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PubMed2005

Are the acute effects of particulate matter on mortality in the National Morbidity, Mortality, and Air Pollution Study the result of inadequate control for weather and season? A sensitivity analysis using flexible distributed lag models.

Time-series studies have linked daily variations in nonaccidental deaths with daily variations in ambient particulate matter air pollution, while controlling for qualitatively larger influences of weather and season. Although time-series analyses typically include nonlinear terms for weather and season, questions remain as to whether models to date have completely controlled for these important predictors. In this paper, the authors use two flexible versions of distributed lag models to control extensively for the confounding effects of weather and season. One version builds on the current approach to controlling for weather, while the other version offers a new approach. The authors conduct a comprehensive sensitivity analysis of the particulate matter-mortality relation by applying these methods to the recently updated National Morbidity, Mortality, and Air Pollution Study database that comprises air pollution, weather, and mortality time series from 1987 to 2000 for 100 US cities. They combine city-specific estimates of the short-term effects of particulate matter on mortality using a Bayesian hierarchical model. They conclude that, within the broad classes of models considered, national average estimates of particulate matter relative risk are consistent with previous estimates from this study and are robust to model specification for weather and seasonal confounding.

Aged↗
PubMed2005

A test of quantitative genetic theory using Drosophila- effects of inbreeding and rate of inbreeding on heritabilities and variance components.

Inbreeding is expected to decrease the heritability within populations. However, results from empirical studies are inconclusive. In this study, we investigated the effects of three breeding treatments (fast and slow rate of inbreeding - inbred to the same absolute level - and a control) on heritability, phenotypic, genetic and environmental variances of sternopleural bristle number in Drosophila melanogaster. Heritability, and phenotypic, genetic and environmental variances were estimated in 10 replicate lines within each of the three treatments. Standard least squares regression models and Bayesian methods were used to analyse the data. Heritability and additive genetic variance within lines were higher in the control compared with both inbreeding treatments. Heritabilities and additive genetic variances within lines were higher in slow compared with fast inbred lines, indicating that slow inbred lines retain more evolutionary potential despite the same expected absolute level of inbreeding. The between line variance was larger with inbreeding and more than twice as large in the fast than in the slow inbred lines. The different pattern of redistribution of genetic variance within and between lines in the two inbred treatments cannot be explained invoking the standard model based on selective neutrality and additive gene action. Environmental variances were higher with inbreeding, and more so with fast inbreeding, indicating that inbreeding and the rate of inbreeding affect environmental sensitivity. The phenotypic variance decreased with inbreeding, but was not affected by the rate of inbreeding. No inbreeding depression for mean sternopleural bristle number was observed in this study. Considerable variance between lines in additive genetic variance within lines was observed, illustrating between line variation in evolutionary potential.

Analysis of Variance↗
PubMed2003

Comparison of the dose-response relationships of 2 lipid-lowering agents: a Bayesian meta-analysis.

BACKGROUND: Comparing the dose-response of a new drug to that of a previously studied drug can aid in understanding their relative potencies. Two dose-finding studies addressed the effect of a new drug, rosuvastatin, on its ability to decrease low-density lipoprotein cholesterol (LDL-C) levels. One of these studies included 2 doses of atorvastatin, and substantial additional information is available in the literature about the effect of atorvastatin on LDL-C level lowering. METHODS: The 2 dose-finding studies of rosuvastatin considered otherwise healthy patients who had hypercholesterolemia. Comparable studies of atorvastatin were identified via a MEDLINE search in December 1999. Multiple reviewer consensus identified 15 of 41 studies on atorvastatin published since 1996 that met these selection criteria: reporting of LDL-C level change from baseline at least 6 weeks after treatment initiation, doses administered, and treatment group sizes. Eligible populations had clinical evidence of hypercholesterolemia. We excluded studies with patients who had severe illness or a previous history of transplantation. Data extraction of the mean, sample sizes, and SDs (or CIs) by dose was carried out independently by multiple reviewers. We combined the results from the various studies with Bayesian hierarchical modeling and analyzed them with Markov chain Monte Carlo techniques. RESULTS: Combining this study and literature results substantially increased the power to compare the dose-response relationships of rosuvastatin and atorvastatin. Rosuvastatin reduced LDL-C level by an estimated 10 to 17 percentage points more than atorvastatin when both were given at the same dose. Approximately one quarter of the dose of rosuvastatin achieved about the same magnitude of LDL-C level reduction as atorvastatin at dosages as high as 80 mg. This finding does not imply a 4-fold difference in efficacy overall and specifically does not describe the results at higher dosage levels. CONCLUSIONS: Bayesian meta-analysis of results from related studies allows the comparison of the dose-response relationships of 2 drugs, better estimates of a particular dose-response relationship within an individual study, and the expression of relative benefits (of dose and drug) in terms of probabilities. Explicitly comparing a study's results with historical data using Bayesian meta-analysis allows clinicians to view the study in the larger context of medical research.

