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PubMed2006

Structural and functional properties of genes involved in human cancer.

BACKGROUND: One of the main goals of cancer genetics is to identify the causative elements at the molecular level leading to cancer. RESULTS: We have conducted an analysis of a set of genes known to be involved in cancer in order to unveil their unique features that can assist towards the identification of new candidate cancer genes. CONCLUSION: We have detected key patterns in this group of genes in terms of the molecular function or the biological process in which they are involved as well as sequence properties. Based on these features we have developed an accurate Bayesian classification model with which human genes have been scored for their likelihood of involvement in cancer.

Amino Acid Sequence↗
PubMed2006

Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK).

Recent advances in disease mapping have focused first on including the time dimension, thus giving rise to spatio-temporal analysis of the variation of disease risk and, secondly, on carrying out joint analysis of two diseases that share common environmental risk factors and are, therefore, related. Here, we try to combine both issues and present a joint analysis of the spatio-temporal variation of the risks of two related diseases processes-male and female lung cancer incidence-in a region of England. To do so, we use a Bayesian hierarchical model that splits the risk of disease into two spatio-temporal components: a shared component and a specific component that calibrates the differential between the two diseases.

Bayes Theorem↗
PubMed2004

Statistical evaluation of learning curve effects in surgical trials.

Randomized controlled trials (RCTs) in surgery have been impeded by concerns that improvements in the technical performance of a new technique over time (a "learning curve") may distort comparisons. The statistical assessment of learning curves in trials has received little attention. In this paper, we discuss what a learning curve effect is, the factors which effect it, how to display it, and how to incorporate the learning effect into the trial analysis. Bayesian hierarchical models are proposed to adjust the trial results for the existence of a learning curve effect. The implications for trial evaluation and data collection are considered.

Bayes Theorem↗
PubMedDate not supplied

A brief primer on automated signal detection.

BACKGROUND: Statistical techniques have traditionally been underused in spontaneous reporting systems used for postmarketing surveillance of adverse drug events. Regulatory agencies, pharmaceutical companies, and drug monitoring centers have recently devoted considerable efforts to develop and implement computer-assisted automated signal detection methodologies that employ statistical theory to enhance screening efforts of expert clinical reviewers. OBJECTIVE: To provide a concise state-of-the-art review of the most commonly used automated signal detection procedures, including the underlying statistical concepts, performance characteristics, and outstanding limitations, and issues to be resolved. DATA SOURCES: Primary articles were identified by MEDLINE search (1965-December 2002) and through secondary sources. STUDY SELECTION AND DATA EXTRACTION: All of the articles identified from the data sources were evaluated and all information deemed relevant was included in this review. DATA SYNTHESIS: Commonly used methods of automated signal detection are self-contained and involve screening large databases of spontaneous adverse event reports in search of interestingly large disproportionalities or dependencies between significant variables, usually single drug-event pairs, based on an underlying model of statistical independence. The models vary according to the underlying model of statistical independence and whether additional mathematical modeling using Bayesian analysis is applied to the crude measures of disproportionality. There are many potential advantages and disadvantages of these methods, as well as significant unresolved issues related to the application of these techniques, including lack of comprehensive head-to-head comparisons in a single large transnational database, lack of prospective evaluations, and the lack of gold standard of signal detection. CONCLUSIONS: Current methods of automated signal detection are nonclinical and only highlight deviations from independence without explaining whether these deviations are due to a causal linkage or numerous potential confounders. They therefore cannot replace expert clinical reviewers, but can help them to focus attention when confronted with the difficult task of screening huge numbers of drug-event combinations for potential signals. Important questions remain to be answered about the performance characteristics of these methods. Pharmacovigilance professionals should take the time to learn the underlying mathematical concepts in order to critically evaluate accumulating experience pertaining to the relative performance characteristics of these methods that are incompletely defined.

Automation↗
PubMed2005

PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.

