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PubMed2001

Bayesian partitioning for estimating disease risk.

This paper presents a Bayesian nonlinear approach for the analysis of spatial count data. It extends the Bayesian partition methodology of Holmes, Denison, and Mallick (1999, Bayesian partitioning for classification and regression, Technical Report, Imperial College, London) to handle data that involve counts. A demonstration involving incidence rates of leukemia in New York state is used to highlight the methodology. The model allows us to make probability statements on the incidence rates around point sources without making any parametric assumptions about the nature of the influence between the sources and the surrounding location.

Bayes Theorem↗

PubMed2026

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with oral submucous fibrosis risk.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic and insidious oral disease characterized by hyalinization of the subepithelial connective tissue and progressive fibrosis of the oral submucosa. It is a precancerous condition of oral squamous cell carcinoma. Studies have demonstrated that single nucleotide polymorphisms (SNPs) are closely associated with susceptibility to OSF. This study aims to comprehensively evaluate the association between SNPs and OSF risk and to rank the strength of the association between different genetic models and OSF susceptibility. METHODS: Literature related to OSF was comprehensively searched from PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wangfang databases up to July 2025. Full-text case-control studies with patients diagnosed with OSF were included. Quality assessment was performed to evaluate the risk of bias. RevMan 5.4, GeMTC 0.14.3, and STATA 17.0 were used for the pairwise and Bayesian network meta-analysis. RESULTS: A total of 24 studies with 2545 cases and 3772 controls, covering 13 SNPs in 11 genes, were included in our meta-analysis. We found that CYP1A1 rs4646903:T>C, CYP1A1 rs1048943:A>G, GSTT1 null genotype, GSTM1 null genotype, and XRCC3 rs861539:C>T were associated with an increased risk of OSF, while MMP2 rs243865:C>T and MMP3 rs3025058: 5A>6A were associated with a decreased risk of OSF. Further Bayesian network meta-analysis indicated the top 5 genetic models with the highest association with OSF risk in network group 1 were the dominant model, homozygous model, allelic model, and recessive model of CYP1A1 rs1048943:A>G (ranked 1-4), and the heterozygous/dominant model of CYP1A1 rs4646903:T>C (both ranked 5). While the allelic models of XRCC3 rs861539:C>T and MMP3 rs3025058: 5A>6A ranked first for predicting OSF in group 2 and group 3, respectively. CONCLUSION: Some specific SNPs are significantly related to the risk of OSF. Among them, the dominant model of CYP1A1 rs1048943:A>G may be the most strongly associated genetic model with OSF risk. Future large-sample, well-designed studies with detailed genotype data are needed to validate the roles of these SNPs in OSF risk.

Humans↗

PubMed2001

A framework for cost-effectiveness analysis from clinical trial data.

We present a general Bayesian framework for cost-effectiveness analysis (CEA) from clinical trial data. This framework allows for very flexible modelling of both cost and efficacy related trial data. A common CEA technique is established for this wide class of models through linking mean efficacy and mean cost to the parameters of any given model. Examples are given in which efficacy may be measured as a continuous, binary, ordinal or time-to-event outcome, and in which costs are modelled as distributed normally, log-normally, as a mixture or non-parametrically. A case study is presented, illustrating the methodology and illuminating the role of prior information.

Bayes Theorem↗

PubMed1996

Nonparametric expectation maximization population modeling of ganciclovir.

