PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Bayesian modelling”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12Linked to original sources

Colour categorization by domestic chicks.

Spectral stimuli form a physical continuum, which humans divide into discrete non-overlapping regions or categories that are designated by colour names. Little is known about whether non-verbal animals form categories on stimulus continua, but work in psychology and artificial intelligence provides models for stimulus generalization and categorization. We compare predictions of such models to the way poultry chicks (Gallus gallus) generalize to novel stimuli following appetitive training to either one or two colours. If the two training colours are (to human eyes) red and greenish-yellow or green and blue, chicks prefer intermediates, i.e. orange rather than red or yellow and turquoise rather than green or blue. The level of preference for intermediate colours implies that the chicks interpolate between the training stimuli. However, they do not extrapolate beyond the limits set by the training stimuli, at least for red and yellow training colours. Similarly, chicks trained to red and blue generalize to purple, but they do not generalize across grey after training to the complementary colours yellow and blue. These results are consistent with a modified version of a Bayesian model of generalization from multiple examples that was proposed by Shepard and show similarities to human colour categorization.

Animals↗

Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes.

Biological markers that are both sensitive and specific for tumour regrowth or metastasis are increasingly becoming available and routinely monitored during the regular follow-up of patients treated for cancer. Obtained by a simple blood test, these markers provide an inexpensive non-invasive means for the early detection of recurrence (or progression). Currently, the longitudinal behaviour of the marker is viewed as an indicator of early disease progression, and is applied by a physician in making clinical decisions. One marker that has been studied for use in both population screening for early disease and for detection of recurrence in prostate cancer patients is PSA. The elevation of PSA levels is known to precede clinically detectable recurrence by 2 to 5 years, and current clinical practice often relies partially on multiple recent rises in PSA to trigger a change in treatment. However, the longitudinal trajectory for individual markers is often non-linear; in many cases there is a decline immediately following radiation therapy or surgery, a plateau during remission, followed by an exponential rise following the recurrence of the cancer. The aim of this article is to determine the multiple aspects of the longitudinal PSA biomarker trajectory that can be most sensitive for predicting time to clinical recurrence. Joint Bayesian models for the longitudinal measures and event times are utilized based on non-linear hierarchical models, implied by unknown change-points, for the longitudinal trajectories, and a Cox proportional hazard model for progression times, with functionals of the longitudinal parameters as covariates in the Cox model. Using Markov chain Monte Carlo sampling schemes, the joint model is fit to longitudinal PSA measures from 676 patients treated at Massachusetts General Hospital between the years 1988 and 1995 with follow-up to 1999. Based on these data, predictive schemes for detecting cancer recurrence in new patients based on their longitudinal trajectory are derived.

Bayes Theorem↗

A new approach to dose escalation in non-small-cell lung cancer.

PURPOSE: To describe the radiobiological rationale for dose-per-fraction escalation in non-small-cell lung cancer (NSCLC) and to devise a novel Phase I scheme to implement this strategy using advanced radiotherapy delivery technologies. METHODS AND MATERIALS: The data from previous dose escalation trials in NSCLC are reanalyzed to establish a dose-response relationship in this disease. We also use data relating prolongation in treatment time to survival to compute the potential doubling time for lung tumors. On the basis of these results, and using a Bayesian model to determine the probability of pneumonitis as a function of mean normalized lung dose, a dose-per-fraction escalation strategy is developed. RESULTS: Standard approaches to dose escalation using 2 Gy per fraction, five fractions per week, require doses in excess of 85 Gy to achieve 50% long-term control rate. This is partly because NSCLCs repopulate rapidly, with a 1.6% per day loss in survival from prolongation in overall treatment time beyond 6 weeks, and a cell doubling time of only 2.5 to 3.3 days. A dose-per-fraction escalation strategy, with a constant number of fractions, 25, and overall time, 5 weeks, is projected to produce tumor control rates predicted to be 10%-15% better than 2 Gy per fraction dose escalation, with equivalent late effects. This Phase I clinical study is divided into three parts. Step 1 examines the feasibility of the maximum breath-holding technique and junctioning of tomotherapy slices. Step 2 treats 10 patients with 30 fractions of 2 Gy over 6 weeks and then reduces duration to 5 weeks using fewer but larger fractions in 10 patients. Step 3 will consist of a dose-per-fraction escalation study on roughly 50 patients, maintaining 25 fractions in 5 weeks. Bayesian methodology (a modification of the Continual Reassessment Method) will be used in Step 3 to allow consistent and efficient escalation within five volume bins. CONCLUSION: A dose-per-fraction escalation approach in NSCLC should yield superior outcomes, compared to standard dose escalation approaches using a fixed dose per fraction, for a given level of pneumonitis and late toxicity. Highly conformal radiotherapy techniques, such as intensity modulated radiotherapy (IMRT) and helical tomotherapy with its adaptive capabilities, will be necessary to achieve significant dose-per-fraction escalation without unacceptable lung and esophageal morbidity.