A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and "background" intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markov-chain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motif-finding algorithms, including algorithms that also take phylogeny into account. Our results also show that, in contrast to the other algorithms, PhyloGibbs can make realistic estimates of the reliability of its predictions. Our tests suggest that, running on the five-species multiple alignment of a single gene's upstream region, PhyloGibbs on average recovers over 50% of all binding sites in S. cerevisiae at a specificity of about 50%, and 33% of all binding sites at a specificity of about 85%. We also tested PhyloGibbs on collections of multiple alignments of intergenic regions that were recently annotated, based on ChIP-on-chip data, to contain binding sites for the same TF. We compared PhyloGibbs's results with the previous analysis of these data using six other motif-finding algorithms. For 16 of 21 TFs for which all other motif-finding methods failed to find a significant motif, PhyloGibbs did recover a motif that matches the literature consensus. In 11 cases where there was disagreement in the results we compiled lists of known target genes from the literature, and found that running PhyloGibbs on their regulatory regions yielded a binding motif matching the literature consensus in all but one of the cases. Interestingly, these literature gene lists had little overlap with the targets annotated based on the ChIP-on-chip data. The PhyloGibbs code can be downloaded from http://www.biozentrum.unibas.ch/~nimwegen/cgi-bin/phylogibbs.cgi or http://www.imsc.res.in/~rsidd/phylogibbs. The full set of predicted sites from our tests on yeast are available at http://www.swissregulon.unibas.ch.

Algorithms↗
PubMed2007

Analysis of gene networks for drug target discovery and validation.

Understanding responses of the cellular system for a dosing molecule is one of the most important problems in pharmacogenomics. In this chapter, we describe computational methods for identifying and validating drug target genes based on the gene networks estimated from microarray gene expression data. We use two types of microarray gene expression data: gene disruptant microarray data and time-course drug response microarray data. For this purpose, the information of gene networks plays an essential role and is unattainable from clustering methods, which are the standard for gene expression analysis. The gene network is estimated from disruptant microarray data by the Bayesian network model, and then the proposed method automatically identifies sets of genes or gene regulatory pathways affected by the drug. We use an actual example from analysis of Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information toward drug target discovery.

Antifungal Agents↗
PubMed2006

Cost-effectiveness of self-managed versus physician-managed oral anticoagulation therapy.

BACKGROUND: Patient self-management of long-term oral anticoagulation therapy is an effective strategy in a number of clinical situations, but it is currently not a funded option in the Canadian health care system. We sought to compare the incremental cost and health benefits of self-management with those of physician management from the perspective of the Canadian health care payer over a 5-year period. METHODS: We developed a Bayesian Markov model comparing the costs and quality-adjusted life years (QALYs) accrued to patients receiving oral anticoagulation therapy through self-management or physician management for atrial fibrillation or for a mechanical heart valve. Five health states were defined: no events, minor hemorrhagic events, major hemorrhagic events, thrombotic events and death. Data from published literature were used for transition probabilities. Canadian 2003 costs were used, and utility estimates were obtained from various published sources. RESULTS: Self-management resulted in 3.50 fewer thrombotic events, 0.78 fewer major hemorrhagic events and 0.12 fewer deaths per 100 patients than physician management. The average discounted incremental cost of self-management over physician management was found to be 989 dollars (95% confidence interval [CI] 310 dollars-1655 dollars) per patient and the incremental QALYs gained was 0.07 (95% CI 0.06-0.08). The cost-effectiveness of self-management was 14,129 dollars per QALY gained. There was a 95% chance that self-management would be cost-effective at a willingness to pay of 23,800 dollars per QALY. Results were robust in probabilistic and deterministic sensitivity analyses. INTERPRETATION: This model suggests that self-management is a cost-effective strategy for those receiving long-term oral anticoagulation therapy for atrial fibrillation or for a mechanical heart valve.

Administration, Oral↗
PubMed2006

Linking chronic wasting disease to mule deer movement scales: a hierarchical Bayesian approach.

Observed spatial patterns in natural systems may result from processes acting across multiple spatial and temporal scales. Although spatially explicit data on processes that generate ecological patterns, such as the distribution of disease over a landscape, are frequently unavailable, information about the scales over which processes operate can be used to understand the link between pattern and process. Our goal was to identify scales of mule deer (Odocoileus hemionus) movement and mixing that exerted the greatest influence on the spatial pattern of chronic wasting disease (CWD) in northcentral Colorado, USA. We hypothesized that three scales of mixing (individual, winter subpopulation, or summer subpopulation) might control spatial variation in disease prevalence. We developed a fully Bayesian hierarchical model to compare the strength of evidence for each mixing scale. We found strong evidence that the finest mixing scale corresponded best to the spatial distribution of CWD infection. There was also evidence that land ownership and habitat use play a role in exacerbating the disease, along with the known effects of sex and age. Our analysis demonstrates how information on the scales of spatial processes that generate observed patterns can be used to gain insight when process data are sparse or unavailable.

