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Results for “probabilistic modelling”

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PubMed2006

Model to assess the cost-effectiveness of new treatments for depression.

OBJECTIVES: The objective of this study was to develop a model to assess the cost-effectiveness of a new treatment for patients with depression. METHODS: A Markov simulation model was constructed to evaluate standard care for depression as performed in clinical practice compared with a new treatment for depression. Costs and effects were estimated for time horizons of 6 months to 5 years. A naturalistic longitudinal observational study provided data on costs, quality of life, and transition probabilities. Data on long-term consequences of depression and mortality risks were collected from the literature. Cost-effectiveness was quantified as quality-adjusted life-years (QALYs) gained from the new treatment compared with standard care, and the societal perspective was taken. Probabilistic analyses were conducted to present the uncertainty in the results, and sensitivity analyses were conducted on key parameters used in the model. RESULTS: Compared with standard care, the new hypothetical therapy was predicted to substantially decrease costs and was also associated with gains in QALYs. With an improved treatment effect of 50 percent on achieving full remission, the net cost savings were 20,000 Swedish kronor over a 5-year follow-up time, given equal costs of treatments. Patients gained .073 QALYs over 5 years. The results are sensitive to changes in assigned treatment effects. CONCLUSIONS: The present study provides a new model for assessing the cost-effectiveness of treatments for depression by incorporating full remission as the treatment goal and QALYs as the primary outcome measure. Moreover, we show the usefulness of naturalistic real-life data on costs and quality of life and transition probabilities when modeling the disease over time.

Cost-Benefit Analysis↗
PubMed2006

RNAKinetics: a web server that models secondary structure kinetics of an elongating RNA.

The RNAKinetics server (http://www.ig-msk.ru/RNA/kinetics) is a web interface for the newly developed RNAKinetics software. The software models the dynamics of RNA secondary structure by the means of kinetic analysis of folding transitions of a growing RNA molecule. The result of the modeling is a kinetic ensemble, i.e. a collection of RNA structures that are endowed with probabilities, which depend on time. This approach gives comprehensive probabilistic description of RNA folding pathways, revealing important kinetic details that are not captured by the traditional structure prediction methods. The access to the RNAKinetics server is free.

Base Sequence↗
PubMed1992

Model misspecification and multipoint linkage analysis.

Pairwise linkage analysis is robust to genetic model misspecification provided dominance is correctly specified, the primary effect being inflation of the recombination fraction. By contrast, we show that multipoint analysis under misspecified models is not robust when a putative disease locus is placed between close flanking markers, with potentially spuriously negative multipoint lod scores being produced. The problem is due to incorrect attribution of segregation of a disease allele and the consequent conclusion of (unlikely) double crossovers between flanking markers. As a possible solution, we propose the use of high disease allele frequencies, as this allows probabilistically for nonsegregation (through parental homozygosity or dual matings). We show analytically and through analysis of pedigree data simulated under a two-locus heterogeneity model that using a disease allele frequency of 0.05 in the dominant case and 0.25 in the recessive case is quite robust in producing positive multipoint lod scores with close flanking markers across a broad range of conditions including varying allele frequencies, epistasis, genetic heterogeneity and phenocopies.

Animals↗
PubMed1993

Behavioral and computational models of spatial attention.

Pigeons pecked for food in a spatially cued choice reaction time (RT) task. A brief (50-ms) white light appeared on a left or right key and probabilistically predicted the location (on either the left or right key) of a subsequent target stimulus. The time between cue and target onset (stimulus onset asynchrony), the base rate of left cues, and the probability that the cue correctly predicted the target (cue validity) were experimentally varied. The mean RT to respond to the target key was faster on correctly cued trials (defining a validity effect), decreased for both valid and invalid trials as stimulus onset asynchrony increased (defining an alerting effect), showed a variety of base-rate effects, and did not depend on cue validity. It is shown with a computational-processing model that dynamic interactions of short-term and associative memory processes are sufficient to produce these attentionlike empirical phenomena.

Animals↗
PubMed2002

Simulation of eutrophication and associated occurrence of hypoxic and anoxic condition in a coastal bay in Japan.