Carcinoma, Non-Small-Cell Lung↗

Pharmacokinetics and milk discard times of pirlimycin after intramammary infusion: a population approach.

A population pharmacokinetic approach was used to analyse milk concentration data to determine whether milk discard times and the clearance of intramammary infusions of pirlimycin could be adequately predicted by readily available demographic variables. Milk samples were collected at 12 hourly milking intervals after dosing with pirlimycin during product development from both normal cows (primary data) and cows with naturally occurring mastitis (validation data) and pirlimycin concentration was determined by microbial inhibition assay. The data were analysed by the conditional estimation/ maximum likelihood population approach within the computer program PPharm and fitted a two compartment open model. Bayesian estimates of individual parameters allowed solutions for each cow, predicting the time after last dosing by which milk concentration reached the target safe concentration. From this population of times, the 95% confidence interval of the 99th percentile was defined as the milk discard time. After elimination of one very low producing outlier, the calculated discard time agreed with the label recommendation of 36 h (3 milkings, USA) after the last dose. Milk pirlimycin clearance was strongly and positively correlated to the logarithm of the kilograms of milk produced in 24 h at time of dosing (r2=0.939). Agreement was strong at most time points between predicted and measured pirlimycin concentrations in milk from cows with mastitis. This alternative method for determining milk discard times was compared to existing recommendations.

Animals↗

A Bayesian method for finding regulatory segments in DNA.

A goal of the human genome project is to determine the entire sequence of DNA (3 x 10(9) base pairs) found in chromosomes. The massive amounts of data produced by this project require interpretation. A Bayesian model is developed for locating regulatory regions in a DNA sequence. Regulatory regions are areas of DNA to which specific proteins bind and control whether or not a gene is transcribed to produce templates for protein synthesis. Each human cell contains the same DNA sequence. Thus the particular function of different cells is determined by the genes that are transcribed in that cell. A Hidden Markov chain is used to model whether a small interval of the DNA is in a regulatory region or not. This can be regarded as a changepoint problem where the changepoints are the start of a regulatory or nonregulatory region. The data consists of protein-binding elements, which are short subsequences, or "words," in the DNA sequence. Although these words can occur anywhere in the sequence, a larger number are expected in regulatory regions. Therefore, regulatory regions are detected by locating clusters of words. For a particular DNA sequence, the model automatically selects those words that best predict regions of interest. Markov chain Monte Carlo methods are used to explore the posterior distribution of the Hidden Markov chain. The model is tested by means of simulations, and applied to several DNA sequences.

Bayes Theorem↗

Empirical bayes methods and false discovery rates for microarrays.

In a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian modeling, and the frequentist method of "false discovery rates" proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences.

Bayes Theorem↗

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Over 75 Alzheimer's disease (AD) and dementia-associated variants have been identified through genome-wide association studies, but the utility of polygenic risk scores (PRS) for predicting AD and dementia in diverse and admixed populations remains unclear. We compared how PRS approaches differing in p-value thresholds, variant weights, and source ancestry perform in predicting dementia in 6338 African American, Chinese, Hispanic, and White individuals from the Multi-Ethnic Study of Atherosclerosis. We tested clumping and thresholding (C+T) methods with varying parameters against Bayesian approaches (PRS-CS, PRS-CSx). We compared the ability of each method to predict incident dementia in all participants and in groups stratified by self-reported race/ethnicity. We additionally analyzed performance across groups stratified by estimated proportion of non-Finnish European (NFE)-like ancestry. Including more variants does not improve performance. We found comparable associations between dementia and PRS when comparing a C+T method with only 15 SNPs and PRS derived from Bayesian models that include >&#x2009;800,000 SNPs (HR5e-08 = 1.18, 95% CI: 1.08-1.28; HRCSx = 1.17, 95% CI: 1.07-1.27). The p&#x2009;<&#x2009;5e-08 C+T method was more strongly associated with incident dementia in populations genetically dissimilar from the source data (HRlowNFE_5e-08 = 1.27, 95% CI: 1.08-1.50; HRlowNFE_CSx = 1.12, 95% CI: 0.94-1.33). More selective PRS models using genome-wide significant SNPs may be preferable for dementia prediction in diverse populations.

