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PubMed1992

[Data analysis by statistical models].

The basic idea for the realization of effective statistical data analysis is illustrated with an example. The use of statistical models is explained and the feasibility of objective comparison of the models by an information criterion AIC is demonstrated. Further, the possibility of practical use of Bayesian models for complex data analysis is explained. Finally, the necessity of cooperation between the experts of respective fields and statisticians for further development of statistical data analysis is mentioned.

Adult↗

PubMed1995

[Cytotoxic chemotherapy in elderly patients: present and future].

Cancer in elderly people accounts for more than 50% of the malignant tumors treated per year in France and this population of patients has a rather high-life expectancy. Chemotherapy is active in these elderly patients but clearly more toxic than for young ones. The general tendency among the physicians to empirically reduce the doses is due to the known increased risk of unexpected toxicities. That is why there is such a large variety of conflicting opinions in the literature concerning the benefit and toxic effects of cytostatic drugs in the elderly. Therefore, it appears consistent to adjust chemotherapy regimen according to physiological criteria. Among them is biological age which is a better parameter than chronological age to describe the biological heterogeneity of this population of patients. Nakamura et al have published an interesting model for the calculation of biological age by principal component analysis using 11 easily measurable biological and clinical variables in a series of healthy elderly people. This kind of approach is not at present available for cancer patients but it allows to demonstrate that the chronological age is only one among many other age-related variables and is not sufficient to fully describe it. The variations in pharmacokinetic data are more frequent in the elderly than in younger people and this reflects age-related physiological heterogeneity. This factor is well taken into account in recently described population pharmacokinetic models, bayesian fittings and adaptative control which may represent promising approaches of cytostatics dose adjustments. Such models have been successfully developed in young patients receiving doxorubicin, methotrexate, melphalan and teniposide. They require a low number of blood samples to determine individual parameters and further adjust the doses, and are therefore of potential interest in old patients. Prospective studies are warranted in the future in order to recommend their use in the elderly.

Aged↗

PubMed2001

Using Bayesian inference to perform meta-analysis.

Bayesian modeling offers an elegant approach to meta-analysis that efficiently incorporates all sources of variability and relevant quantifiable external information. It provides a more informative summary of the likely value of parameters after observing the data than do non-Bayesian approaches. This leads to direct probabilistic inference about model parameters such as the average treatment effect, the between-study variance, and individual study treatment effects. The latter are weighted averages of the common mean and individual study means with weights reflecting the amount of information provided by each study relative to the others. Homogeneity among these posterior study estimates indicates that pooling these studies is appropriate; heterogeneity suggests that some cause of between-study variation should be explored. The author describes the construction of such models and shows how to use them to estimate a common mean and regression slopes. Two examples illustrate the additional inferences available with the Bayesian methodology.

Angiotensin-Converting Enzyme Inhibitors↗

PubMed1996

Bayesian inference for hierarchical mixtures-of-experts with applications to regression and classification.

This paper studies the problems of inference and prediction in a class of models known as hierarchical mixtures-of-experts (HME). The statistical model underlying an HME is a mixture model in which both the mixture coefficients and the mixture components are generalized linear models. Bayesian inference regarding an HME's parameters is presented in the contexts of regression and classification using Markov chain Monte Carlo methods. A benefit of this Bayesian approach is the ability to obtain a sample from the posterior distribution of any functional of the parameters of the given model. In this way, more information is obtained than provided by a point estimate. The methods are illustrated on a nonlinear regression problem and on a breast cancer classification problem. The results indicate that the HME showed good prediction performance, and also gave the additional benefit of providing for the opportunity to assess the degree of certainty of the model in its predictions.

Algorithms↗

PubMed1989

A Bayesian methodology for scaling radiation studies from animals to man.

