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PubMed1999

Multivariate survival analysis with positive stable frailties.

In this paper, we describe Bayesian modeling of dependent multivariate survival data using positive stable frailty distributions. A flexible baseline hazard formulation using a piecewise exponential model with a correlated prior process is used. The estimation of the stable law parameter together with the parameters of the (conditional) proportional hazards model is facilitated by a modified Gibbs sampling procedure. The methodology is illustrated on kidney infection data (McGilchrist and Aisbett, 1991).

Algorithms↗
PubMed2004

Bayesian point estimation of quantitative trait loci.

In this article, we consider the problem of the estimation of quantitative trait loci (QTL), those chromosomal regions at which genetic information affecting some quantitative trait is encoded. Generally the number of such encoding sites is unknown, and associations between neutral molecular marker genotypes and observed trait phenotypes are sought to locate them. We consider a Bayesian model for simple experimental designs, and discuss the existing approaches to inference for this problem. In particular, we focus on locating positions of the best candidate markers segregating for the trait, a situation which is of primary interest in comparative mapping. We introduce a loss function for estimating both the number of QTL and their location, and we illustrate its application via simulated and real data.

Animals↗
PubMed2006

Genetic diversity, population structure, effective population size and demographic history of the Finnish wolf population.

The Finnish wolf population (Canis lupus) was sampled during three different periods (1996-1998, 1999-2001 and 2002-2004), and 118 individuals were genotyped with 10 microsatellite markers. Large genetic variation was found in the population despite a recent demographic bottleneck. No spatial population subdivision was found even though a significant negative relationship between genetic relatedness and geographic distance suggested isolation by distance. Very few individuals did not belong to the local wolf population as determined by assignment analyses, suggesting a low level of immigration in the population. We used the temporal approach and several statistical methods to estimate the variance effective size of the population. All methods gave similar estimates of effective population size, approximately 40 wolves. These estimates were slightly larger than the estimated census size of breeding individuals. A Bayesian model based on Markov chain Monte Carlo simulations indicated strong evidence for a long-term population decline. These results suggest that the contemporary wolf population size is roughly 8% of its historical size, and that the population decline dates back to late 19th century or early 20th century. Despite an increase of over 50% in the census size of the population during the whole study period, there was only weak evidence that the effective population size during the last period was higher than during the first. This may be caused by increased inbreeding, diminished dispersal within the population, and decreased immigration to the population during the last study period.

Animal Migration↗
PubMed2004

Zinc and nitrate in the ground water and the incidence of Type 1 diabetes in Finland.

AIMS: In Finland, the risk of childhood Type 1 diabetes varies geographically. Therefore we investigated the association between spatial variation of Type 1 diabetes and its putative environmental risk factors, zinc and nitrates. METHODS: The association was evaluated using Bayesian modelling and the geo-referenced data on diabetes cases and population. RESULTS: Neither zinc nor nitrate nor the urban/rural status of the area had a significant effect on the variation in incidence of childhood Type 1 diabetes. CONCLUSIONS: The results showed that although there was no significant difference in incidence between rural and urban areas, there was a tendency to increasing risk of Type 1 diabetes with the increasing concentration of NO3 in drinking water. The fact that no significant effect was found may stem from the aggregated data being too crude to detect it.

Adolescent↗
PubMed2005

Bias adjustment in Bayesian estimation of bird nest age-specific survival rates.

The populations of many North American landbirds are showing signs of declining. Gathering information on breeding productivity allows early detection of unhealthy populations and helps develop good habitat-management practices. In this paper, we study the performance of the Bayesian model (He, 2003, Biometrics 59, 962-973) for age-specific nest survival rates with irregular visits. We find that the estimates are satisfactory except for the age-one survival rate. Usually the more days skipped between two visits, the more serious the underestimation of the age-one survival rate. We investigated the problem and developed three approaches to adjust for the underestimation bias. The simulation results show that the three approaches can significantly improve the estimation of the age-one survival rate.

Age Factors↗
PubMed1994

Chronic diffuse infiltrative lung disease: determination of the diagnostic value of clinical data, chest radiography, and CT and Bayesian analysis.