Animal Migration↗
PubMed2004

Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation.

The aim of this review is to analyse critically the recent literature on the clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplant recipients. Dosage and target concentration recommendations for tacrolimus vary from centre to centre, and large pharmacokinetic variability makes it difficult to predict what concentration will be achieved with a particular dose or dosage change. Therapeutic ranges have not been based on statistical approaches. The majority of pharmacokinetic studies have involved intense blood sampling in small homogeneous groups in the immediate post-transplant period. Most have used nonspecific immunoassays and provide little information on pharmacokinetic variability. Demographic investigations seeking correlations between pharmacokinetic parameters and patient factors have generally looked at one covariate at a time and have involved small patient numbers. Factors reported to influence the pharmacokinetics of tacrolimus include the patient group studied, hepatic dysfunction, hepatitis C status, time after transplantation, patient age, donor liver characteristics, recipient race, haematocrit and albumin concentrations, diurnal rhythm, food administration, corticosteroid dosage, diarrhoea and cytochrome P450 (CYP) isoenzyme and P-glycoprotein expression. Population analyses are adding to our understanding of the pharmacokinetics of tacrolimus, but such investigations are still in their infancy. A significant proportion of model variability remains unexplained. Population modelling and Bayesian forecasting may be improved if CYP isoenzymes and/or P-glycoprotein expression could be considered as covariates. Reports have been conflicting as to whether low tacrolimus trough concentrations are related to rejection. Several studies have demonstrated a correlation between high trough concentrations and toxicity, particularly nephrotoxicity. The best predictor of pharmacological effect may be drug concentrations in the transplanted organ itself. Researchers have started to question current reliance on trough measurement during therapeutic drug monitoring, with instances of toxicity and rejection occurring when trough concentrations are within 'acceptable' ranges. The correlation between blood concentration and drug exposure can be improved by use of non-trough timepoints. However, controversy exists as to whether this will provide any great benefit, given the added complexity in monitoring. Investigators are now attempting to quantify the pharmacological effects of tacrolimus on immune cells through assays that measure in vivo calcineurin inhibition and markers of immunosuppression such as cytokine concentration. To date, no studies have correlated pharmacodynamic marker assay results with immunosuppressive efficacy, as determined by allograft outcome, or investigated the relationship between calcineurin inhibition and drug adverse effects. Little is known about the magnitude of the pharmacodynamic variability of tacrolimus.

Drug Interactions↗
PubMed2006

Methodological aspects in the assessment of treatment effects in observational health outcomes studies.

Prospective observational studies, which provide information on the effectiveness of interventions in natural settings, may complement results from randomised clinical trials in the evaluation of health technologies. However, observational studies are subject to a number of potential methodological weaknesses, mainly selection and observer bias. This paper reviews and applies various methods to control for selection bias in the estimation of treatment effects and proposes novel ways to assess the presence of observer bias. We also address the issues of estimation and inference in a multilevel setting. We describe and compare the use of regression methods, propensity score matching, fixed-effects models incorporating investigator characteristics, and a multilevel, hierarchical model using Bayesian estimation techniques in the control of selection bias. We also propose to assess the existence of observer bias in observational studies by comparing patient- and investigator-reported outcomes. To illustrate these methods, we have used data from the SOHO (Schizophrenia Outpatient Health Outcomes) study, a large, prospective, observational study of health outcomes associated with the treatment of schizophrenia. The methods used to adjust for differences between treatment groups that could cause selection bias yielded comparable results, reinforcing the validity of the findings. Also, the assessment of observer bias did not show that it existed in the SOHO study. Observational studies, when properly conducted and when using adequate statistical methods, can provide valid information on the evaluation of health technologies.

Bayes Theorem↗
PubMed2026

Genomic background of gestation length and calving-related traits in Holstein cattle.