This paper describes a Bayesian methodology for integrating studies in experimental animals and humans to obtain a risk estimate for a radionuclide for which no data or very limited human data are available. The method is quite general and is not limited to radiation studies. In fact, it was first developed for chemical toxicants. The methodology is illustrated using studies with rats, beagles, and humans exposed to isotopes of Ra and Pu. The goal is a quantitative risk estimate for bone cancer in humans exposed to internally deposited Pu. The choice of bone cancer as an end point and of Pu as the source of exposure was made partially because of its inherent interest but also because of issues of data availability and suitability. We performed Poisson regression analyses on 13 of 15 data sets. These analyses form the basis for the unifying method of interpreting the entire ensemble of studies. Each of the studies is summarized by the estimated dose-response slope and its estimated standard error. These summary statistics are combined with other available biological and physical information about species differences, physical and metabolic characteristics of isotopes, disease mechanisms, and the like. This information enters the analysis in the form of prior assumptions about the parameters of the Bayesian model combining the studies. The posterior distribution for the bone cancer rate in man from the Bayesian analysis of the 13 studies is updated with the limited data on Pu in humans. This update gives the final probability density for the bone cancer rate in humans exposed to internally deposited Pu. This density has a median of about three cancers per 100 Gy and has a 95% probability interval from 0.8 to 11 bone cancers per 100 Gy.

Animals↗

PubMed2000

A study of early stopping and model selection applied to the papermaking industry.

This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.

Algorithms↗

PubMed1987

Bayesian statistical theory in the preoperative diagnosis of pulmonary lesions.

We used a computerized Bayesian algorithm to assist in the preoperative diagnosis of pulmonary lesions. One hundred consecutive patients who were undergoing exploratory thoracotomy for newly discovered pulmonary lesions were prospectively evaluated. The Bayesian model used a total of 44 preoperative clinical and roentgenographic factors to categorize the lesions as benign or malignant. The Bayesian algorithm correctly categorized 96 of the 100 lesions, thereby providing an accuracy of 96 percent. The sensitivity of the model was 98 percent and the specificity was 87 percent. All but two of the 85 malignant lesions were correctly categorized and 13 of the 15 benign lesions were correctly analyzed by the model. These results indicate that computer-assisted diagnosis using the Theorem of Bayes may provide valuable preoperative information for the management of selected patients.

Adolescent↗

PubMed1999

All maps of parameter estimates are misleading.

Maps are frequently used to display spatial distributions of parameters of interest, such as cancer rates or average pollutant concentrations by county. It is well known that plotting observed rates can have serious drawbacks when sample sizes vary by area, since very high (and low) observed rates are found disproportionately in poorly-sampled areas. Unfortunately, adjusting the observed rates to account for the effects of small-sample noise can introduce an opposite effect, in which the highest adjusted rates tend to be found disproportionately in well-sampled areas. In either case, the maps can be difficult to interpret because the display of spatial variation in the underlying parameters of interest is confounded with spatial variation in sample sizes. As a result, spatial patterns occur in adjusted rates even if there is no spatial structure in the underlying parameters of interest, and adjusted rates tend to look too uniform in areas with little data. We introduce two models (normal and Poisson) in which parameters of interest have no spatial patterns, and demonstrate the existence of spatial artefacts in inference from these models. We also discuss spatial models and the extent to which they are subject to the same artefacts. We present examples from Bayesian modelling, but, as we explain, the artefacts occur generally.

Bayes Theorem↗

PubMed1987

The "constant intake rate" assumption in interim recruitment goal methodology for multicenter clinical trials.

A primary concern of any multihospital clinical trial is the recruitment of a predetermined number of patients during a prespecified interval of time. In several recent papers a Poisson based model was used to estimate the time needed to recruit a predetermined number of patients and the probabilities of recruiting specified fractions of the sample during subintervals. The Poisson model requires the assumption that patients be recruited at a constant rate over the entire length of the interval. In this paper we test the adequacy of this model and assumption using patient intake data from nine multihospital VA clinical trials and propose an alternative Bayesian model.

Bayes Theorem↗

PubMed2001

Estimating the Bayesian loss function. A conjoint analysis approach.

Current health economic literature does not provide clear guidelines on how uncertainty around cost-effectiveness estimates should be incorporated into economic decision models. Bayesian analysis is a promising alternative to classical statistics for incorporating uncertainty in economic analysis. Estimating a loss function that relates outcomes to societal welfare is a key component of Bayesian decision analysis. Health economists commonly compute the loss function based on the quality-adjusted life-years associated with each outcome. However, if welfare economics is adopted as the theoretical foundation of the analysis, a loss function based in cost-benefit analysis (CBA) may be more appropriate. CBA has not found wide use in health economics due to practical issues associated with estimating such a loss function. In this paper, we present a method based in conjoint analysis for estimating the CBA loss function that can be applied in practice. We illustrate the use of the methodology using data from a pilot study.