PURPOSE: To assess the value of clinical, chest radiographic, and computed tomographic (CT) findings in classifying chronic diffuse infiltrative lung disease (CDILD) MATERIALS AND METHODS: Two samples from the same population were consecutively studied: the training set (group A, n = 208) for the development of the decision aid and the test set (group B, n = 100) for validation. Computer-aided diagnoses were made with a Bayesian model that assigned to each patient diagnostic probabilities based on clinical, radiographic, or CT variables. RESULTS: In group A, a correct diagnosis based on clinical data was obtained in 29% of cases; radiography, 9%; and CT, 36%. This increased to 54% when clinical and radiographic variables were combined (P < .0001) and to 80% when data from all three were analyzed together (P < .0001). With prior and conditional probabilities determined from group A, the frequency of correct diagnosis in group B was 27% with clinical data, which increased to 53% (P < .0001) with radiographic findings and 61% after including CT data (P = .07). CONCLUSION: CT can help determine the specific diagnosis in patients with CDILD.

Bayes Theorem↗
PubMed2004

Bayesian integration in force estimation.

When we interact with objects in the world, the forces we exert are finely tuned to the dynamics of the situation. As our sensors do not provide perfect knowledge about the environment, a key problem is how to estimate the appropriate forces. Two sources of information can be used to generate such an estimate: sensory inputs about the object and knowledge about previously experienced objects, termed prior information. Bayesian integration defines the way in which these two sources of information should be combined to produce an optimal estimate. To investigate whether subjects use such a strategy in force estimation, we designed a novel sensorimotor estimation task. We controlled the distribution of forces experienced over the course of an experiment thereby defining the prior. We show that subjects integrate sensory information with their prior experience to generate an estimate. Moreover, subjects could learn different prior distributions. These results suggest that the CNS uses Bayesian models when estimating force requirements.

Bayes Theorem↗
PubMed2005

[Probability of developing and dying of cancer in Catalonia during the period 1998-2001].

BACKGROUND AND OBJECTIVE: We intended to estimate the probability of developing and dying from cancer in Catalonia during the period 1998-2001. PATIENTS AND METHOD: We used a Bayesian model which incorporates data from the Tarragona and Girona Cancer Registries and from the Catalonia Mortality Registry. The probability of developing and dying from cancer has been calculated using a competitive risk-based methodology. RESULTS: Lifetime probability of developing cancer in Catalonia is almost 1 out of 2 (43.7%) for men and 1 out of 3 (32.1%) in women. The probability of dying from cancer is 29.1% for men and 17.9% in women. 67% of men and 56% of women diagnosed with cancer will die from this disease. One out of 14 men will develop a lung cancer during his life and 1 out of 11 women will develop breast cancer. CONCLUSIONS: The observed rising in the probability of developing cancer in Catalonia over the last ten years highlights even more than ever the importance of this health problem.

Adolescent↗
PubMed2002

Coronary artery pattern and outcome of arterial switch operation for transposition of the great arteries: a meta-analysis.

BACKGROUND: Prior studies of coronary pattern and outcome after arterial switch operation (ASO) for transposition of the great arteries (TGA) have been hindered by limited statistical power. This meta-analysis assesses the effect of coronary anatomy on post-ASO mortality, both overall and adjusted for time. METHODS AND RESULTS: A literature search revealed 9 independent series that reported post-ASO mortality by coronary pattern in a total of 1942 patients. Odds ratios comparing all-cause mortality in patients with usual versus variant coronary patterns were calculated and combined by use of an empirical Bayesian model. Single coronary patterns, both of which loop around the great vessels, were associated with significant mortality (OR 2.9, 95% CI 1.3 to 6.8), whereas looping patterns that arose from 2 separate ostia were not (OR 1.2, 95% CI 0.8 to 1.9). This latter group includes patients with the most common variant, circumflex from right coronary artery. Patients with an intramural coronary artery had the greatest mortality (OR 6.5, 95% CI 2.9 to 14.2). Overall, patients with any variant coronary pattern had nearly twice the mortality seen in those with the usual pattern (OR 1.7, 95% CI 1.3 to 2.4). Single ostium patterns and intramural coronary arteries remained associated with significant added mortality after adjustment for time-trend effects. CONCLUSIONS: Over the past 2 decades, patients with common coronary variants have undergone ASO without added mortality compared with those with the usual coronary pattern. Those with intramural or single coronary arteries have significant added mortality that has persisted over time.