Anticholesteremic Agents↗
PubMed2004

Mixed-effects modeling of the intrinsic ventilatory depressant potency of propofol in the non-steady state.

BACKGROUND: Despite the ubiquitous use of propofol for anesthesia and conscious sedation and numerous publications about its effect, a pharmacodynamic model for propofol-induced ventilatory depression in the non-steady state has not been described. To investigate propofol-induced ventilatory depression in the clinically important range (at and below the metabolic hyperbola while carbon dioxide is accumulating because of drug-induced ventilatory depression), the authors applied indirect effect modeling to Paco2 data at a fraction of inspired carbon dioxide of 0 during and after administration of propofol. METHODS: Ten volunteers underwent determination of their carbon dioxide responsiveness by a rebreathing design. The parameters of a power function were fitted to the end-expiratory carbon dioxide and minute ventilation data. The volunteers then received propofol in a stepwise ascending pattern with use of a target-controlled infusion pump until significant ventilatory depression occurred (end-tidal pressure of carbon dioxide > 65 mmHg and/or imminent apnea). Thereafter, the concentration was reduced to 1 microg/ml. Propofol pharmacokinetics and the Paco2 were determined from frequent arterial blood samples. An indirect response model with Bayesian estimates of the pharmacokinetics and carbon dioxide responsiveness in the absence of drug was used to describe the Paco2 time course. Because propofol reduces oxygen requirements and carbon dioxide production, a correction factor for propofol-induced decreasing of carbon dioxide production was included. RESULTS: The following pharmacodynamic parameters were found to describe the time course of hypercapnia after administration of propofol (population mean and interindividual variability expressed as coefficients of variation): F (gain of the carbon dioxide response), 4.37 +/- 36.7%; ke0, CO2, 0.95 min-1 +/- 59.8%; baseline Paco2, 40.9 mmHg +/- 12.8%; baseline minute ventilation, 6.45 l/min +/- 36.3%; kel, CO2, 0.11 min-1 +/- 34.2%; C50,propofol, 1.33 microg/ml +/- 49.6%; gamma, 1.68 +/- 21.3%. CONCLUSION: Propofol at common clinical concentrations is a potent ventilatory depressant. An indirect response model accurately described the magnitude and time course of propofol-induced ventilatory depression. The indirect response model can be used to optimize propofol administration to reduce the risk of significant ventilatory depression.

Adult↗
PubMed2000

Exponential family models and statistical genetics.

This article describes the evolution of applied exponential family models, starting at 1972, the year of publication of the seminal papers on generalized linear models and on Cox regression, and leading to multivariate (i) marginal models and inference based on estimating equations and (ii) random effects models and Bayesian simulation-based posterior inference. By referring to recent work in genetic epidemiology, on semiparametric methods for linkage analysis and on transmission/disequilibrium tests for haplotype transmission this paper illustrates the potential for the recent advances in applied probability and statistics to contribute to new and unified tools for statistical genetics. Finally, it is emphasized that there is a need for well-defined postgraduate education paths in medical statistics in the year 2000 and thereafter.

Biometry↗
PubMed2004

A sequence alignment-independent method for protein classification.