The reproductive success of cows directly influences the profitability of dairy farms. Reproductive traits, particularly calving-related traits, generally have low heritability but sufficient additive genetic variance to enable genetic progress through genomic selection. Thus, the primary objectives of this study were to estimate genetic parameters and perform single-step genome-wide association studies (ssGWAS) for calf size, calving ease, gestation length, and stillbirth in Holstein cattle. Variance components were estimated based on animal models and Bayesian inference using a data set containing 226,717 animals with phenotypic records, 15,761 animals genotyped with 45,101 SNP markers, and 461,819 animals in the pedigree. SNP effects were estimated using the single-step GBLUP method. For direct and maternal genetic effects, heritability estimates (posterior standard deviation) ranged from 0.001 (0.002) for gestation length in heifers to 0.16 (0.001) for gestation length in cows. Genetic correlations ranged from -0.57 (0.01) between calving ease and stillbirth in heifers to 0.74 (0.01) between gestation length evaluated in heifers and cows. The ssGWAS results supported a highly polygenic architecture for calving-related traits, with most genomic signals not reaching genome-wide significance. A genome-wide significant association was detected for calving ease in cows on BTA23, highlighting FARS2 as a positional candidate gene. The strongest GWAS signals for each trait harbored additional biologically important candidate genes, including NPPA, NPPB, BCHE, EPHA4, DLD, and GTF2I. Given the generally low heritability estimates and the predominantly polygenic architecture observed for these traits, genomic selection may contribute to the genetic improvement of calving-related traits in Holstein cattle, with potential benefits for cow welfare, calf survival, and overall dairy production efficiency.

dairy cattle↗
PubMed2006

[Correlaton of mortality for lower urinary tract cancer and medical resource factor].

PURPOSE: To compare mortality among different populations, the standardized mortality ratio (SMR) is usually used. This study is investigated the relationships between health care resources and mortality for lower urinary tract cancer. MATERIALS AND METHODS: Data for medical resource factors were obtained from annual reports published by the Ministry of Health, Labor and Welfare, Japan. A Bayesian regression model was used to examine the relationships between SMR and these factors. The SMRs for prostate cancer and bladder cancer were calculated for the period from 2000 to 2002 and for 1990. RESULTS: When the number of urological hospitals increased to 1 per 100,000 person in a region, the influence of the SMR for prostate cancer was - 3.0 - 0.5%, the male bladder cancer SMR was - 0.2 - 1.8% and the female bladder cancer SMR was - 2.6 - 0.8%. These findings were not significant. CONCLUSION: The study shows that an appropriate distribution of urology resources is one of the factors contributing to a reduction in the mortality for urological cancer in a region. However, further increase of urological hospitals in a region exerts a limited beneficial influence on reducing the mortality.

Bayes Theorem↗
PubMed1999

Population pharmacokinetics of methotrexate in the guinea pig.

The population pharmacokinetics of an antitumoral and antiinflammatory agent, methotrexate (MTX), a folic acid antagonist, was studied in guinea pigs. Animals received an acute intraperitoneal injection of 0.25, 1 or 5 mg/kg MTX. Blood sampling was carried out for 12 hrs. after MTX administration and plasma drug concentrations were measured by fluorescence polarization immunoassay. The pharmacokinetic (PK) parameters were computed using the bayesian population model. MTX reached the level of detection at 3 hrs. for the animals injected with the lowest dose (0.25 mg/kg), at 3.5 hrs. for those animals which had the intermediate dose (1 mg/kg) and more than 6 hrs. for animals having received the highest dose (5 mg/kg). Each kinetic parameter (half life, total clearance - CLt, volume of distribution at steady state - VDSS, mean residence time - MRT - and area under curve - AUC) didn't show any significant difference between doses. MTX kinetic was linear for the first two doses (0.25 and 1 mg/kg MTX) and non-linear thereafter. MTX presented a one compartment distribution.

Animals↗
PubMedDate not supplied

A management tool for controlling the rate of non-acceptable inpatient hospital claims.

This paper demonstrates a tool for substantially improved monitoring of the validity of health insurance claims. Using a Bayesian regression model, we predict the probability of a non-acceptable claim (NAC) for each claim record and the expected number of NACs for any set of claims. When applied to a large set of hospital discharge claims, the tool shows a substantial improvement in the ability to estimate the actual number of NACs in the set. The tool permits ongoing monitoring of claims, more precise control, and a substantial reduction in audit cost in claims administration. It is conceptually applicable to other ongoing quality control systems, where inexpensively obtained information can be used to predict events that are costly to measure directly.

Bayes Theorem↗
PubMed2001

Constructing influence views from data to support dynamic decision making in medicine.

A dynamic decision model can facilitate the complicated decision-making process in medicine, in which both time and uncertainty are explicitly considered. In this paper, we address the problem of automatic construction of a dynamic decision model from a large medical database. Within the DynaMoL (a dynamic decision modeling language) framework, a model can be represented in influence view. Thus, our proposed approach first learns the structures of the influence view based on the minimal description length (MDL) principle, and then obtains the conditional probabilities of the model by Bayesian method. The experiment results demonstrate that our system can efficiently construct the influence views from data with high fidelity.

