Teacher reports as a predictor of a schizophrenia and borderline schizophrenia: a Bayesian decision analysis.
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Adolescent↗
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Results for “Bayesian analysis”
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Bayesian linkage analysis, or: how I learned to stop worrying and love the posterior probability of linkage.
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Bayes Theorem↗
Fast LROC analysis of Bayesian reconstructed emission tomographic images using model observers.
Lesion detection and localization is an important task in emission computed tomography. Detection and localization performance with signal location uncertainty may be summarized by a scalar figure of merit, the area under the localization receiver operating characteristic (LROC) curve, A(LROC). We consider model observers to compute A(LROC) for two-dimensional maximum a posteriori (MAP) reconstructions. Model observers may be used to rapidly prototype studies that use human observers. We address the case background-known-exactly (BKE) and signal known except for location. Our A(LROC) calculation makes use of theoretical expressions for the mean and covariance of the reconstruction and, unlike conventional methods that also use model observers, does not require computation of a large number of sample reconstructions. We validate the results of the procedure by comparison to A(LROC) obtained using a gold-standard Monte Carlo method employing a large set of reconstructed noise samples. Under reasonable simulation conditions, our theoretical calculation is about one to two orders of magnitude faster than the conventional Monte Carlo method.
Algorithms↗
Bayesian segregation analysis of milk flow in Swiss dairy cattle using Gibbs sampling.
Segregation analyses with Gibbs sampling were applied to investigate the mode of inheritance and to estimate the genetic parameters of milk flow of Swiss dairy cattle. The data consisted of 204,397, 655,989 and 40,242 lactation records of milk flow in Brown Swiss, Simmental and Holstein cattle, respectively (4 to 22 years). Separate genetic analyses of first and multiple lactations were carried out for each breed. The results show that genetic parameters especially polygenic variance and heritability of milk flow in the first lactation were very similar under both mixed inheritance (polygenes + major gene) and polygenic models. Segregation analyses yielded very low major gene variances which favour the polygenic determinism of milk flow. Heritabilities and repeatabilities of milk flow in both Brown Swiss and Simmental were high (0.44 to 0.48 and 0.54 to 0.59, respectively). The heritability of milk flow based on scores of milking ability in Holstein was intermediate (0.25). Variance components and heritabilities in the first lactation were slightly larger than those estimates for multiple lactations. The results suggest that milk flow (the quantity of milk per minute of milking) is a relevant measurement to characterise the cows milking ability which is a good candidate trait to be evaluated for a possible inclusion in the selection objectives in dairy cattle.
Animals↗
Sirolimus population pharmacokinetic/pharmacogenetic analysis and bayesian modelling in kidney transplant recipients.
OBJECTIVES: The objectives of the present study were: (i) to analyse the population pharmacokinetics of sirolimus in renal transplant recipients co-administered mycophenolate mofetil, but no calcineurin inhibitor over the first 3 months post-transplantation and study the influence of different potential covariates, including genetic polymorphisms of cytochrome P450 (CYP) metabolic enzymes and active transporters, on pharmacokinetic parameters; and (ii) to develop a Bayesian estimator able to reliably estimate the individual pharmacokinetic parameters and exposure indices in this population. METHODS: Twenty-two adult renal transplant patients treated with sirolimus participated in this study. Ninety concentration-time profiles (938 sirolimus whole blood samples) were collected at days 7 and 14, and months 1 and 3 post-transplantation. The population pharmacokinetic study was conducted using the nonlinear mixed effects model software, NONMEM, and validated using both the bootstrap and the cross-validation approaches. Finally, a Bayesian estimator based on a limited sampling strategy was built using the post hoc option. RESULTS: A two-compartment open model with first-order elimination and Erlang's distribution (to describe the absorption phase) best fitted the data. The mean pharmacokinetic parameter estimates were 5.25 h(-1), 218L and 292L for the transfer rate constant, the apparent volume of the central and peripheral compartments, respectively. The CYP3A5*1/*3 polymorphism significantly influenced the apparent oral clearance: mean oral clearance = 14.1 L/h for CYP3A5 non expressers (CYP3A5*3/*3 genotype) versus 28.3 L/h for CYP3A5 expressers (CYP3A5*1/*3 and *1/*1 genotypes). The standard errors of all the parameter estimates were <15%. Maximum a posteriori Bayesian forecasting allowed accurate prediction of sirolimus area under the concentration-time curve from 0 to 24 hours using a combination of only three sampling times (0, 1 and 3 hours post-dose), with a non-significant bias of -2.1% (range -22.2% to +25.9%), and a good precision (root mean square error = 10.3%). This combination is also easy to implement in clinical practice. CONCLUSION: This study presents an accurate population pharmacokinetic model showing the significant influence of the CYP3A5*1/*3 polymorphism on sirolimus apparent oral clearance, and a Bayesian estimator accurately predicting sirolimus pharmacokinetics in patients co-administered mycophenolate mofetil, but no calcineurin inhibitor.