The use of the nonparametric expectation maximization (NPEM2) program to estimate pharmacokinetic parameters of ganciclovir in a group of patients with human immunodeficiency virus (HIV) and cytomegalovirus (CMV) infection was evaluated. A 10-point data set per patient obtained over 8 hours was analyzed. Mean pharmacokinetic parameters obtained included rate constant from the central to the peripheral compartment (KCP,3.1 hr-1), rate constant from the peripheral to the central compartment (KPC, 0.824 hr-1), slope of the volume of distribution to body weight (VS, 0.246 L/kg), and slope of clearance to creatinine clearance (Cl(cr)) and body weight (CLS,0.222L/hr/kg/100 mL/ min Cl(cr). Use of NPEM2 led to identification of a subset of patients with CMV retinitis who had a more rapid clearance of ganciclovir of 0.51 to 0.54 L/hr/kg/100 mL/min Cl(cr). Use of smaller, optimally timed samples of five, four, and three data points per patient produced mean pharmacokinetic parameter results consistent with the full ten-point data set. When Bayesian-derived parameter estimates using a five-point data set were compared with a traditional, nonlinear, least-square analysis of the entire ten-point data set, estimates of clearance were determined to be relatively unbiased and precise. The ability of NPEM2 to estimate pharmacokinetic parameters and to determine the population distribution of the parameters was demonstrated. By using points in the analysis chosen by D-optimal design theory, NPEM2 was able to give consistent parameter estimates with as few as three data points. Determination of the distribution appeared to have been dependent on the time points used, however. The approach of MAP-Bayesian analysis to derive patient-specific estimates using optimal samples and prior estimates from a previous population pharmacokinetic analysis for inclusion in subsequent pharmacodynamic analyses of drug exposure (area under the concentration-time curve) may enable development of exposure-response and exposure-toxicity relationships.

Antiviral Agents↗

PubMed2006

Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.

Algorithms↗

PubMed1993

Population pharmacokinetic analysis of new aminoglycosides, astromicin and isepamicin, and evaluation of Bayesian prediction method for approximation of individual clearance of drug.

Pharmacokinetic data obtained from healthy subjects after a single i.v. infusion over 30 min of either astromicin (AST) or isepamicin (ISP), the newly developed aminoglycoside antibiotics, were analyzed by a computer program, NONMEM, together with those in patients having impaired renal functions of various degrees, which were cited from the literature. A two-compartment open model was utilized for the analysis, assuming that the total body clearance of drug (ClB) is linearly correlated with endogeneous creatinine clearance (Clcr). By the analysis, it was found that the body weight explains some part of interindividual variability in ClB of ISP, although it did not hold true in the case of AST. For each drug, the means and variances of ClB and the distribution volume of central compartment, and only the means of two intercompartmental constants (K12 and K21), all of which were obtained by the NONMEM analysis, were implemented in a Bayesian prediction program for a microcomputer. This, thus, clarified a point as to when blood sample should be collected in order to get the best prediction of individual ClB with the use of the above Bayesian program and the measurement of drug concentration in the sampled blood as a feedback information. For this purpose, the drug concentrations in plasma obtained in the multiple-dose study of each drug, in which the drug was administered in healthy subjects as i.v. infusion over 1 h every 12 h for 4.5 days, were used.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

PubMed2007

Bayesian comparison of spatially regularised general linear models.

In previous work (Penny et al., [2005]: Neuroimage 24:350-362) we have developed a spatially regularised General Linear Model for the analysis of functional magnetic resonance imaging data that allows for the characterisation of regionally specific effects using Posterior Probability Maps (PPMs). In this paper we show how it also provides an approximation to the model evidence. This is important as it is the basis of Bayesian model comparison and provides a unified framework for Bayesian Analysis of Variance, Cluster of Interest analyses and the principled selection of signal and noise models. We also provide extensions that implement spatial and anatomical regularisation of noise process parameters.

Artifacts↗

PubMed2002

Phylogeographic analysis of the cornsnake (Elaphe guttata) complex as inferred from maximum likelihood and Bayesian analyses.

