantify and compare experimental designs. These performance analysis tools have been previously applied in inverse electroencephalography and magnetoencephalography, but only in relatively simple scenarios. The main motivation here was to extend the utility of Bayesian estimation techniques and performance analysis tools in bioelectric inverse problems, with a particular focus on electrocardiography. In a simulation study we first investigated whether Bayesian error covariance, computed without knowledge of the true sources and based on instead statistical assumptions, accurately predicted the actual reconstruction error. Our study showed that error variance was a reasonably reliable qualitative and quantitative predictor of estimation performance even when there was error in the prior model. We also examined whether the evidence statistic accurately predicted relative estimation performance when distinct priors were used. In a simple scenario our results support the hypothesis that the prior model that maximizes the evidence is a good choice for inverse reconstructions.

Algorithms↗

PubMed2026

Comparative Efficacy of Non-opioid Analgesic Drugs for Chronic Cancer Pain: A Bayesian Network Meta-analysis.

PURPOSE: While opioids remain the primary pharmacological intervention for cancer pain management, their clinical utility is frequently compromised by dose-limiting toxicities. This study aimed to determine the comparative efficacy, opioid-sparing potential, and clinical hierarchy of non-opioid adjuvant drug classes. The study was structured around the PICO framework to evaluate the pharmacological strategies currently utilized in multimodal clinical oncology. METHODS: A systematic search of electronic databases (PubMed, Embase, Cochrane) was conducted for randomized controlled trials (RCTs) published between 2000 and 2025. The primary outcome was global analgesic efficacy (standardized mean difference [SMD]), while secondary outcomes included the opioid-sparing effect, defined as the percentage reduction in morphine equivalent daily dose (MEDD) and the incidence of treatment-emergent adverse events (Harms). A Bayesian network meta-analysis (NMA) was performed to rank treatments using SUCRA values. The methodological quality was assessed using the Cochrane Risk of Bias (RoB 2.0) tool. RESULTS: Twenty-three RCTs (n = 1845) met the inclusion criteria. Nonsteroidal anti-inflammatory drugs (NSAIDs) (-1.10) and anticonvulsants (-1.06) demonstrated the most robust analgesic effects. The SUCRA ranking confirmed a clear hierarchy, with the combination of anticonvulsants and antidepressants showing the highest probability of efficacy. A significant opioid-sparing effect was observed for gabapentinoids and ketamine, facilitating MEDD reduction. While serious adverse events were rare, minor harms (somnolence, dizziness) were more frequent in the most effective classes. CONCLUSION: Our NMA provides a robust evidence base for a "Clinical Tier" system, ranking adjuvants by their balance of efficacy and safety. These findings support the early integration of Tier I agents (anticonvulsants and NSAIDs) to optimize pain control and reduce opioid-related toxicities in chronic cancer pain management.

Humans↗

PubMed2025

Comparative Efficacy of Janus Kinase Inhibitors Indicated for Severe Alopecia Areata: A Bayesian Network Meta-Analysis and Matching-Adjusted Indirect Comparison.

Systemic Janus kinase inhibitors (JAKIs) have markedly advanced the therapeutic landscape for alopecia areata (AA). Although baricitinib and ritlecitinib are approved in the United States (US) and Europe, and deuruxolitinib in the US for severe AA, the lack of head-to-head randomized controlled trials (RCTs) limits evidence-based prescribing decisions. Moreover, prior meta-analyses excluded data on certain oral JAKIs or incorporated findings from agents and dosing regimens that were abandoned, investigational, clinically ineffective, or associated with unacceptable safety profiles. To compare the efficacy of oral JAKIs, limited to FDA, EMA, or MHRA approved drugs and doses-baricitinib (2 and 4 mg QD), ritlecitinib (50 mg QD), and deuruxolitinib (8 mg BID)-for severe AA, using advanced indirect comparison methodologies. A systematic review was performed following PRISMA 2020 guidelines (CRD420251116775). Bayesian network meta-analysis (NMA) synthesized data from RCTs reporting Week 24 outcomes on Severity of Alopecia Tool (SALT) ≤ 10 and SALT ≤ 20 thresholds. Multilevel network meta-regression (ML-NMR) evaluated heterogeneity and adjusted for baseline imbalances. Additionally, unanchored matching-adjusted indirect comparisons (MAIC) were conducted using individual patient-level data from THRIVE trials. Surface under the cumulative ranking (SUCRA) values were calculated to rank treatments. Seven RCTs (n = 4560 participants) were included. Deuruxolitinib 8 mg significantly outperformed baricitinib 2 and 4 mg on both SALT endpoints. Differences with ritlecitinib 50 mg were directionally favorable for deuruxolitinib but not statistically significant in NMA and ML-NMR models. MAICs confirmed superior odds for deuruxolitinib versus baricitinib 2 mg (OR = 71.55) and ritlecitinib (OR = 18.27) for SALT ≤ 20. SUCRA rankings also consistently favored deuruxolitinib. Among approved oral JAKIs, deuruxolitinib 8 mg shows the highest short-term efficacy for severe AA. These findings provide preliminary evidence to guide treatment decisions but should be interpreted as exploratory pending confirmation.