Adult↗
Bayesian subset analysis.
As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models. Exchange-ability among the interactions is a key assumption; thus, the results are of interest primarily in the context of examining a collection of subsets with no definite a priori distinction relative to treatment effect. Exchangeability leads to a shrinking of the posterior distributions of the interaction terms toward the natural origin of 0, offsetting the tendency of the estimated effects to disperse. The method is applied to parameter estimates from a proportional hazards regression analysis of survival data from a clinical trial, invoking the approximate multivariate normal distribution of the estimates. No subjective prior distributions are required. Vague priors are used for all of the regression coefficients except the treatment-by-covariate interactions, which are assumed to follow a normal distribution.
Clinical Trials as Topic↗
Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis.
The Bayesian approach and several of its advantages in drug and medical device development are described. One advantage from the perspective of analysis is that it provides a methodology for synthesizing information. However, taking a Bayesian approach to designing clinical trials is potentially more valuable than using this approach in analyzing trial results. Bayesian methodology provides a mechanism for updating what is known as results accumulate during a trial. Such updating can be incorporated completely explicitly and prospectively. An important way in which the Bayesian approach can be used is in calculating the predictive probability distribution of future results on the basis of current results. I show how to exploit predictive distributions in adapting to results that accumulate during the course of a trial. Possible adaptations including decreasing or increasing sample size, dropping treatment arms, and modifying the randomization proportions to the various arms depending on the interim results. Consequences of taking a Bayesian approach to clinical trial design are efficiency, better treatment of patients in the trial, and greater precision regarding the primary endpoints. An example of the last of these is Bayesian modeling of the relationship between early and longer term endpoints. Such modeling also enables earlier decision making. Case studies 2 and 3 deal with trials that were shorter and smaller, respectively, because of such modeling.
Bayes Theorem↗
Phylogenetic analysis of honey bee behavioral evolution.
DNA sequences from three mitochondrial (rrnL, cox2, nad2) and one nuclear gene (itpr) from all 9 known honey bee species (Apis), a 10th possible species, Apis dorsata binghami, and three outgroup species (Bombus terrestris, Melipona bicolor and Trigona fimbriata) were used to infer Apis phylogenetic relationships using Bayesian analysis. The dwarf honey bees were confirmed as basal, and the giant and cavity-nesting species to be monophyletic. All nodes were strongly supported except that grouping Apis cerana with A. nigrocincta. Two thousand post-burnin trees from the phylogenetic analysis were used in a Bayesian comparative analysis to explore the evolution of dance type, nest structure, comb structure and dance sound within Apis. The ancestral honey bee species was inferred with high support to have nested in the open, and to have more likely than not had a silent vertical waggle dance and a single comb. The common ancestor of the giant and cavity-dwelling bees is strongly inferred to have had a buzzing vertical directional dance. All pairwise combinations of characters showed strong association, but the multiple comparisons problem reduces the ability to infer associations between states between characters. Nevertheless, a buzzing dance is significantly associated with cavity-nesting, several vertical combs, and dancing vertically, a horizontal dance is significantly associated with a nest with a single comb wrapped around the support, and open nesting with a single pendant comb and a silent waggle dance.