Annotation of the rapidly accumulating body of sequence data relies heavily on the detection of remote homologues and functional motifs in protein families. The most popular methods rely on sequence alignment. These include programs that use a scoring matrix to compare the probability of a potential alignment with random chance and programs that use curated multiple alignments to train profile hidden Markov models (HMMs). Related approaches depend on bootstrapping multiple alignments from a single sequence. However, alignment-based programs have limitations. They make the assumption that contiguity is conserved between homologous segments, which may not be true in genetic recombination or horizontal transfer. Alignments also become ambiguous when sequence similarity drops below 40%. This has kindled interest in classification methods that do not rely on alignment. An approach to classification without alignment based on the distribution of contiguous sequences of four amino acids (4-grams) was developed. Interest in 4-grams stemmed from the observation that almost all theoretically possible 4-grams (20(4)) occur in natural sequences and the majority of 4-grams are uniformly distributed. This implies that the probability of finding identical 4-grams by random chance in unrelated sequences is low. A Bayesian probabilistic model was developed to test this hypothesis. For each protein family in Pfam-A and PIR-PSD, a feature vector called a probe was constructed from the set of 4-grams that best characterised the family. In rigorous jackknife tests, unknown sequences from Pfam-A and PIR-PSD were compared with the probes for each family. A classification result was deemed a true positive if the probe match with the highest probability was in first place in a rank-ordered list. This was achieved in 70% of cases. Analysis of false positives suggested that the precision might approach 85% if selected families were clustered into subsets. Case studies indicated that the 4-grams in common between an unknown and the best matching probe correlated with functional motifs from PRINTS. The results showed that remote homologues and functional motifs could be identified from an analysis of 4-gram patterns.

Algorithms↗
PubMedDate not supplied

[Pharmacokinetic evaluation of a computerized target-controlled infusion system: application to propofol in orthopedic surgery].

PaMo 2.0, a type of software, includes a pharmacokinetic model for propofol in the adult. It allows both administration and monitoring of propofol target-controlled infusions. In order to evaluate PaMo 2.0, a prospective clinical trial compared, at defined infusion times, predicted and measured plasma propofol concentrations, in 28 patients programmed for hip-replacement surgery. A propofol plasma determination technique had first been validated, including high performance liquid chromatography with fluorescence detection. A statistical analysis based on correlation (r = 0.73), inaccuracy = 29.18 per cent, bias = 11.67 per cent, wobble = 19.15 per cent, and divergence = -0.06 per cent/min calculation, related to this system, has been carried out. PaMo 2.0 under-estimated plasma propofol concentrations. The convergence between predicted and measured propofol concentrations was good and not modified in respect of infusion time. This infusion system is suitable for propofol administration, but the integration of Bayesian pharmacokinetic models would greatly improve propofol plasma concentration estimation and regimen adaptation to each patient.

Adult↗
PubMed2000

Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis.

Cancer geneticists seek to identify genetic changes in tumor cells and to relate the genetic changes to tumor development. Because single changes can disrupt the cell cycle and promote other genetic changes, it is extremely hard to distinguish cause from effect. In this article we illustrate how 7 techniques from statistics, theoretical computer science, and phylogenetics can be used to infer and test possible models of tumor progression from single genome-wide descriptions of aberrations in a large sample of tumors. Specifically, we propose 4 tree models for tumor progression inferred from the large ovarian cancer data set described in the first 2 articles in this series. The models are derived from 2 different methods to select the non-random genetic aberrations and 2 different methods to infer the trees, given a set of events. Various aspects of the tree models are tested and extended by 5 methods: overall tests of independence, likelihood ratio tests, principal components analysis, directed acyclic graph modeling, and Bayesian survival analysis. All our methods lead to strikingly consistent conclusions about chromosomal breakpoints in ovarian adenocarcinoma, including (1) the non-random breakpoints in ovarian adenocarcinoma do not occur independently; (2) breakpoints in regions 1p3 and 11p1 are important early events and distinguish a class of tumors associated with poor prognosis; and (3) breakpoints in 1p1, 3p1, and 1q2 distinguish a class of ovarian tumors, and the breaks at 1p1 and 3p1 are associated with poor prognosis.