A probabilistic method of calculating the occurrence of oxygen-depleted water within a combined hydrothermal and water quality model was presented in this paper to investigate the environmental impact of eutrophication on the living resources. The method was applied to an eutrophicated shallow coastal bay in western Japan, where the occurrence of red tides at the water surface and the onset of bottom hypoxic waters are observed every summer. Both meteorology and freshwater inflow contribute to the development of stratification of the bay, thus limiting the dissolved oxygen supply to bottom waters. The resulting hydrodynamics enhances the development of oxygen-depleted bottom waters by transporting organic matter produced by algal blooms to the inner bay, where it decomposes and exerts high SOD. During August, about 60% of the inner bay is hypoxic for prolonged durations and as a result most of the benthic biota and fish die. The method used here is a very useful and informative way to evaluate the spatial and temporal damage and severity caused by hypoxia on living resources. Moreover, the model results agreed very well with the observed hydrodynamics, thermal structure and water quality data of the stratified bay. The model can be used for other lakes and bays where knowledge of temperature and density stratification is important for assessing water quality.

Environmental Monitoring↗
PubMed1998

Dynamic decision analysis in medicine: a data-driven approach.

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. Two major challenges in dynamic decision analysis are on proper formulation of a model for the problem and effective elicitation of the numerous time-dependent conditional probabilities for the model. Based on a new, general dynamic decision modeling framework called DynaMoL (Dynamic decision Modeling Language), we propose a data-driven approach to addressing these issues. Our approach uses available problem data from large medical databases, guides the decision modeling at a proper level of abstraction and establishes a Bayesian learning method for automatic extraction of the probabilistic parameters. We demonstrate the theoretical implications and practical promises of this new approach to dynamic decision analysis in medicine through a comprehensive case study in the optimal follow-up of patients after curative colorectal cancer surgery.

Bayes Theorem↗
PubMed2004

Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model.

Traditional attempts to model genotoxicity data have been limited to congeneric data sets, primarily because the mechanism of action was ignored, and frequently, the chemicals required metabolism to the active species. In this exercise, the COmmon REactivity PAtterns (COREPA) approach was used to delineate the structural requirements for eliciting mutagenicity in terms of ranges of descriptors associated with three-dimensional molecular structures. The database used to build the mutagenicity model includes 1196 structurally diverse chemicals tested in the Ames assay by the National Toxicology Program. This manuscript describes the development of the TA100 model that predicts the results of mutagenicity testing using only the Ames TA100 strain. The TA100 model was developed using 148 chemicals that tested positive in TA100 strain without rat liver enzymes (S-9) and 188 chemicals that tested positive in TA100 strain with rat liver enzymes. A decision tree was developed by first comparing the reactivity profile of chemicals that were positive in TA100 without rat liver enzymes to the reactivity profile of the remaining 1048 chemicals. This approach correctly identified 82% of the primary acting mutagens and 94% of the nonmutagens in the training set. The 188 chemicals in the training set that are positive only in the presence of metabolic activation would pass through the decision tree as negative. The next step was to identify the chemicals that are positive only in the presence of metabolic activation. To accomplish this, a series of hierarchically ordered metabolic transformations were used to develop an S-9 metabolism simulator that was applied to each of the 1048 chemicals. The potential metabolites were then screened through the decision tree to identify reactive mutagens. This model correctly identified 77% of the metabolically activated chemicals in a training set. A computer system that applies the COREPA models and predicts mutagenicity of chemicals, including their metabolic activation, was developed. Each prediction is accompanied by a probabilistic estimate of the chemical being in the structural domain covered by the training set.

Biotransformation↗
PubMed2002

Logic and human reasoning: an assessment of the deduction paradigm.

The study of deductive reasoning has been a major paradigm in psychology for approximately the past 40 years. Research has shown that people make many logical errors on such tasks and are strongly influenced by problem content and context. It is argued that this paradigm was developed in a context of logicist thinking that is now outmoded. Few reasoning researchers still believe that logic is an appropriate normative system for most human reasoning, let alone a model for describing the process of human reasoning, and many use the paradigm principally to study pragmatic and probabilistic processes. It is suggested that the methods used for studying reasoning be reviewed, especially the instructional context, which necessarily defines pragmatic influences as biases.

Cognition↗
PubMed2001

Evolutionary HMMs: a Bayesian approach to multiple alignment.