Coronary Vessel Anomalies↗
PubMed2003

Dictionary learning algorithms for sparse representation.

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial "25 words or less"), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).

Algorithms↗
PubMed2002

Two-dimensional motion perception without feature tracking.

Feature-tracking explanations of 2D motion perception are fundamentally distinct from motion-energy, correlation, and gradient explanations, all of which can be implemented by applying spatiotemporal filters to raw image data. Filter-based explanations usually suffer from the aperture problem, but 2D motion predictions for moving plaids have been derived from the intersection of constraints (IOC) imposed by the outputs of such filters, and from the vector sum of signals generated by such filters. In most previous experiments, feature-tracking and IOC predictions are indistinguishable. By constructing plaids in apparent motion from missing-fundamental gratings, we set feature-tracking predictions in opposition to both IOC and vector-sum predictions. The perceived directions that result are inconsistent with feature tracking. Furthermore, we show that increasing size and spatial frequency in Type 2 missing-fundamental plaids drives perceived direction from vector-sum toward IOC directions. This reproduces results that have been used to support feature-tracking, but under experimental conditions that rule it out. We discuss our data in the context of a Bayesian model with a gradient-based likelihood and a prior favoring slow speeds. We conclude that filter-based explanations alone can explain both veridical and non-veridical 2D motion perception in such stimuli.

Adult↗
PubMed2002

Ecological statistics of Gestalt laws for the perceptual organization of contours.

Although numerous studies have measured the strength of visual grouping cues for controlled psychophysical stimuli, little is known about the statistical utility of these various cues for natural images. In this study, we conducted experiments in which human participants trace perceived contours in natural images. These contours are automatically mapped to sequences of discrete tangent elements detected in the image. By examining relational properties between pairs of successive tangents on these traced curves, and between randomly selected pairs of tangents, we are able to estimate the likelihood distributions required to construct an optimal Bayesian model for contour grouping. We employed this novel methodology to investigate the inferential power of three classical Gestalt cues for contour grouping: proximity, good continuation, and luminance similarity. The study yielded a number of important results: (1) these cues, when appropriately defined, are approximately uncorrelated, suggesting a simple factorial model for statistical inference; (2) moderate image-to-image variation of the statistics indicates the utility of general probabilistic models for perceptual organization; (3) these cues differ greatly in their inferential power, proximity being by far the most powerful; and (4) statistical modeling of the proximity cue indicates a scale-invariant power law in close agreement with prior psychophysics.

Form Perception↗
PubMedDate not supplied

Ambulatory blood pressure monitoring and diagnostic errors in hypertension: a Bayesian approach.

Random variability of blood pressure complicates the diagnosis and subsequent treatment of hypertension. To evaluate the importance of the number of blood pressure measurements in the correct diagnosis and control of hypertension, the authors used a Bayesian model to estimate the true average blood pressure of a group of newly diagnosed hypertensives, then calculated the diagnostic error that would result from monitoring methods using 24 daytime measurements or from using only three random monitoring measurements. The study population consisted of 129 individuals with newly diagnosed mild hypertension according to standard criteria, who were also evaluated with an ambulatory blood pressure monitor. In true normotensives (daytime diastolic blood pressure <90 mm Hg), the negative predictive value with three measurements was 0.92, and it rose to 0.96 with monitoring methods. In mild hypertensives (90-104 mm Hg), the positive predictive value was 0.64 with three measurements and 0.84 with monitoring methods, thus reducing the rate of false mild hypertensives from 35% to 15%. Finally, in patients with moderate or severe hypertension (>104 mm Hg), the positive predictive value improved from 0.26 with three readings to 0.61 with monitoring methods. Similar results were observed with daytime systolic pressure measurements. As the number of measurements increased, the diagnostic error due to the random variability of blood pressure became progressively smaller. In cases of hypertension, the large improvement in predictive values may justify using monitoring methods to confirm standard diagnosis.