Acute Disease↗

PubMed1992

Ranitidine pharmacokinetics and adverse central nervous system reactions.

BACKGROUND: Treatment with histamine2-receptor antagonists has been associated with adverse central nervous system reactions (CNS-ADRs). Previous studies of cimetidine have shown an association between CNS-ADRs and high cimetidine drug levels. While case reports of ranitidine CNS-ADRs have appeared, we wanted to study a series of patients, some of whom were critically ill, for the presence of CNS-ADRs and to correlate these with ranitidine pharmacokinetics. METHODS: A prospective, observational, open study included 163 consecutive patients, of whom 41 met entry criteria. A nonlinear least-squares regression analysis was used to establish a ranitidine pharmacokinetic dosing model. Ranitidine levels were determined by a high-performance liquid chromatographic assay. Individual ranitidine pharmacokinetics were determined by means of a bayesian model. Observations on 13 possible CNS-ADRs were recorded. The CNS-ADRs were evaluated by the Naranjo rating system. RESULTS: Ranitidine-associated CNS-ADRs, particularly lethargy, confusion, somnolence, and disorientation, occurred more frequently in patients with renal function impairment, and these were associated with higher peak concentrations, average plasma concentrations, and area under the curve. CONCLUSIONS: Ranitidine, when given in conventional doses, can cause CNS-ADRs, particularly in older patients who have substantial renal function impairment. These CNS-ADRs occur as a consequence of altered ranitidine disposition. Ranitidine doses should be reduced when renal function impairment is present, and patients should be carefully observed for CNS-ADRs.

Aged↗

PubMed2002

Bayesian infinite mixture model based clustering of gene expression profiles.

MOTIVATION: The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. RESULTS: We developed a clustering procedure based on the Bayesian infinite mixture model and applied it to clustering gene expression profiles. Clusters of genes with similar expression patterns are identified from the posterior distribution of clusterings defined implicitly by the stochastic data-generation model. The posterior distribution of clusterings is estimated by a Gibbs sampler. We summarized the posterior distribution of clusterings by calculating posterior pairwise probabilities of co-expression and used the complete linkage principle to create clusters. This approach has several advantages over usual clustering procedures. The analysis allows for incorporation of a reasonable probabilistic model for generating data. The method does not require specifying the number of clusters and resulting optimal clustering is obtained by averaging over models with all possible numbers of clusters. Expression profiles that are not similar to any other profile are automatically detected, the method incorporates experimental replicates, and it can be extended to accommodate missing data. This approach represents a qualitative shift in the model-based cluster analysis of expression data because it allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles. We also demonstrated the importance of incorporating the information on experimental variability into the clustering model. AVAILABILITY: The MS Windows(TM) based program implementing the Gibbs sampler and supplemental material is available at http://homepages.uc.edu/~medvedm/BioinformaticsSupplement.htm CONTACT: medvedm@email.uc.edu

Bayes Theorem↗

PubMed2000

Bayesian nonparametric inference on the dose level with specified response rate.

The richness of nonparametric Bayesian models has attracted many different applications. Its application in dose-finding studies has been hindered due to lack of methodologies on the nonparametric Bayesian inference on percentiles. The primary interest in dose-finding studies focuses inference on the unknown toxicity or efficacy dose level corresponding to a prespecified rate. This paper shows how this problem may generally be handled by deriving inference on percentiles of a distribution following a Dirichlet process prior. In particular, theoretical results are derived to obtain the nonparametric Bayesian inference of the unknown dose level. This is followed by a description of the numerical implementation of that theory. The method also allows efficient estimation of the entire potency curve. Finally, the usefulness of the approach is demonstrated via an experimental data example.

Animals↗

PubMed2000

Comparing the performance of two indices for spatial model selection: application to two mortality data.