Algorithms↗
PubMed2002

Applying modern survival analysis methods to longitudinal dental caries studies.

Before the 1960s, tooth-specific caries risk was reported to be highest at 2 to 4 years after eruption. We studied the tooth-specific caries risk in three contemporary age cohorts in Finland. All together, 4072 boys and girls were followed annually from age 6 to age 18+ years in three age cohorts born in the 1960s, 1970s, and 1980s. We used a survival model and Bayesian inferential methods in the statistical analyses to establish the secular changes during this period. The analysis was based on the caries risk in individual teeth as a function of tooth age instead of summary measures, such as DMFS values. Our first finding was a marked overall decrease of caries. Moreover, analyses of the 1960 and 1970 cohorts revealed that the risk in molar teeth was highest immediately after eruption; in the youngest cohort, however, the risks of individual teeth were so low that no such dependencies on tooth age could be established.

Adolescent↗
PubMedDate not supplied

Is there regional variation in the SF-36 scores of Canadian adults?

BACKGROUND: Canadian normative data for the Medical Outcomes Study 36-item short form (SF-36) have recently been published. However, there is evidence from other countries to suggest that regional variation in health-related quality of life (HRQOL) may exist. We therefore examined the SF-36 data from nine Canadian centres for evidence of systematic differences. METHODS: Bayesian hierarchical modelling was used to compare the differences in the eight SF-36 domains and the two summary component scores within each of the age and gender strata across the nine sites. RESULTS: Five domains and the two summary component scores showed little clinically important variation. Other than a small number of exceptions, there was little overall evidence of HRQOL differences across most domains and across most sites. INTERPRETATION: Our finding of only a few small differences suggests that there is no need to develop region-specific Canadian normative data for the SF-36 health survey.

Adult↗
PubMed2005

An analysis of the costs of treating schizophrenia in Spain: a hierarchical Bayesian approach.

BACKGROUND: Health care decisions should incorporate cost of illness and treatment data, particularly for disorders such as schizophrenia with a high morbidity rate and a disproportionately low allocation of resources. Previous cost of illness analyses may have disregarded geographical aspects relevant for resource consumption and unit cost calculation. AIMS: To compare the utilisation of resources and the care costs of schizophrenic patients in four mental-health districts in Spain (in Madrid, Catalonia, Navarra and Andalusia), and to analyse factors that determine the costs and the differences between areas. METHODS: A treated prevalence bottom-up three year follow-up design was used for obtaining data concerning socio-demography, clinical evolution and the utilisation of services. 1997 reference prices were updated for years 1998-2000 in euros. We propose two different scenarios, varying in the prices applied. In the first (Scenario 0) the reference prices are those obtained for a single geographic area, and so the cost variations are only due to differences in the use of resources. In the second situation (Scenario 1), we analyse the variations in resource utilisation at different levels, using the prices applicable to each healthcare area. Bayesian hierarchical models are used to discuss the factors that determine such costs and the differences between geographic areas. RESULTS: In scenario 0, the estimated mean cost was 4918.948 euros for the first year. In scenario 1 the highest cost was in Gava (Catalonia) and the lowest in Loja (Andalusia). Mean costs were respectively 4547.24 and 2473.98 euros. With respect to the evolution of costs over time, we observed an increase during the second year and a reduction during the third year. Geographical differences appeared in follow-up costs. The variables related to lower treatment costs were: residence in the family household, higher patient age and being in work. On the contrary, the number of relapses is directly related to higher treatment costs. No differences were observed between health areas concerning resource utilisation. DISCUSSION: Calculating the costs of a given disease involves two principal factors: the resource utilisation and the prices. In most studies, emphasis is placed on the analysis of resource utilisation. Other evaluations, however, have recognized the implications of incorporating different prices into the final results. In this study we show both scenarios. The factors that determine the cost of schizophrenia for the Spanish case are similar to the factors encountered in studies carried out in other countries. IMPLICATIONS FOR HEALTH POLICIES: Treatment costs may be reduced by the prevention of psychotic symptoms and relapse. IMPLICATIONS FOR FUTURE RESEARCH: The use of the same price data in multicentre studies may not be realistic. More effort should be made to obtain price data from all the centres or countries participating in a study. In the present study, only direct healthcare and social costs have been included. Future research should consider informal and indirect costs.

Adult↗