Most phylogeographic studies have used maximum likelihood or maximum parsimony to infer phylogeny and bootstrap analysis to evaluate support for trees. Recently, Bayesian methods using Marlov chain Monte Carlo to search tree space and simultaneously estimate tree support have become popular due to its fast search speed and ability to create a posterior distribution of parameters of interest. Here, I present a study that utilizes Bayesian methods to infer phylogenetic relationships of the cornsnake (Elaphe guttata) complex using cytochrome b sequences. Examination of the posterior probability distributions confirms the existence of three geographic lineages. Additionally, there is no support for the monophyly of the subspecies of E. guttata. Results suggest the three geographic lineages partially conform to the ranges of previously defined subspecies, although Shimodaira-Hasegawa tests suggest that subspecies-constrained trees produce significantly poorer likelihood estimates than the most likely trees reflecting the evolution of three geographic assemblages. Based on molecular support, these three geographic assemblages are recognized as species using evolutionary species criteria: E. guttata, Elaphe slowinskii, and Elaphe emoryi [phylogeographic, maximum likelihood, maximum parsimony, bootstrap, Bayesian, Markov chain Monte Carlo, cornsnake, Cytochrome b, geographic lineages, E. guttta, E. slowinskii, and E. emoryi].

Animals↗

PubMed2005

Country specific cost comparisons from multinational clinical trials using empirical Bayesian shrinkage estimation: the Canadian ASSENT-3 economic analysis.

The growing number of multinational clinical trials in which patient-level health care resource data are collected have raised the issue of which is the best approach for making inference for individual countries with respect to the between-treatment difference in mean cost. We describe and discuss the relative merits of three approaches. The first uses the random effects pooled estimate from all countries to estimate the difference for any particular country. The second approach estimates the difference using only the data from the specific country in question. Using empirical Bayes estimation a third approach estimates the country-specific difference using a variance-weighted linear sum of the estimates provided by the other two approaches. The approaches are illustrated and compared using the data from the ASSENT-3 trial.

Abciximab↗

PubMed2006

Evaluating and improving power in whole-genome association studies using fixed marker sets.

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.

Genetic Markers↗

PubMed2006

Even more suicide attempts in clinical trials with paroxetine randomised against placebo.

BACKGROUND: Following our previous publication we have received critical comments to our conclusions as well as new data that are strengthening our findings. RESULTS: With the new data, 11 suicide attempts among patients on paroxetine against 1 among patients on placebo, we found with a Bayesian technique that the posterior probability that medication with paroxetine is associated with an increased intensity per year of a suicide attempt is from 0.98 to 0.99, depending on the prior. We found that the comment to our article by GSK representatives contained errors, misunderstanding and unwillingness to accept Bayesian principles in the analysis of clinical trials. CONCLUSION: We were in our previous publication, with preliminary data and a Bayesian approach, able to raise a concern that suicide attempts might be connected with the use of paroxetine. This suspicion has now been confirmed.

Adult↗

PubMed2003

Connective molecular pathways of experimental bladder inflammation.

Inflammation is an inherent response of the organism that permits its survival despite constant environmental challenges. The process normally leads to recovery from injury and to healing. However, if targeted destruction and assisted repair are not properly phased, chronic inflammation can result in persistent tissue damage. To better understand the inflammatory process, we recently introduced a profiling methodology to identify common genes involved in bladder inflammation. The method represents a complementation to the classic quantification of inflammation and provides information regarding the early, intermediate, and late events in gene regulation. However, gene profiling fails to describe the molecular pathways and their interconnections involved in the particular inflammatory response. The present work introduces a new statistical technique for inferring functional interconnections between inflammatory pathways underlying classic models of bladder inflammation and permits the modeling of the inflammatory network. This new statistical method is based on variants of cluster analysis, Boolean networking, differential equations, Bayesian networking, and partial correlation. By applying partial correlation analysis, we developed mosaics of gene expression that permitted a global visualization of common and unique pathways elicited by different stimuli. The significance of these processes was tested from both biological and statistical viewpoints. We propose that connective mosaic may represent the necessary simplification step to visualize cDNA array results.

Animals↗

PubMed2026

Molecular targeted therapy in combination with chemotherapy for the treatment of platinum-resistant/refractory ovarian cancer (PROC): a systematic review and network meta-analysis.