Humans↗

PubMed2004

Bayesian Monte Carlo uncertainty analysis of human health risks from animal antimicrobial use in a dynamic model of emerging resistance.

Recent qualitative analyses warn of potential future human health risks from emergence of antibiotic resistance in food-borne pathogens due to the use of similar antimicrobial drugs in both food animals and human medicine. While historical data suggest that human health risks from some animal antimicrobials, such as virginiamycin (VM), have remained low (McDonald et al., 2001), there is a widespread concern that "resistance epidemics" or endemics could arise in the future. How reassuring is the past about the future? This article applies quantitative risk assessment methods to help find out, using human health risks from VM and the nearly identical human antimicrobial quinupristin-dalfopristin (QD) as a case study. A dynamic simulation model is used to predict the risks of emerging resistance to human antimicrobials in human populations from given input assumptions. Bayesian Monte Carlo uncertainty analysis allows past data to constrain and inform selection of input parameter values, and thus to predict the possible future resistance patterns that are consistent with historical data. The results show that health risks from VM use in food animals are highly sensitive to the human prescription rate of QD. For realistic prescription rates, quantitative risks are less than 1 x 10(-6) even for members of the most-threatened (ICU patient) population, while societal risks are <1 excess statistical death per year for the whole U.S. population. Such quantitative estimates complement more qualitative assessments that discuss the possibility of future "resistance epidemics" (or endemics) without quantifying their probabilities.

Animals↗

PubMed2001

Bayesian cost-effectiveness analysis from clinical trial data.

A key tool for assessing the relative cost-effectiveness of two treatments in health economics is the incremental C/E acceptability curve. We present Bayesian computations for this curve in the case where data on both costs and efficacy are available from a clinical trial. Analysis is given under various formulations of prior information. A case study is analysed in which reasonable prior information is shown to strengthen substantially the posterior inference, leading to a more conclusive assessment of cost-effectiveness. Calculations can be performed using readily available Bayesian software.

Anti-Asthmatic Agents↗

PubMed2003

Bayesian gene/species tree reconciliation and orthology analysis using MCMC.

MOTIVATION: Comparative genomics in general and orthology analysis in particular are becoming increasingly important parts of gene function prediction. Previously, orthology analysis and reconciliation has been performed only with respect to the parsimony model. This discards many plausible solutions and sometimes precludes finding the correct one. In many other areas in bioinformatics probabilistic models have proven to be both more realistic and powerful than parsimony models. For instance, they allow for assessing solution reliability and consideration of alternative solutions in a uniform way. There is also an added benefit in making model assumptions explicit and therefore making model comparisons possible. For orthology analysis, uncertainty has recently been addressed using parsimonious reconciliation combined with bootstrap techniques. However, until now no probabilistic methods have been available. RESULTS: We introduce a probabilistic gene evolution model based on a birth-death process in which a gene tree evolves 'inside' a species tree. Based on this model, we develop a tool with the capacity to perform practical orthology analysis, based on Fitch's original definition, and more generally for reconciling pairs of gene and species trees. Our gene evolution model is biologically sound (Nei et al., 1997) and intuitively attractive. We develop a Bayesian analysis based on MCMC which facilitates approximation of an a posteriori distribution for reconciliations. That is, we can find the most probable reconciliations and estimate the probability of any reconciliation, given the observed gene tree. This also gives a way to estimate the probability that a pair of genes are orthologs. The main algorithmic contribution presented here consists of an algorithm for computing the likelihood of a given reconciliation. To the best of our knowledge, this is the first successful introduction of this type of probabilistic methods, which flourish in phylogeny analysis, into reconciliation and orthology analysis. The MCMC algorithm has been implemented and, although not yet being in its final form, tests show that it performs very well on synthetic as well as biological data. Using standard correspondences, our results carry over to allele trees as well as biogeography.

Algorithms↗

PubMedDate not supplied

Bayesian approaches to meta-analysis of ROC curves.

A comparative review of important classic and Bayesian approaches to fixed-effects and random-effects meta-analysis of binormal ROC curves and areas underneath them is presented. The ROC analyses results of seven evaluation studies concerning the dexamethasone suppression test provide the basis for a worked example. Particular attention is given to fully Bayesian inference, a novelty in the ROC context, based on Gibbs samples from posterior distributions of hierarchical model parameters and related quantities. Fully Bayesian meta-analysis may properly account for the uncertainty associated with the model parameters, possibly incorporating prior knowledge and beliefs, and allows clinically intuitive predictions of unobserved study effects via calculation of posterior predictive densities. The effects of various different prior specifications (six noninformative as well as one informative) on the posterior estimates are investigated (sensitivity-analysis). Recommendations and suggestions for further research are made. Computer code for the more advanced methods may either be downloaded via the Internet or be found elsewhere.

Bayes Theorem↗