Aged↗

Assessing tumour markers.

This paper explores the factors involved in assessing the value of a medical test in a given clinical situation. It examines the problems involved in designing studies to investigate the role for a tumour marker in differential diagnosis and patient monitoring. An overview is given, including a critical evaluation of the preliminary research, the laboratory assay technique and potential clinical problems. Some examples are given. Specific approaches to the analysis of monitoring data are outlined, including the application of a sequential Bayesian model. This is illustrated using data from patients in the Medical Research Council's series of studies on the tumour marker Carcinoembryonic antigen.

Carcinoembryonic Antigen↗

Bayesian analysis of space-time variation in disease risk.

The analysis of variation of risk for a given disease in space and time is a key issue in descriptive epidemiology. When the data are scarce, maximum likelihood estimates of the area-specific risk and of its linear time-trend can be seriously affected by random variation. In this paper, we propose a Bayesian model in which both area-specific intercept and trend are modelled as random effects and correlation between them is allowed for. This model is an extension of that originally proposed for disease mapping. It is illustrated by the analysis of the cumulative prevalence of insulin dependent diabetes mellitus as observed at the military examination of 18-year-old conscripts born in Sardinia during the period 1936-1971. Data concerning the genetic differentiation of the Sardinian population are used to interpret the results.

Adolescent↗

Spatial variation of natural radiation and childhood leukaemia incidence in Great Britain.

This paper describes an analysis of the geographical variation of childhood leukaemia incidence in Great Britain over a 15 year period in relation to natural radiation (gamma and radon). Data at the level of the 459 district level local authorities in England, Wales and regional districts in Scotland are analysed in two complementary ways: first, by Poisson regressions with the inclusion of environmental covariates and a smooth spatial structure; secondly, by a hierarchical Bayesian model in which extra-Poisson variability is modelled explicitly in terms of spatial and non-spatial components. From this analysis, we deduce a strong indication that a main part of the variability is accounted for by a local neighbourhood 'clustering' structure. This structure is furthermore relatively stable over the 15 year period for the lymphocytic leukaemias which make up the majority of observed cases. We found no evidence of a positive association of childhood leukaemia incidence with outdoor or indoor gamma radiation levels. There is no consistent evidence of any association with radon levels. Indeed, in the Poisson regressions, a significant positive association was only observed for one 5-year period, a result which is not compatible with a stable environmental effect. Moreover, this positive association became clearly non-significant when over-dispersion relative to the Poisson distribution was taken into account.

Air Pollution, Indoor↗

A graphical user interface to facilitate patient-specific drug dosing.

This paper describes the development of a graphical user-interface (GUI) designed to facilitate the process of generating patient-specific drug doses using bayesian modelling software. The graphical user interface was developed in Visual Basic (Microsoft, Inc.) and runs under the Windows 3.1 (Microsoft, Inc.) operating system. Patient demographic data are stored in a relational database (Access, Microsoft, Inc.). The GUI and the database communicate via dynamic data exchange links. The largely object-oriented nature of the language allowed us to change the entire look and feel of the system with a few simple changes to the underlying code. Use of the relational database to store patient demographic information allows greater flexibility in searching for and displaying patient specific information. The GUI reduces the time required to enter data. The prototype has allowed us to experiment with different presentation methods, greatly improving the clinical acceptance of the dosing programs.

Computer Simulation↗

Relation of risk of systemic lupus erythematosus to west African admixture in a Caribbean population.