Adenocarcinoma↗
PubMed2006

Clustering microarray gene expression data using weighted Chinese restaurant process.

MOTIVATION: Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging. RESULTS: We devised an improved model-based Bayesian approach to cluster microarray gene expression data. Cluster assignment is carried out by an iterative weighted Chinese restaurant seating scheme such that the optimal number of clusters can be determined simultaneously with cluster assignment. The predictive updating technique was applied to improve the efficiency of the Gibbs sampler. An additional step is added during reassignment to allow genes that display complex correlation relationships such as time-shifted and/or inverted to be clustered together. Analysis done on a real dataset showed that as much as 30% of significant genes clustered in the same group display complex relationships with the consensus pattern of the cluster. Other notable features including automatic handling of missing data, quantitative measures of cluster strength and assignment confidence. Synthetic and real microarray gene expression datasets were analyzed to demonstrate its performance. AVAILABILITY: A computer program named Chinese restaurant cluster (CRC) has been developed based on this algorithm. The program can be downloaded at http://www.sph.umich.edu/csg/qin/CRC/.

Algorithms↗
PubMed2004

Identifying and accommodating statistical outliers when setting prospective payment rates for inpatient rehabilitation facilities.

OBJECTIVE: To demonstrate how a Bayesian outlier accommodation model identifies and accommodates statistical outlier hospitals when developing facility payment adjustments for Medicare's prospective payment system for inpatient rehabilitation care. DATA SOURCES/STUDY SETTING: Administrative data on costs and facility characteristics of inpatient rehabilitation facilities (IRFs) for calendar years 1998 and 1999. STUDY DESIGN: Compare standard linear regression and the Bayesian outlier accommodation model for developing facility payment adjustors for a prospective payment system. DATA COLLECTION: Variables describing facility average cost per case and facility characteristics were derived from several administrative data sources. PRINCIPAL FINDINGS: Evidence was found of non-normality of regression errors in the data used to develop facility payment adjustments for the inpatient rehabilitation facilities prospective payment system (IRF PPS). The Bayesian outlier accommodation model is shown to be appropriate for these data, but the model is largely consistent with the standard linear regression used in the development of the IRF PPS payment adjustors. CONCLUSIONS: The Bayesian outlier accommodation model is more robust to statistical outlier IRFs than standard linear regression for developing facility payment adjustments. It also allows for easy interpretation of model parameters, making it a viable policy alternative to standard regression in setting payment rates.

Bayes Theorem↗
PubMed2004

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

Arthritis, Rheumatoid↗
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↗
PubMed2002

Bayesian prediction of spatial count data using generalized linear mixed models.

Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction.

Agriculture↗
PubMed1998

Bayesian optimal design in population models for haematologic data.

We introduce a population model to design optimal apheresis schedules to collect blood stem cells from cancer patients. Blood stem cells are collected prior to the patient undergoing high-dose chemoradiotherapy and are returned after this treatment to enable reconstitution of the white blood cell components. Maximizing the number of cells collected in as few apheresis as possible is desirable. We use a longitudinal data model with random effects to describe profiles of individual patients. A hierarchical prior model introduces common mean profiles for patients undergoing different treatments. We find the optimal apheresis schedule for a new patient by minimizing an expected loss over the posterior predictive distribution of the patient's predicted CD34 profile. We implement estimation of the model and solution of the optimal design problem by a simulation approach, which allows us to accommodate arbitrary shapes for the profiles and realistic loss functions that include relative penalties for the number of scheduled stem cell collections and for collecting fewer than a specified target quantity of total collected stem cells.

Antigens, CD34↗
PubMed1999

Enantioselective induction of cyclophosphamide metabolism by phenytoin.