BACKGROUND: Although single-agent chemotherapy is the most common approach for treating platinum-resistant or refractory ovarian cancer (PROC), there is growing evidence that combining molecular targeted agents with chemotherapy is beneficial, especially for certain patient groups. However, the most effective combination regimen remains elusive. OBJECTIVES: This Bayesian network meta-analysis (NMA) aims to identify the best combination therapy for PROC. METHODS: Relevant studies were searched in PubMed, EMBASE, Web of Science and the Cochrane Central Register of Controlled Trials from their inception until October 2024. The primary outcomes were overall survival (OS), progression-free survival (PFS) and adverse events (AEs). Statistical analyses were performed using the GEMTC package (1.0-2) and R 4.2.0. This review was registered in PROSPERO (CRD42023428414). RESULTS: Our analysis of 22 randomized controlled trials (RCTs) (n = 3408) demonstrated that chemotherapy combinations with bevacizumab (hazard ratio (HR) = 0.52-0.65), sorafenib (HR = 0.65, 95% confidence interval (CI): 0.45-0.93) or adavosertib (HR = 0.56, 95%CI: 0.35-0.90) significantly improved OS and PFS versus chemotherapy alone. Notably, adavosertib + gemcitabine was associated with an increased risk of grade 3-4 AEs (relative risk (RR) = 1.8, 95%CI: 1.3-2.7), but these were generally manageable. CONCLUSIONS: Bevacizumab-based combinations demonstrate consistent benefits across multiple regimens for PROC. Paclitaxel + bevacizumab emerges as the optimal balance of efficacy and safety. Topotecan + sorafenib could be an alternative for patients who are ineligible for anti-angiogenic therapy.

Humans↗

PubMed1992

Evaluation of Bayesian estimation in comparison to NONMEM for population pharmacokinetic data analysis: application to pefloxacin in intensive care unit patients.

The pharmacokinetics of pefloxacin (PF) were investigated in a population of 74 intensive care unit patients receiving 400 mg bid as 1-hr infusion using (i) Bayesian estimation (BE) of individual patient parameters followed by multiple linear regression (MLR) analysis and (ii) NONMEM analysis. The data consisted of 3 to 9 PF plasma levels per patient measured over 1 to 3 dosage intervals (total 113) according to four different limited (suboptimal) sampling 3-point protocols. Twenty-nine covariates (including 15 comedications) were considered to explain the interpatient variability. Predicted PF CL for a patient with median covariates values was similar in both BE/MLR and NONMEM analysis (4.02 and 3.92 L/hr, respectively). Bilirubin level and age were identified as the major determinants of PF CL by both approaches with similar predicted magnitude of effects (about 40 and 30% decrease of median CL, respectively). Confounding effects were observed between creatinine clearance (26% decrease of PF CL in the BE/MLR model), simplified acute physiology score (a global score based on 14 biological and clinical variables) (18% decrease of median CL in the NONMEM model) and age (entered in both models) which were highly correlated in our data base. However, both models predicted similar PF CL for actual subpopulations by using actual covariate values. Finally, the NONMEM analysis allowed identification of an effect of weight on CL (decrease of CL for weight < 65 kg) whereas the BE/MLR analysis predicted an increase of CL in patients treated with phenobarbital. In conclusion, both approaches allowed identification of the major risk factors of PF pharmacokinetics in ICU patients. Their potential use at different stages of drug development is discussed.

Adolescent↗

PubMed1999

A novel Bayesian decision procedure for early-phase dose-finding studies.

Phase I first-in-man studies in normal, healthy volunteers are performed to define a maximum safe dose and to identify a range of acceptable doses for later drug development studies in patients. Analysis of pharmacokinetic and pharmacodynamic data using mixed-effects modeling can be used to fit an overall dose-response relationship. By expressing prior information as pseudodata, the same methodology can be used to perform a Bayesian analysis and to determine posterior modal estimates for the model parameters. Decision theory can then be applied to maximize a chosen gain function, utilizing real-time data capture for choosing safe doses in a way that will provide more informative responses, thus accelerating study completion. The methodology is introduced elsewhere (1). The purpose of this paper is to describe software currently in development and to illustrate the method using an example from a recent study.

