Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “probabilistic modelling”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

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

At least 775 records · Page 43Linked to original sources
PubMedDate not supplied

Expected value of sample information calculations in medical decision modeling.

There has been an increasing interest in using expected value of information (EVI) theory in medical decision making, to identify the need for further research to reduce uncertainty in decision and as a tool for sensitivity analysis. Expected value of sample information (EVSI) has been proposed for determination of optimum sample size and allocation rates in randomized clinical trials. This article derives simple Monte Carlo, or nested Monte Carlo, methods that extend the use of EVSI calculations to medical decision applications with multiple sources of uncertainty, with particular attention to the form in which epidemiological data and research findings are structured. In particular, information on key decision parameters such as treatment efficacy are invariably available on measures of relative efficacy such as risk differences or odds ratios, but not on model parameters themselves. In addition, estimates of model parameters and of relative effect measures in the literature may be heterogeneous, reflecting additional sources of variation besides statistical sampling error. The authors describe Monte Carlo procedures for calculating EVSI for probability, rate, or continuous variable parameters in multi parameter decision models and approximate methods for relative measures such as risk differences, odds ratios, risk ratios, and hazard ratios. Where prior evidence is based on a random effects meta-analysis, the authors describe different ESVI calculations, one relevant for decisions concerning a specific patient group and the other for decisions concerning the entire population of patient groups. They also consider EVSI methods for new studies intended to update information on both baseline treatment efficacy and the relative efficacy of 2 treatments. Although there are restrictions regarding models with prior correlation between parameters, these methods can be applied to the majority of probabilistic decision models. Illustrative worked examples of EVSI calculations are given in an appendix.

Algorithms↗
PubMed2026

An Application of Iterative Health Economic Evaluation: An Update on the Early Cost Effectiveness of Whole-Genome Sequencing in Advanced Non-small-Cell Lung Cancer.

OBJECTIVE: Whole genome sequencing (WGS) can identify more druggable targets than the standard of care (SoC) panels, however, its health effects and costs are highly uncertain. Given the rapidly evolving treatment landscape and pricing, an iterative approach is crucial to continuously reassess evidence and adapt economic models. Our objective was to update a previously developed economic model for WGS. METHODS: We used a structured approach to identify and report model elements requiring updates, based on established tools and methodological guidance, and applied it to the probabilistic decision model by Simons et al.(2021), which compared SoC, WGS, and SoC followed by WGS in patients with inoperable stage IIIB, C/IV NSCLC in the Dutch setting. RESULTS: Updates included a new treatment (sotorasib), revised drug and diagnostic costs, and adherence to the latest guidelines. Drug and WGS diagnostics costs fell by 8% and 26%, respectively. SoC diagnostic prices increased by 17%. We explored the impact of the prevalence of druggable targets, effectiveness of off-label treatments, (academic-specific) diagnostic costs, and price negotiations. The ICER of WGS versus SoC decreased from €737,197 to €419,053/QALY. WGS would become cost-effective if diagnostic costs descended from €2,180 to €1,246 or if additional druggable targets were identified in ≥3.3% of patients. CONCLUSION: Our structured approach effectively identified items in the original analysis requiring updates and provides a foundation for further developing a checklist to guide iterative HTA. Continued monitoring and assessment of new treatment options, the dynamic diagnostics and costs throughout the life-cycle remain necessary to determine when WGS can be considered cost-effective.

NSCLC↗
PubMed2001

A stochastic model describes the heterogeneous pharmacokinetics of cyclosporin.