Adult↗
PubMed1999

Systematic reviews and meta-analysis: a structured review of the methodological literature.

OBJECTIVE: To systematically review methods for systematic review/meta-analysis in order to identify the different methodological and statistical methods that have been proposed. A summary of the main findings is presented here, with emphasis given to health services research topics. METHODS: A thorough systematic search for methodological papers was carried out using a variety of methods, including the use of electronic databases. Approximately 1000 potentially relevant references were identified, a number of them from education, psychology and sociology. RESULTS: After briefly reviewing the procedural methods required to carry out a review, and the basic statistical methods used to combine study estimates, less established methods are discussed. These include methods for dealing with publication bias, meta-regression, meta-analysis of individual patient data, the synthesis of non-randomized evidence alone and in combination with randomized studies. Bayesian modelling and economic evaluation through meta-analysis. Recommendations for meta-analytical practice are given; these are either distilled from previous guidelines, or constructed where there appears to be a broad consensus across the literature. CONCLUSIONS: It is hoped that this review will provide a consistent and comprehensive, but concise, description of the methods available for synthesizing evidence, that it will promote better quality reviews of the results of health services research and identify specific areas which require methodological development.

Bayes Theorem↗
PubMed2006

BayGO: Bayesian analysis of ontology term enrichment in microarray data.

BACKGROUND: The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. RESULTS: BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. CONCLUSION: The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

Bacteria↗
PubMed2003

Locating disease genes using Bayesian variable selection with the Haseman-Elston method.

BACKGROUND: We applied stochastic search variable selection (SSVS), a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis) and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models. RESULTS: In applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers) according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results. CONCLUSIONS: We showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.

Bayes Theorem↗
PubMed2006

Operon information improves gene expression estimation for cDNA microarrays.

BACKGROUND: In prokaryotic genomes, genes are organized in operons, and the genes within an operon tend to have similar levels of expression. Because of co-transcription of genes within an operon, borrowing information from other genes within the same operon can improve the estimation of relative transcript levels; the estimation of relative levels of transcript abundances is one of the most challenging tasks in experimental genomics due to the high noise level in microarray data. Therefore, techniques that can improve such estimations, and moreover are based on sound biological premises, are expected to benefit the field of microarray data analysis RESULTS: In this paper, we propose a hierarchical Bayesian model, which relies on borrowing information from other genes within the same operon, to improve the estimation of gene expression levels and, hence, the detection of differentially expressed genes. The simulation studies and the analysis of experiential data demonstrated that the proposed method outperformed other techniques that are routinely used to estimate transcript levels and detect differentially expressed genes, including the sample mean and SAM t statistics. The improvement became more significant as the noise level in microarray data increases. CONCLUSION: By borrowing information about transcriptional activity of genes within classified operons, we improved the estimation of gene expression levels and the detection of differentially expressed genes.

Animals↗
PubMed2005

Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging.

BACKGROUND: Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers) under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided. RESULTS: The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothing techniques. Because of the small weight assigned to the rate observed over the entity being smoothed (kernel weight), the population-weighted average leads to risk maps that show little variability. Other techniques assign larger and similar kernel weights but they use a different piece of auxiliary information in the prediction: global or local means for global or local empirical Bayes smoothers, and spatial combination of surrounding rates for the geostatistical estimator. Simulation studies indicated that Poisson kriging outperforms other approaches for most scenarios, with a clear benefit when the risk values are spatially correlated. Global empirical Bayes smoothers provide more accurate predictions under the least frequent scenario of spatially random risk. CONCLUSION: The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of mortality rates into the mapping of risk values and the quantification of the associated uncertainty, while being easier to implement than a full Bayesian model. The availability of a public-domain executable makes the geostatistical analysis of health data, and its comparison to traditional smoothers, more accessible to common users. In future papers this methodology will be generalized to the simulation of the spatial distribution of risk values and the propagation of the uncertainty attached to predicted risks in local cluster analysis.

Journal Article↗