The objective of this study was to investigate the effect of phenytoin (PHE) on cyclophosphamide (CP) disposition. CP was administered to 6 adult patients in a preparative regimen for bone marrow transplantation consisting of busulfan and CP. Three of the patients received PHE and the other 3 "control" patients received diazepam (DZP) as anti-epileptic prophylactic treatment. Plasma samples were collected at intervals up to 24 h after CP administration. The plasma concentrations of (R)- and (S)-CP and their respective N-dechloroethylated metabolites, (R)- and (S)-DCE-CP were simultaneously fitted using an enantiospecific 2-compartment pharmacokinetic (PK) model with Bayesian control estimation. DZP had no significant effect on the metabolism of CP and any of its PK parameters. PHE, however, increased significantly the formation of (S)-DCE-CP while having no effect on the formation of (R)-DCE-CP. These results suggest that different enzymes are responsible for the formation of (S)-DCE-CP from (S)-CP and (R)-DCE-CP from (R)-CP. Additionally, assuming that PHE does not affect the passive renal elimination of (R)- and (S)-CP, this analysis suggests that the clearance of both (R)- and (S)-CP to 4-hydroxy-CP (the activation pathway) is increased by PHE.

Adult↗
PubMed2000

Spatio-temporal interaction with disease mapping.

Markov chain Monte Carlo methods are used to estimate mortality rates under a Bayesian hierarchical model. Spatial correlations are introduced to examine spatial effects relative to both regional and regional changes over time by groups. A special feature of the models is the inclusion of longitudinal variables which will describe temporal trends in mortality or incidences for different population groups. Disease maps are used to illustrate the role of different parameters in the model and pinpointing areas of interesting patterns. The methods are demonstrated by male cancer mortality data from the state of Missouri during 1973-1992. Of special interest will be the geographic variations in the trend of lung cancer mortality over the recent past. Marginal posterior distributions are used to examine effects due to spatial correlations and age difference in temporal trends. Numerical results from the Missouri data show that although spatial correlations exist, they do not have a large effect on the estimated mortality rates.

Aged↗
PubMedDate not supplied

Statistical issues in the analysis of disease mapping data.

In this paper we discuss a number of issues that are pertinent to the analysis of disease mapping data. As an illustrative example we consider the mapping of larynx cancer across electoral wards in the North West Thames region of the U.K. Bayesian hierarchical models are now frequently employed to carry out such mapping. In a typical situation, a three-stage hierarchical model is specified in which the data are modelled as a function of area-specific relative risks at stage one; the collection of relative risks across the study region are modelled at stage two; and at stage three prior distributions are assigned to parameters of the stage two distribution. Such models allow area-specific disease relative risks to be 'smoothed' towards global and/or local mean levels across the study region. However, these models contain many structural and functional assumptions at different levels of the hierarchy; we aim to discuss some of these assumptions and illustrate their sensitivity. When relative risks are the endpoint of interest, it is common practice to assume that, for each of the age-sex strata of a particular area, there is a common multiplier (the relative risk) acting upon each of the stratum-specific risks in that area; we will examine this proportionality assumption. We also consider the choices of models and priors at stages two and three of the hierarchy, the effect of outlying areas, and an assessment of the level of smoothing that is being carried out. For inference, we concentrate on the description of the spatial variability in relative risks and on the association between the relative risks of larynx cancer and an area-level measure of socio-economic status.

Age Factors↗
PubMed2003

Bayesian analysis of multilocus association in quantitative and qualitative traits.

A Bayesian model-based method for multilocus association analysis of quantitative and qualitative (binary) traits is presented. The method selects a trait-associated subset of markers among candidates, and is equally applicable for analyzing wide chromosomal segments (genome scans) and small candidate regions. The method can be applied in situations involving missing genotype data. The number of trait loci, their marker positions, and the magnitudes of their gene effects (strengths of association) are all estimated simultaneously. The inference of parameters is based on their posterior distributions, which are obtained through Markov chain Monte Carlo simulations. The strengths of the approach are: 1) flexible use of oligogenic models with unknown number of loci, 2) performing the estimation of association jointly with model selection, and 3) avoidance of the multiple testing problem, which typically complicates the approaches based on association testing. The performance of the method was tested and compared to the multilocus conditional search procedure by analyzing two simulated data sets. We also applied the method to cystic fibrosis haplotype data (two-locus haplotypes), where gene position has already been identified. The method is implemented as a software package, which is freely available for research purposes under the name BAMA.

Algorithms↗