The pharmacokinetics of cyclosporin (CsA) are unusual because of several heterogeneous features which include the presence of more than one conformer, considerable accumulation in erythrocytes and lipoproteins, extensive plasma protein binding, distribution into deep tissues, biliary secretion and hepatic clearance involving a large number of metabolites. In this study, a stochastic compartmental model was developed to describe the heterogeneous elimination kinetics of CsA. This new approach relies on a probabilistic transfer model with a gamma distributed probability intensity coefficient for drug elimination. For comparative purposes both the stochastic model and compartmental deterministic models were fitted to real post infusion data from patients receiving CsA as a 2-hr intravenous infusion. The criteria for selecting the best model showed that the stochastic model, although simpler than the compartmental deterministic models, is more flexible and gives a better fit to the kinetic data of CsA than the compartmental deterministic models. The stochastic model with a random rate intensity coefficient adequately describes the heterogeneous pharmacokinetics of CsA.

Cyclosporine↗
PubMedDate not supplied

Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction.

In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in beta-sheets, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/-wild/bsm.html.

Algorithms↗
PubMed2003

A hidden Markov model for progressive multiple alignment.

MOTIVATION: Progressive algorithms are widely used heuristics for the production of alignments among multiple nucleic-acid or protein sequences. Probabilistic approaches providing measures of global and/or local reliability of individual solutions would constitute valuable developments. RESULTS: We present here a new method for multiple sequence alignment that combines an HMM approach, a progressive alignment algorithm, and a probabilistic evolution model describing the character substitution process. Our method works by iterating pairwise alignments according to a guide tree and defining each ancestral sequence from the pairwise alignment of its child nodes, thus, progressively constructing a multiple alignment. Our method allows for the computation of each column minimum posterior probability and we show that this value correlates with the correctness of the result, hence, providing an efficient mean by which unreliably aligned columns can be filtered out from a multiple alignment.

Algorithms↗
PubMed2003

Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo.

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.

Base Sequence↗
PubMed2006

Using hidden scale for salient object detection.

This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm.

Algorithms↗
PubMed2006

Distributed virtual experiments in water quality management.

Since the complexity of virtual experiments (VEs) and their underlying models is constantly increasing, computational performance of monolithic software solutions is rapidly becoming insufficient. Examples of VEs are probabilistic design, model calibration, optimal experimental design and scenario analysis. In order to tackle this computational bottleneck, a framework for the distributed execution of VEs on a potentially heterogeneous pool of work nodes has been implemented. This framework was named WDVE (WEST distributed virtual experimentation) and is built on top of technologies such as C++, XML and SOAP. It was designed for stability, expandability, performance, platform-independence and ease of use. Complex VEs are most often composed of mutually independent sub-experiments, which can be run concurrently. With WDVE, a complex VE that is executed on a so-called Master machine will therefore attempt to execute its sub-experiments on Slave machines that have previously registered with the Master. The process of submitting requests for the execution of sub-experiments is transparent and involves the transfer of a description of the experiment to be executed, and the resources that are needed for the execution (i.e., model and input data). WDVE is in many ways similar to the Grid Computing paradigm, which is currently receiving widespread attention. However, WDVE is more geared towards application within the scope of water quality management.

Calibration↗
PubMed2002

Bayesian phylogenetics using an RNA substitution model applied to early mammalian evolution.

We study the phylogeny of the placental mammals using molecular data from all mitochondrial tRNAs and rRNAs of 54 species. We use probabilistic substitution models specific to evolution in base paired regions of RNA. A number of these models have been implemented in a new phylogenetic inference software package for carrying out maximum likelihood and Bayesian phylogenetic inferences. We describe our Bayesian phylogenetic method which uses a Markov chain Monte Carlo algorithm to provide samples from the posterior distribution of tree topologies. Our results show support for four primary mammalian clades, in agreement with recent studies of much larger data sets mainly comprising nuclear DNA. We discuss some issues arising when using Bayesian techniques on RNA sequence data.

Animals↗
PubMed2006

Revised assessment of cancer risk to dichloromethane: part I Bayesian PBPK and dose-response modeling in mice.