Area Under Curve↗

PubMed2004

Bayesian approach to study the temporal trend and the geographical variation in the risk of type 1 diabetes. The Sardinian Conscript Type 1 Diabetes Registry.

BACKGROUND: A previous analysis of the Sardinian Conscript Type 1 Diabetes Registry indicated an abrupt increase in the risk for type 1 diabetes (T1D) among Sardinian male cohorts starting from the 1946 one. OBJECTIVE: The aim of this study was to update the point prevalence of T1D among Sardinian male army conscripts aged 18 yr from 1992 to 1997 described in the previous survey and to update its secular trend. It also looked at the geographical distribution of prevalence. SUBJECTS AND METHODS: All male Italian citizens must appear before the Conscript Board for a clinical examination prior to military service. T1D patients are rejected with T1D listed as the reason. We examined, retrospectively, a total of 83807 Sardinian males aged 18, born between 1 January 1974 and 31 December 1979. They virtually represent the whole Sardinian population of that age and sex. RESULTS: A total of 307 subjects with T1D were identified. T1D point prevalence for the birth cohorts 1974-1979 was 3.66/1000 (95% CI 3.28-4.09). Bayesian analysis of the geographical distribution of prevalence shows the highest rate in the Southern part of the island (Cagliari province) and the lowest in the north-western part (Sassari province). A similar picture was produced by data from the Eurodiab Registry in Sardinia (1989 ongoing). No significant temporal trend of prevalence was seen in the 6 yr examined in the current study; pooling these recent birth cohorts with the previous ones, the increasing trend of prevalence was confirmed. CONCLUSIONS: Our data confirmed the South to North gradient of T1D and the progressive increase of T1D risk among males in Sardinia.

Adult↗

PubMed1992

On some applications of Bayesian methods in cancer clinical trials.

The NCCTG randomized controlled clinical trial for the treatment of advanced colorectal carcinoma is a wonderful case study of the dynamic interplay between scientific learning and statistical inference. Ethical concerns for minimizing the number of patients assigned to an inferior treatment and interest in identifying subsets of patients for whom a treatment is most likely efficacious pose challenging problems for the practice of statistics. In the first part of this paper, I comment on the applications of Bayesian methods to these problems in the NCCTG trial as presented by Freedman and Spieglehalter and Dixon and Simon, respectively. In the second part of this paper, I discuss and illustrate a Bayesian approach to model sensitivity analysis with a particular focus on model specification and criticism. The Bayesian approach provides a formal methodology to assess the sensitivity of inferences to the inputs into an analysis so that it is possible to investigate the consequences of the specification of the model. I apply these methods to the specification and criticism of a class of survival models for the analysis of survival times in the NCCTG trial.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed2005

Evaluation of decay times in coupled spaces: reliability analysis of Bayeisan decay time estimation.

This paper discusses quantitative tools to evaluate the reliability of "decay time estimates" and inter-relationships between multiple decay times for estimates made within a Bayesian framework. Previous works [Xiang and Goggans, J. Acoust. Soc. Am. 110, 1415-1424 (2001); 113, 2685-2697 (2003)] have applied Bayesian framework to cope with the demanding tasks in estimating multiple decay times from Schroeder decay functions measured in acoustically coupled spaces. A parametric model of Schroeder decay function [Xiang, J. Acoust. Soc. Am. 98, 2112-2121 (1995)] has been used for the Bayesian model-based analysis. The relevance of this work is that architectural acousticians need to know how well determined are the estimated decay times calculated within Bayesian framework using Schroeder decay function data. This paper will first address the estimation of global variance of the residual errors between the Schroeder function data and its model. Moreover, this paper discusses how the "landscape" shape of the posterior probability density function over the decay parameter space influences the individual decay time estimates, their associated variances, and their inter-relationships. This paper uses experimental results from measured room impulse responses in real halls to describe a model-based sampling method for an efficient estimation of decay times, and their individual variances. These parameters along with decay times are relevant decay parameters for evaluation and understanding of acoustically coupled spaces.

Journal Article↗