Animals↗
Cross-validation of a patient classification procedure: an application of the U method.
The objective of this article is to present the methods used for the validation of a patient classification system that was based on the concept of types of care (PCTC system). The PCTC system was developed to improve placement decisions for long-term care patients and also to provide information required for planning in the field of long-term care. A sample of long-term care patients was selected from various institutions/programs and the patients in the sample were assessed and classified by the program practitioners (users) as well as an independent panel (criterion team) composed of a physician, a nurse, and a social worker, using prototype forms specially designed for the project. An objective and empiric classification model was developed by applying discriminant analysis, Bayesian classification procedure, and cluster analysis techniques. The classification validity was evaluated by the use of the R, H, and U methods.
Adult↗
Effect of atypical antibiotic resistance on microorganism identification by pattern recognition.
We classified microorganisms from the clinical laboratory by using information provided by the Gram stain and antibiotic sensitivity profiles obtained with the Bauer-Kirby technique. Approximately 4,000 microorganisms, routinely identified and tested for antibiotic sensitivities in a large hospital microbiology laboratory, were used as a data set for several pattern recognition classification methods: K--nearest-neighbor analysis, statistical isolinear multicomponent analysis, Bayesian inference, and linear discriminant analysis. K--nearest-neighbor analysis yielded the highest prospective classification accuracy for gram-negative organisms, 90%. When those organisms displaying an atypical antibiotic resistance pattern were excluded from the data, the gram-negative classification accuracy improved to 95%. These results are inferior to currently accepted biochemical identification methods. Microorganisms with atypical antibiotic resistance patterns are likely to be misidentified and are common enough (17% of our isolates) to limit the feasibility of routine identification of microorganisms from their antibiotic sensitivities.
Anti-Bacterial Agents↗
Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny.
In plants and laboratory animals, QTL mapping is commonly performed using F(2) or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F(2) individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F(3) progeny derived from selfing F(2) plants in place of the F(2) phenotype itself. All F(3) progeny derived from the same F(2) plant belong to the same F(2:3) family, denoted by F(2:3). If the size of each F(2:3) family (the number of F(3) progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F(2) plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F(2) marker genotypes and F(3) average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F(2:3) strategy. We also propose to combine both the F(2) phenotypes and the F(2:3) average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F(2:3) families of heterozygous F(2) plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F(2) design, even if only a single F(3) progeny is collected from each F(2:3) family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.
Algorithms↗
Fall-prevention programs for the elderly: a Bayesian secondary meta-analysis.
A secondary meta-analysis of programs to reduce falls in the elderly is undertaken to demonstrate a Bayesian analysis. The Bayesian statistical tradition is carefully distinguished from the standard Neyman-Pearson-Wald (NPW) statistical tradition. In the 12 studies, the logit effect size is used to compare treatment groups using a prevention program to control groups without a program. To contrast the Bayesian analysis, independent-effects and fixed-effect meta-analyses are first conducted in the NPW tradition. This is followed by Bayesian independent-effects and fixed-effect meta-analyses that numerically replicate the NPW results but have conceptually different interpretations. The final analyses comprise Bayesian random-effects and predictive meta-analyses. These results differ numerically from all the previous meta-analyses and conceptually from the NPW meta-analyses. The random-effects analysis allows for heterogeneity in the effect sizes. The predictive analysis yields the distribution of a new, out-of-sample effect size, which accommodates not only the heterogeneity of the effects but also the imprecision in the parameter estimates. This last analysis shows that the effectiveness of new fall-prevention programs is less definitive than that found in the sample. Bayesian statistical methods are particularly well-suited for the complexities of nursing science studies.
Accidental Falls↗
Do antidepressants cause suicidality in children? A Bayesian meta-analysis.