The current USEPA cancer risk assessment for dichloromethane (DCM) is based on deterministic physiologically based pharmacokinetic (PBPK) modeling involving comparative metabolism of DCM by the GST pathway in the lung and liver of humans and mice. Recent advances in PBPK modeling include probabilistic methods and, in particular, Bayesian inference to quantitatively address variability and uncertainty separately. Although Bayesian analysis of human PBPK models has been published, no such efforts have been reported specifically addressing the mouse, apart from results included in the OSHA final rule on DCM. Certain aspects of the OSHA model, however, are not consistent with current approaches or with the USEPA's current DCM cancer risk assessment. Therefore, Bayesian analysis of the mouse PBPK model and dose-response modeling was undertaken to support development of an improved cancer risk assessment for DCM. A hierarchical population model was developed and prior parameter distributions were selected to reflect parameter values that were considered the most appropriate and best available. Bayesian modeling was conducted using MCSim, a publicly available software program for Markov Chain Monte Carlo analysis. Mean posterior values from the calibrated model were used to develop internal dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day in the lung and liver using exposure concentrations and results from the NTP mouse bioassay, consistent with the approach used by the USEPA for its current DCM cancer risk assessment. Internal dose metrics were 3- to 4-fold higher than those that support the current USEPA IRIS assessment. A decrease of similar magnitude was also noted in dose-response modeling results. These results show that the Bayesian PBPK model in the mouse provides an improved basis for a cancer risk assessment of DCM.

Animals↗
PubMed2004

Estimation of surface pose with a physically-based ultrasonic image model.

State-of-the-art approaches to shape analysis in medical images use a variety of sophisticated models for object shape. We have developed an image model that permits the application of these approaches to ultrasonic images, with detailed methods for representing rough surfaces. Our physically-based, probabilistic image model incorporates the combined effects of the system point-spread function (PSF), the tissue microstructure, and the gross tissue shape. At each image pixel, the amplitude mean and variance are computed directly from the model, characterizing the combined influence of shape, microstructure, and system PSF. Calculation of the SNR0 is used to further classify each pixel as Rayleigh- or non-Rayleigh-distributed. This characterization was used here to generate a data likelihood representing any set of images of a given surface by a probability density conditioned on the surface pose, or rotation and translation. The utility of this likelihood was demonstrated by applying maximum likelihood estimation to infer the pose of a cadaveric vertebra from simulated images of its surface. Successful results were achieved using derivative-based optimization algorithms for a data set of only three images. With a quasi-Newton BFGS algorithm, error in 15 of 20 trials was less than 0.4 degrees in rotation and 0.2 mm in translation. Estimation was inaccurate in only 1 of 20 trials. These results illustrate the potential of a physically-based image model in a rigorous approach to image analysis and also serve as an example of quantitative assessment of the model via performance in a specific application.

Algorithms↗
PubMed1994

Modeling protein cores with Markov random fields.

A mathematical formalism is introduced that has general applicability to many protein structure models used in the various approaches to the "inverse protein folding problem." The inverse nature of the problem arises from the fact that one begins with a set of assumed tertiary structures and searches for those most compatible with a new sequence, rather than attempting to predict the structure directly from the new sequence. The formalism is based on the well-known theory of Markov random fields (MRFs). Our MRF formulation provides explicit representations for the relevant amino acid position environments and the physical topologies of the structural contacts. In particular, MRF models can readily be constructed for the secondary structure packing topologies found in protein domain cores, or other structural motifs, that are anticipated to be common among large sets of both homologous and nonhomologous proteins. MRF models are probabilistic and can exploit the statistical data from the limited number of proteins having known domain structures. The MRF approach leads to a new scoring function for comparing different threadings (placements) of a sequence through different structure models. The scoring function is very important, because comparing alternative structure models with each other is a key step in the inverse folding problem. Unlike previously published scoring functions, the one derived in this paper is based on a comprehensive probabilistic formulation of the threading problem.

Amino Acid Sequence↗
PubMed2002

Decision analytical economic modelling within a Bayesian framework: application to prophylactic antibiotics use for caesarean section.