Risk of systemic lupus erythematosus (SLE) is higher in people of west African descent than in Europeans. The objective of this study was to distinguish between genetic and environmental explanations for this ethnic difference by examining the relationship of disease risk to individual admixture (defined as the proportion of the genome that is of west African ancestry); 124 cases of SLE and 219 matched controls resident in Trinidad were studied. Analysis of admixture was restricted to 52 cases and 107 controls who reported no Indian or Chinese ancestry. These individuals were typed with a panel of 26 single-nucleotide polymorphisms and five insertion/deletion polymorphisms chosen to have large allele frequency differentials between west African, European and Native American populations. A Bayesian model for population admixture, individual admixture and locus ancestry was fitted by Markov chain simulation. Mean west African admixture (M) was 0.81 in cases and 0.74 in controls (P=0.01). The risk ratio for SLE associated with unit change in M was estimated as 32.5 with a 95% confidence interval (CI) of 2.0-518. Adjustment for measures of socioeconomic status (household amenities in childhood and years of education) altered this risk ratio only slightly (adjusted risk ratio: 28.4, 95% CI 1.7-485). These results support an additive genetic model for the ethnic difference in risk of SLE between west Africans and Europeans, rather than an environmental explanation or an "overdominant" model in which risk is higher in heterozygous than in homozygous individuals. This conclusion lays a basis for localizing the genes underlying this ethnic difference in risk of SLE by admixture mapping.

Adult↗

Electron-beam computed tomography: a Bayesian approach to risk assessment.

The epidemic of coronary artery disease continues to affect a large number of individuals who often experience sudden and unexpected events. This underscores the need to develop more effective programs to detect silent atherosclerosis, with the ultimate goal of preventing coronary events. The use of conventional risk factors is helpful in assessing the median risk of a population, but it is often unsatisfactory in estimating the actual risk of an individual patient. As a consequence, newer imaging modalities are being developed to detect atherosclerosis in its early developmental phases. Technologies such as electron-beam computed tomography (EBCT) may render risk stratification more accurate if used in the appropriate patient populations and with the right diagnostic approach. Several studies have already demonstrated the power of coronary calcification as a strong predictor of future cardiovascular events. Nonetheless, the medical literature is currently pervaded by an animated debate, as some investigators still have concerns about the effectiveness of a preventive approach driven by technology. The use of Bayesian models to interpret data acquired with EBCT screening may provide practitioners with valuable evidence to aid in their decision making.

Bayes Theorem↗

Perception of 3D surface orientation from skew symmetry.

In this paper, we investigate how symmetry can be used to perceive 3D surface orientation. When a symmetric planar object is viewed from an angle, the projected contour has skew symmetry, which provides partial information about the 3D orientation of the object. For a given skew symmetry, this information can be characterized by a constraint curve of possible slant/tilt combinations that are consistent with a mirror-symmetric interpretation. These constraint curves move around when an object is rotated within a plane, and depend on what we will term the spin of the object: the angle between its axis of symmetry and the direction of tilt. To test the influence of symmetry constraint curves, we presented subjects with stereo images of symmetric objects that varied in spin, and had them perform an orientation-matching task. We found that the judgments showed biases that depended on the spin of the objects. Since other sources of information depend only on slant and tilt, not on spin, the biases imply that skew symmetry contributed to subjects' judgments. In a second experiment, we introduced conflicts between stereo and symmetry cues, and found that the spin-dependent biases can be modulated by selectively changing stereo slant. We propose an explanation of these results involving the optimal integration of stereo and skew symmetry, and present a Bayesian model that can account for the pattern of biases.

Analysis of Variance↗

Risk assessment for Cryptosporidium: a hierarchical Bayesian analysis of human dose response data.