MOTIVATION: We review proposed syntheses of probabilistic sequence alignment, profiling and phylogeny. We develop a multiple alignment algorithm for Bayesian inference in the links model proposed by Thorne et al. (1991, J. Mol. Evol., 33, 114-124). The algorithm, described in detail in Section 3, samples from and/or maximizes the posterior distribution over multiple alignments for any number of DNA or protein sequences, conditioned on a phylogenetic tree. The individual sampling and maximization steps of the algorithm require no more computational resources than pairwise alignment. METHODS: We present a software implementation (Handel) of our algorithm and report test results on (i) simulated data sets and (ii) the structurally informed protein alignments of BAliBASE (Thompson et al., 1999, Nucleic Acids Res., 27, 2682-2690). RESULTS: We find that the mean sum-of-pairs score (a measure of residue-pair correspondence) for the BAliBASE alignments is only 13% lower for Handelthan for CLUSTALW(Thompson et al., 1994, Nucleic Acids Res., 22, 4673-4680), despite the relative simplicity of the links model (CLUSTALW uses affine gap scores and increased penalties for indels in hydrophobic regions). With reference to these benchmarks, we discuss potential improvements to the links model and implications for Bayesian multiple alignment and phylogenetic profiling. AVAILABILITY: The source code to Handelis freely distributed on the Internet at http://www.biowiki.org/Handel under the terms of the GNU Public License (GPL, 2000, http://www.fsf.org./copyleft/gpl.html).

Algorithms↗
PubMed2005

Probability distribution on the parse trees.

Paper describes probability distribution on the parse trees of natural language by using Bayesian networks. First parts of the paper describes probabilistic context-free grammar and parse trees. In the second part of the paper, Bayesian network was modelled and joint probability distribution on their vertex. On these theoretical ideas, in the third part, we describe our model tested on Database of grammatical sentences of Croatian language (http:/ /infoz.ffzg.hr / tepes /). At the end was presented a backward procedure and evaluation of our results.

Anthropology↗
PubMed1995

Evaluation of partial classification algorithms using ROC curves.

When using computer programs for decision support in clinical routine, an assessment or a comparison of the underlying classification algorithms is essential. In classical (forced) classification, the classification rule always selects exactly one alternative. A number of proven discriminant measures are available here, e.g.sensitivity and error rate. For probabilistic classification, a series of additional measures has been developed [1]. However, for many clinical applications, there are models where an observation is classified into several classes (partial classification), e.g., models from artificial intelligence, decision analysis, or fuzzy set theory. In partial classification, the discriminatory ability (Murphy) can be adjusted a priori to any level, in most practical cases. Here the usual measures do not apply. We investigate the preconditions for assessment and comparison based on medical decision theory. We focus on problems in the medical domain and establish a methodological framework. When using partial classification procedures, a ROC analysis in the classical sense is no longer appropriate. In forced classification for two classes, the problem is to find a cutoff point on the ROC curve; while in partial classification, you have to find two of them. They characterize the elements being classified as coming from both classes. This extends to several classes. We propose measures corresponding to the usual discriminant measures for forced classification (e.g., sensitivity and error rate) and demonstrate the effects using the ROC approach. For this purpose, we extend the existing method for forced classification in a mathematically sound manner. Algorithms for the construction of thresholds can easily be adapted. Two specific measurement models, based on parametric and non-parametric approaches, will be introduced. The basic methodology is suitable for all partial classification problems, whereas the extended ROC analysis assumes a rank order of the selected alternatives. The method is based on the black box principle and makes use only of the results of the compared algorithms and some general intuitive principles. Therefore, algorithms arising from different "philosophical" approaches may also be compared according to their results. For demonstration purposes, a clinical example from cranial computed tomography is presented. Linear discriminant analysis as a reference is compared to a Bayesian and a fuzzy procedure.