Economic evaluation of health care interventions based on decision analytic modelling can generate valuable information for health policy decision makers. However, the usefulness of the results obtained depends on the quality of the data input into the model; that is, the accuracy of the estimates for the costs, effectiveness, and transition probabilities between the different health states of the model. The aim of this paper is to review the use of Bayesian decision models in economic evaluation and to demonstrate how the individual components required for decision analytical modelling (i.e., systematic review incorporating meta-analyses, estimation of transition probabilities, evaluation of the model, and sensitivity analysis) may be addressed simultaneously in one coherent Bayesian model evaluated using Markov Chain Monte Carlo simulation implemented in the specialist Bayesian statistics software WinBUGS. To illustrate the method described, a simple probabilistic decision model is developed to evaluate the cost implications of using prophylactic antibiotics in caesarean section to reduce the incidence of wound infection. The advantages of using the Bayesian statistical approach outlined compared to the conventional classical approaches to decision analysis include the ability to: (i) perform all necessary analyses, including all intermediate analyses (e.g., meta-analyses) required to derive model parameters, in a single coherent model; (ii) incorporate expert opinion either directly or regarding the relative credibility of different data sources; (iii) use the actual posterior distributions for parameters of interest (opposed to making distributional assumptions necessary for the classical formulation); and (iv) incorporate uncertainty for all model parameters.

Antibiotic Prophylaxis↗
PubMed2001

The physiological kinetics of nitrogen and the prevention of decompression sickness.

Decompression sickness (DCS) is a potentially crippling disease caused by intracorporeal bubble formation during or after decompression from a compressed gas underwater dive. Bubbles most commonly evolve from dissolved inert gas accumulated during the exposure to increased ambient pressure. Most diving is performed breathing air, and the inert gas of interest is nitrogen. Divers use algorithms based on nitrogen kinetic models to plan the duration and degree of exposure to increased ambient pressure and to control their ascent rate. However, even correct execution of dives planned using such algorithms often results in bubble formation and may result in DCS. This reflects the importance of idiosyncratic host factors that are difficult to model, and deficiencies in current nitrogen kinetic models. Models describing the exchange of nitrogen between tissues and blood may be based on distributed capillary units or lumped compartments, either of which may be perfusion- or diffusion-limited. However, such simplistic models are usually poor predictors of experimental nitrogen kinetics at the organ or tissue level, probably because they fail to account for factors such as heterogeneity in both tissue composition and blood perfusion and non-capillary exchange mechanisms. The modelling of safe decompression procedures is further complicated by incomplete understanding of the processes that determine bubble formation. Moreover, any formation of bubbles during decompression alters subsequent nitrogen kinetics. Although these factors mandate complex resolutions to account for the interaction between dissolved nitrogen kinetics and bubble formation and growth, most decompression schedules are based on relatively simple perfusion-limited lumped compartment models of blood: tissue nitrogen exchange. Not surprisingly, all models inevitably require empirical adjustment based on outcomes in the field. Improvements in the predictive power of decompression calculations are being achieved using probabilistic bubble models, but divers will always be subject to the possibility of developing DCS despite adherence to prescribed limits.

Algorithms↗
PubMed2000

A probabilistic and decision-theoretic approach to the management of infectious disease at the ICU.