BACKGROUND: To quantify the risk of suicidal behavior/ideation (suicidality) for children who use antidepressants, the FDA collected randomized placebo-controlled trials of antidepressant efficacy in children. Although none of the 4487 children completed suicide, 1.7% exhibited suicidality. The FDA meta-analyzed these studies and found sufficient evidence of an increased risk to require a black-box warning on antidepressants for children. PURPOSE: The FDA considered different drug formulations and psychiatric diagnoses to be equivalent in their effect on suicidality. If this assumption does not hold, the FDA analysis may have underestimated the variance of the risk estimate. We investigate the consequences of relaxing these assumptions. METHODS: We extend the FDA analysis using a Bayesian hierarchical model that allows for a study-level component of variability and facilitates extensive sensitivity analyses. RESULTS: We found an association between antidepressant use and an increased risk of suicidality in studies where the diagnosis was major depressive disorder (odds ratio 2.3 [1.3, 3.8]), and where the antidepressant was an SSRI (odds ratio 2.2 [1.3, 3.6]). We did not find evidence for such an association in the complement sets of trials. Although the results based on the hierarchical model are insensitive to model perturbations, the robustness of the FDA's meta-analysis to model assumptions is less clear. These data have limited generalizability due to exclusion of patients with baseline risk of suicide and the use of relatively short duration trials. CONCLUSIONS: Because of model specification and interpretation issues raised in this paper, we conclude that the evidence supporting a causal link between antidepressant use and suicidality in children is weak. The use of Bayesian hierarchical models for meta-analysis has facilitated the incorporation of potentially important sources of variability and the use of sensitivity analysis to assess the consequences of model specifications and their impact on important regulatory decisions.
Antidepressive Agents↗
Expert systems in psychiatry. A review.
Existing computer-based decision aids in the areas of psychiatric diagnosis and consultation are reviewed, and the prospects for expert system development within the mental health field are discussed. Emphasis is placed upon the decision-making models used in these systems rather than on their particular application area. The decision-making paradigms discussed are (1) data bank analysis, (2) statistical pattern recognition, (3) Bayesian analysis, (4) logical flow chart method, and (5) knowledge-based (expert system) approaches. For each paradigm, its essential features, its strengths and weaknesses, and some example applications are presented.
Diagnosis, Computer-Assisted↗
Pharmacokinetics of Ibuprofen in children with cystic fibrosis.
An exaggerated inflammatory response is responsible for the decline of lung function in patients with cystic fibrosis (CF). Ibuprofen is a potent anti-inflammatory agent that demonstrates inhibition of neutrophil activity in vitro at concentrations between 50 and 100 mg/L, whereas lower concentrations result in an increase in inflammatory mediators. Significant decline in the rate of deterioration of pulmonary function and increased nutritional status were observed in children with CF who were administered long-term high-dosage ibuprofen therapy. As with many other drugs, CF patients appear to exhibit altered pharmacokinetics of ibuprofen (reduced bioavailability, increased volume of distribution, and more rapid clearance) when compared with healthy controls. However, the absence of studies with intravenous ibuprofen as well as protein binding measurements in patients with CF currently limits the ability to compare the pharmacokinetics with those in other populations. Current studies indicate that there is high interpatient variability in ibuprofen pharmacokinetics among CF patients. Some of this variability can be explained by differences in ibuprofen formulation administered. Therapeutic drug monitoring of high-dosage ibuprofen therapy is recommended because of the biphasic response to inflammatory mediators demonstrated in vitro as well as the high interpatient variability in pharmacokinetics. Due to the differences in absorption characteristics between ibuprofen formulations, the timing of obtaining blood samples for pharmacokinetic analysis is critical. Maximum a posteriori Bayesian analysis has been shown to provide more accurate and precise estimates of the pharmacokinetic parameters of ibuprofen in children with CF, and may also be a useful tool to further investigate the relationship between measures of drug exposure and efficacy/toxicity outcomes.
Anti-Inflammatory Agents, Non-Steroidal↗
Spatial distribution of seropositivity to the causative agent of granulocytic ehrlichiosis in dogs in California.