Algorithms↗
PubMed2006

Value of magnetic resonance cholangiopancreatography in the diagnosis of biliary abnormalities in postcholecystectomy patients: a probabilistic cost-effectiveness analysis of diagnostic strategies.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is considered the gold standard for imaging of the biliary tract but is associated with complications. Less invasive imaging techniques, such as magnetic resonance cholangiopancreatography (MRCP), have a much lower complication rate. The accuracy of MRCP is comparable to that of ERCP, and MRCP may be more effective and cost-effective, particularly in cases for which the suspected prevalence of disease is low and further intervention can be avoided. A model was constructed to compare the effectiveness and cost-effectiveness of MRCP and ERCP in patients with a previous history of cholecystectomy, presenting with abdominal pain and/or abnormal liver function tests. METHODS: Diagnostic accuracy estimates came from a systematic review of MRCP. A decision analytic model was constructed to represent the diagnostic and treatment pathway of this patient group. The model compared the following two diagnostic strategies: (i) MRCP followed with ERCP if positive, and then management based on ERCP; and (ii) ERCP only. Deterministic and probabilistic analyses were used to assess the likelihood of MRCP being cost-effective. Sensitivity analyses examined the impact of prior probabilities of common bile duct stones (CBDS) and test performance characteristics. The outcomes considered were costs, quality-adjusted life years (QALYs), and cost per additional QALY. RESULTS: The deterministic analysis indicated that MRCP was dominant over ERCP. At prior probabilities of CBDS, less than 60 percent MRCP was the less costly initial diagnostic test; above this threshold, ERCP was less costly. Similarly, at probabilities of CBDS less than 68 percent, MRCP was also the more effective strategy (generated more QALYs). Above this threshold, ERCP became the more effective strategy. Probabilistic sensitivity analyses indicated that, in this patient group for which there is a low to moderate probability of CBDS, there was a 59 percent likelihood that MRCP was cost-saving, an 83 percent chance that MRCP was more effective with a higher quality adjusted survival, and an 83 percent chance that MRCP had a cost-effectiveness ratio more favorable than dollars 50,000 per QALY gained. CONCLUSIONS: Costs and cost-effectiveness are dependent upon the prior probability of CBDS. However, probabilistic analysis indicated that, with a high degree of certainty, MRCP was the more effective and cost-effective initial test in postcholecystectomy patients with a low to moderate probability of CBDS.

Biliary Tract↗
PubMed2002

Continuous probabilistic prediction of angiographically significant coronary artery disease using electron beam tomography.

BACKGROUND: We sought to incorporate electron beam tomography-derived calcium scores in a model for prediction of angiographically significant coronary artery disease (CAD). Such a model could greatly facilitate clinical triage in symptomatic patients with no known CAD. METHODS AND RESULTS: We examined 1851 patients with suspected CAD who underwent coronary angiography for clinical indications. An electron beam tomographic scan was performed in all patients. Total per-patient calcium scores and separate scores for the major coronary arteries were added to logistic regression models to calculate a posterior probability of the severity and extent of angiographic disease. These models were designed to be continuous, adjusted for age and sex, corrected for verification bias, and independently validated in terms of their incremental diagnostic accuracy. The overall sensitivity was 95%, and specificity was 66% for coronary calcium to predict obstructive disease on angiography. With calcium scores >20, >80, and >100, the sensitivity to predict stenosis decreased to 90%, 79%, and 76%, whereas the specificity increased to 58%, 72%, and 75%, respectively. The logistic regression model exhibited excellent discrimination (receiver operating characteristic curve area, 0.842+/-0.023) and calibration (chi2 goodness of fit, 8.95; P=0.442). CONCLUSIONS: Electron beam tomographic calcium scanning provides incremental and independent power in predicting the severity and extent of angiographically significant CAD in symptomatic patients, in conjunction with pretest probability of disease. This algorithm is most useful when applied to an intermediate-risk population.

Adult↗
PubMed1992

Graph-grammar productions for the modeling of medical dilemmas.

We introduce graph-grammar production rules, which can guide physicians to construct models for normative decision making. A physician describes a medical decision problem using standard terminology, and the graph-grammar system matches a graph-manipulation rule to each of the standard terms. With minimal help from the physician, these graph-manipulation rules can construct an appropriate Bayesian probabilistic network. The physician can then assess the necessary probabilities and utilities to arrive at a rational decision. The grammar relies on prototypical forms that we have observed in models of medical dilemmas. We have found graph grammars to be a concise and expressive formalism for describing prototypical forms, and we believe such grammars can greatly facilitate the modeling of medical dilemmas and medical plans.

Bayes Theorem↗