The medical community is presently in a state of transition from a situation dominated by the paper medical record to a future situation where all patient data will be available on-line by an electronic clinical information system. In data-intensive clinical environments, such as intensive care units (ICUs), clinical patient data are already fully managed by such systems in a number of hospitals. However, providing facilities for storing and retrieving patient data to clinicians is not enough; clinical information systems should also offer facilities to assist clinicians in dealing with hard clinical problems. Extending an information system's capabilities by integrating it with a decision-support system may be a solution. In this paper, we describe the development of a probabilistic and decision-theoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the intensive-care unit. Its underlying probabilistic-network model includes temporal knowledge to diagnose pneumonia on the basis of the likelihood of laryngotracheobronchial-tree colonisation by pathogens, and symptoms and signs actually present in the patient. Optimal antimicrobial therapy is selected by balancing the expected efficacy of treatment, which is related to the likelihood of particular pathogens causing the infection, against the spectrum of antimicrobial treatment. The models were built on the basis of expert knowledge. The patient data that were available were of limited value in the initial construction of the models because of problems of incompleteness. In particular, detailed temporal information was missing. By means of a number of different techniques, among others from the theory of linear programming, these data have been used to check the probabilistic information elicited from infectious-disease experts. The results of an evaluation of a number of slightly different models using retrospective patient data are discussed as well.

Anti-Bacterial Agents↗
PubMed1998

Herd-level test performance based on uncertain estimates of individual test performance, individual true prevalence and herd true prevalence.

A generalized model was derived for understanding the performance of herd-testing protocols when there is uncertainty and variability in individual-level sensitivity, specificity, prevalence of infection within infected herds, and prevalence of infected herds in the population. The model uses Monte-Carlo techniques to provide estimates of test performance for a dichotomous classification of herd-disease status. Uncertainty and variability in input assumptions are described using empirical and parametric probability distributions. The model permits both cluster-correlated behavior of inputs and sampling of animals without replacement. Disease due to obligate parasites is modeled differently from that due to organisms that persist for long periods in the environment. Dependence among model outcomes is assessed using Spearman's rank correlation. Model output is suitable for inclusion in risk-assessment models requiring probabilistic estimates of herd-level test performance, such as those developed for food-safety decision making and import-export risk assessment. The model was demonstrated using an example scenario based on Shiga-like toxin (SLT) producing Escherichia coli O157 in Ontario beef-cattle herds. Inputs were derived from the literature and Statistics Canada agricultural census data. Where appropriate, these data were subjected to distribution-fitting techniques. Otherwise, subjective interpretation was used to select input distributions and their parameters. Simulation revealed that the distribution of herd-level sensitivity for detecting herds infected with SLT producing E. coli O157 has a large range (0.003-0.99) and a median of 0.19. Herd-level specificity also had a large range (0.58-1) and a median of 0.94. Distributions of herd-level positive and negative predictive values exhibited similar degrees of uncertainty. In combination with poor likelihood ratios for positive and negative herd tests, results indicate that the testing protocol investigated has limited ability to discriminate between herds infected and not infected with SLT producing E. coli O157.

Algorithms↗
PubMed2000

Distance-based reconstruction of tree models for oncogenesis.

Comparative genomic hybridization (CGH) is a laboratory method to measure gains and losses in the copy number of chromosomal regions in tumor cells. It is hypothesized that certain DNA gains and losses are related to cancer progression and that the patterns of these changes are relevant to the clinical consequences of the cancer. It is therefore of interest to develop models which predict the occurrence of these events, as well as techniques for learning such models from CGH data. We continue our study of the mathematical foundations for inferring a model of tumor progression from a CGH data set that we started in Desper et al. (1999). In that paper, we proposed a class of probabilistic tree models and showed that an algorithm based on maximum-weight branching in a graph correctly infers the topology of the tree, under plausible assumptions. In this paper, we extend that work in the direction of the so-called distance-based trees, in which events are leaves of the tree, in the style of models common in phylogenetics. Then we show how to reconstruct the distance-based trees using tree-fitting algorithms developed by researchers in phylogenetics. The main advantages of the distance-based models are that 1) they represent information about co-occurrences of all pairs of events, instead of just some pairs, 2) they allow quantitative predictions about which events occur early in tumor progression, and 3) they bring into play the extensive methodology and software developed in the context of phylogenetics. We illustrate the distance-based tree method and how it complements the branching tree method, with a CGH data set for renal cancer.

Algorithms↗