Three dose-response studies were conducted with healthy volunteers using different Cryptosporidium parvum isolates (IOWA, TAMU, and UCP). The study data were previously analyzed for median infectious dose (ID50) using a simple cumulative percent endpoint method (Reed and Muench, 1938). ID50s were derived using two definitions of infection: one as subjects having oocysts detected in stool by direct fluorescence assay, and the other by a clinical finding of diarrhea with or without detected oocysts (Chappell et al., 1998; Okhuysen et al., 1999). In the present study, the data were analyzed using the broader definition of infection (i.e., presence of oocysts in stool and/or diarrheal illness characteristic of cryptosporidiosis). Maximum likelihood dose-response parameter estimates for UCP, IOWA, and TAMU were 2980, 190, and 17.5, respectively. Based on these estimates, the ID50s of the three respective isolates were 2066, 132, and 12.1. The three oocyst isolates were considered representative of a larger population of human-infecting strains and analyzed as combined data using a hierarchical Bayesian model. Hyperparameters defined the distribution of dose-response parameters for the population of strains. Output from Markov Chain Monte Carlo analysis described posterior distributions for the hyperparameters and for the parameters of the IOWA, TAMU, and UCP strains. Point estimates of dose-response parameters produced by this analysis were similar to the maximum likelihood estimates. Finally, the utility of these results for probabilistic risk assessment was evaluated. The risk of infection from single oocyst doses was derived for a mixture of the three isolates (where IOWA, TAMU, or UCP are equally likely), and for an oocyst selected at random from the larger population of strains. These estimated risks of infection were 0.018 and 0.028, respectively.

Animals↗

Prevalence estimates for paratuberculosis adjusted for test variability using Bayesian analysis.

The ELISA tests that are available to detect an infection with Mycobacterium avium subsp. paratuberculosis (MAP) have a limited validity expressed as the sensitivity (Se) and specificity (Sp). In many studies, the Se and Sp of the tests are treated as constants and this will result in an underestimation of the variability of the true prevalence (TP). Bayesian inference provided a natural framework for using information on the test variability (i.e., the uncertainty) in the estimates of test Se and Sp when estimating the TP. Data from two prevalence studies for MAP using an ELISA in several regions in two locations were available for the analyses. In location 1, all cattle of at least 3 years of age were sampled in approximately 90 randomly sampled herds in each of the four regions of the country. In location 2, in 30 randomly sampled herds in each of three regions, approximately 30 randomly selected cows were sampled. Information about the unknown test Se and Sp and MAP prevalence was incorporated into a Bayesian model by joint prior probability distributions. Posterior estimates were obtained by combining the actual likelihood with the prior distributions using Bayes' formula. The corrected cow-level TP (proportion of infected cows in a herd) was low, 5.8 and 3.6% in locations 1 and 2, respectively. Certain regions within a location differed significantly in herd-level TP (proportion of infected herds). The herd-level TP was 54.3% in location 1 (95% credible interval (CI) 46.1, 63.3%) and 32.9% in location 2 (95% CI: 14.4, 73.3%). The variation in the herd-level TP estimate for location 2 was more than three times as large as the variation in location 1 mainly because of the relatively small number of investigated herds in location 2. In future prevalence studies for MAP, sample size calculations should be based on a very low cow-level prevalence. Approximately 50 and 90% of the herds in the current study had an estimated cow-level TP below 4 and 10%, respectively.

Animals↗

Multimodality in the superior colliculus: an information theoretic analysis.

The deep superior colliculus (DSC) integrates multisensory input and triggers an orienting movement toward the source of stimulation (target). It would seem reasonable to suppose that input of an additional modality should always increase the amount of information received by a DSC neuron concerning a target. However, of all DSC neurons studied, only about one half in the cat and one-quarter in the monkey were multimodal. The rest received only unimodal input. Multimodal DSC neurons show the properties of multisensory enhancement, in which the neural response to an input of one modality is augmented by input of another modality, and of inverse effectiveness, in which weaker unimodal responses produce a higher percentage enhancement. Previously, we demonstrated that these properties are consistent with the hypothesis that DSC neurons use Bayes' rule to compute the posterior probability that a target is present given their stochastic sensory inputs. Here we use an information theoretic analysis of our Bayesian model to show that input of an additional modality may indeed increase target information, but only if input received from the initial modality does not completely reduce uncertainty concerning the presence of a target. Unimodal DSC neurons may be those whose unimodal input fully reduces target uncertainty and therefore have no need for input of another modality.

Bayes Theorem↗

Mortality risk perceptions: a Bayesian reassessment.

"This study uses data on perceived and actual mortality risks to test several alternative Bayesian models of the factors influencing risk beliefs. The analysis...indicates that while the hazard rate for the individual age group is an influential factor, the overall population death rate and the discounted expected number of life years lost due to the cause of death are also influential in affecting risk perceptions.... The predictive power of a linear perception model increases with the level of the risk and is least accurate for very small risks."

Behavior↗