The statistical analysis of spatially correlated data has become an important scientific research topic lately. The analysis of the mortality or morbidity rates observed at different areas may help to decide if people living in certain locations are considered at higher risk than others. Once the statistical model for the data of interest has been chosen, further effort can be devoted to identifying the areas under higher risks. Many scientists, including statisticians, have tried the conditional autoregressive (CAR) model to describe the spatial autocorrelation among the observed data. This model has greater smoothing effect than the exchangeable models, such as the Poisson gamma model for spatial data. This paper focuses on comparing the two types of models using the index LG, the ratio of local to global variability. Two applications, Taiwan asthma mortality and Scotland lip cancer, are considered and the use of LG is illustrated. The estimated values for both data sets are small, implying a Poisson gamma model may be favoured over the CAR model. We discuss the implications for the two applications respectively. To evaluate the performance of the index LG, we also compute the Bayes factor, a Bayesian model selection criterion, to see which model is preferred for the two applications and simulation data. To derive the value of LG, we estimate its posterior mode based on samples derived from the BUGS program, while for Bayes factor we use the double Laplace-Metropolis method, Schwarz criterion, and a modified harmonic mean for approximations. The results of LG and Bayes factor are consistent. We conclude that LG is fairly accurate as an index for selection between Poisson gamma and CAR model. When easy and fast computation is of concern, we recommend using LG as the first and less costly index.

Asthma↗

PubMed2002

The development and application of a multilevel decision analysis model for the remediation of contaminated groundwater under uncertainty.

A study was initiated which combined elements of stochastic hydrology, risk assessment, simulation modeling, cost analysis and decision making to define the optimum remediation choice(s) for a Superfund site in the southern United States. The effort focused upon the premise that groundwater remediation is inherently complex due to uncertainties in the geological matrix as well as in contaminant concentrations at points of compliance and/or exposure. The technical analyst should supply the decision maker with estimates of these uncertainties as well as the cost penalties required to reduce them to manageable levels. Monte Carlo transport modeling was employed to define the probability of contaminant excursions from the site, while geostatistical simulation identified a joint plume configuration and its attendant probability. Bayesian modeling was used to define the worth of additional data. These individual components were combined within a Decision Model to identify optimum remediation configurations for a given levels of risk tolerance which could be supplied by the decision maker or affected community. Sensitivity analyses were conducted to define ranges over which the decision would not be affected by variation in the respective decision parameter.

Decision Making↗

PubMed2000

Bayesian detection and modeling of spatial disease clustering.

Many current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about specific models for spatial clustering. The proposed methodology incorporates ideas from image analysis, from Bayesian model averaging, and from model selection. With our approach, we obtain estimates for disease rates and allow for greater flexibility in both the type of clusters and the number of clusters that may be considered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data.

Bayes Theorem↗

PubMedDate not supplied

Disease mapping models: an empirical evaluation. Disease Mapping Collaborative Group.

The analysis of small area disease incidence has now developed to a degree where many methods have been proposed. However, there are few studies of the relative merits of the methods available. While many Bayesian models have been examined with respect to prior sensitivity, it is clear that wider comparisons of methods are largely missing from the literature. In this paper we present some preliminary results concerning the goodness-of-fit of a variety of disease mapping methods to simulated data for disease incidence derived from a range of models. These simulated models cover simple risk gradients to more complex true risk structures, including spatial correlation. The main general results presented here show that the gamma-Poisson exchangeable model and the Besag, York and Mollie (BYM) model are most robust across a range of diverse models. Mixture models are less robust. Non-parametric smoothing methods perform badly in general. Linear Bayes methods display behaviour similar to that of the gamma-Poisson methods.

Algorithms↗

PubMed2003

Estimating allelic number and identity in state of QTLs in interconnected families.

When multiple related families derived from inbred lines are jointly analysed to detect quantitative trait loci (QTLs), the analysis should estimate allelic effects as accurately as possible and estimate the probability that different parents carry alleles that are identical in state. Analyses exist that assume that all parents carry unique alleles or that all parents but one carry the same allele. In practice, many configurations are possible that group different parents according to their identity-in-state condition at a putative QTL allele. Here, we propose a variable model Bayesian analysis that selects among possible identity-in-state configurations and jointly estimates the allelic effects of identical-in-state parents. We contrast this analysis with a fixed model analysis that estimates unique allelic effects for all parents. We analyse two simulated mating designs: an experimental design in which three inbred parents were crossed to generate two families of 150 doubled haploid lines; and a breeding design in which 20 inbred parents were crossed to generate 60 families of 20 doubled haploid lines, with each parent contributing to six families. In all cases where some parents were simulated to carry alleles of identical effect (that is, they were identical in state), the variable analysis estimated allelic effects with lower mean-squared error than the fixed analysis. The variable analysis showed that, unless each family contains many individuals (more than 100), there is insufficient information in DNA-marker and phenotypic data to determine with high probability the QTL allelic number.

Bayes Theorem↗