OBJECTIVE: To assess spatial and temporal patterns of seroprevalence among dogs in California to the causative agent of granulocytic ehrlichiosis (GE). SAMPLE POPULATION: Sera of 1,082 clinically normal dogs from 54 of 59 counties in California in 1997 to 1998. PROCEDURES: Serum-specific IgG reactivity to Ehrlichia equi was assessed by use of an immunofluorescent antibody assay, using E. equi-infected horse neutrophils as substrate. Data were analyzed, using a geographic information system. Spatial analysis of seroprevalence included first order Bayesian analysis of seroprevalence and second order analysis of clustering by K-function and Cuzick-Edwards tests. Monthly seroprevalence among dogs was examined by use of regression on monthly densities of Ixodes pacificus adults and nymphs. RESULTS: Seroprevalence among dogs to E. equi was 8.68%. Data were seasonally bimodal with highest prevalence in winter (when adult ticks were abundant) and a secondary peak in late spring (corresponding to nymphal ticks). Humboldt County had the highest seroprevalence (47.3%), and other northern coast range counties had seroprevalence from 15 to 30%. CONCLUSIONS AND CLINICAL RELEVANCE: The patchy distribution of exposure to Ehrlichia organisms is a subset of the distribution of the tick vector. This may reflect enzootic cycles or climatic or historical factors that limited the range of the disease. Dogs, horses, and humans from north coast range counties in California are at increased risk of GE. These data provide a background for assessing risk of infection in horses and dogs, depending on geographic location. Dogs may be sentinels for assessing risk of GE in humans.
Animals↗
Bayesian versus Fourier spectral analysis of ion cyclotron resonance time-domain signals.
The frequency-domain spectrum obtained by Fourier transformation (FT) of a time-domain signal is accurate only for a continuous noiseless time-domain signal of infinite duration. For discrete noisy truncated time-domain signals, non-FT (e.g., Bayesian analysis) methods may provide more accurate spectral estimates of time-domain signal frequencies, relaxation time(s), and relative abundances. In this paper, we show that Bayesian analysis of simulated and experimental ion cyclotron resonance (ICR) time-domain noisy signals can produce a spectrum with mass accuracy improved by a factor of 10 or more over that obtained from a magnitude-mode discrete fast Fourier transform (FFT) spectrum. Moreover, Bayesian analysis offers the useful advantage that it automatically estimates the precision of its iteratively determined spectral parameters. The main disadvantage of Bayesian analysis is its lengthy computation time compared to that of FFT (hours vs seconds on the same hardware for approximately 4K time-domain data points); the Bayesian computation time increases rapidly with the number of spectral peaks and (less rapidly) with the number of time-domain data points. Bayesian analysis should thus prove useful for those FT/ICR applications involving relatively few data points and/or requiring high mass accuracy.
Bayes Theorem↗
Multivariate analysis of cardiovascular reflexes applied to the diagnosis of autonomic neuropathy.
A battery of cardiovascular reflex tests is usually performed for the diagnosis of autonomic neuropathy. The tests discriminate well between normal and definitely abnormal autonomic function. However, in some patients the results are borderline and their autonomic status cannot be better defined. We performed multivariate statistical analysis of six cardiovascular autonomic tests with the aim of increasing their diagnostic efficiency. Eighty-five healthy subjects and 95 patients at risk for autonomic neuropathy were studied. Principal component analysis and two pattern recognition methods, the Bayesian technique and the SIMCA method, were applied. It was found that: (1) normal models obtained by Bayesian analysis showed very high specificity and sensitivity; (2) a battery of two tests for parasympathetic function (R-R interval variation test, deep breathing) and two tests for sympathetic function (blood pressure responses to standing and to sustained handgrip) provide an appropriate diagnostic approach, if multivariate analysis is used; (3) multivariate analysis allows a more precisely defined assessment of autonomic nervous system function in so-called borderline patients.
Adult↗