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PubMed2006

User subjectivity in Monte Carlo modeling of pesticide exposure.

Monte Carlo techniques are increasingly used in pesticide exposure modeling to evaluate the uncertainty in predictions arising from uncertainty in input parameters and to estimate the confidence that should be assigned to the modeling results. The approach typically involves running a deterministic model repeatedly for a large number of input values sampled from statistical distributions. In the present study, six modelers made choices regarding the type and parameterization of distributions assigned to degradation and sorption data for an example pesticide, the correlation between the parameters, the tool and method used for sampling, and the number of samples generated. A leaching assessment was carried out using a single model and scenario and all data for sorption and degradation generated by the six modelers. The distributions of sampled parameters differed between the modelers, and the agreement with the measured data was variable. Large differences were found between the upper percentiles of simulated concentrations in leachate. The probability of exceeding 0.1 microg/L ranged from 0 to 35.7%. The present study demonstrated that subjective choices made in Monte Carlo modeling introduce variability into probabilistic modeling and that the results need to be interpreted with care.

Monte Carlo Method↗
PubMed2004

Cost effectiveness of memantine in Alzheimer's disease: an analysis based on a probabilistic Markov model from a UK perspective.

BACKGROUND: Clinical trials with memantine, an uncompetitive moderate-affinity NMDA antagonist, have shown improved clinical outcomes, increased independence and a trend towards delayed institutionalisation in patients with moderately severe-to-severe Alzheimer's disease. In a randomised double-blind, placebo-controlled, 28-week study conducted in the US, reductions in resource utilisation and total healthcare costs were noted with memantine relative to placebo. While these findings suggest that, compared with placebo, memantine provides cost savings, further analyses may help to quantify potential economic gains over a longer treatment period. OBJECTIVE: To evaluate the cost effectiveness of memantine therapy compared with no pharmacological treatment in patients with moderately severe-to-severe Alzheimer's disease over a 2-year period. METHODS: A Markov model was constructed to simulate patient progression through a series of health states related to severity, dependency (determined by patient scores on the Alzheimer's Disease Cooperative Study-Activities of Daily Living [ADCS-ADL] inventory and residential status ('institutionalisation') with a time horizon of 2 years (each 6-month Markov cycle was repeated four times). Transition probabilities from one health state to another 6 months later were mainly derived from a 28-week, randomised, double-blind, placebo-controlled clinical trial. Inputs related to epidemiological and cost data were derived from a UK longitudinal epidemiological study, while data on quality-adjusted life-years (QALYs) were derived from a Danish longitudinal study. To ensure conservative estimates from the model, the base case analysis assumed drug effectiveness was limited to 12 months. Monte Carlo simulations were performed for each state parameter following definition of a priori distributions for the main variables of the model. Sensitivity analyses included worst case scenario in which memantine was effective for 6 months and one-way sensitivity analyses on key parameters. Finally, a subgroup analysis was performed to determine which patients were most likely to benefit from memantine. Informal care was not included in this model as the costs were considered from National Health Service and Personal Social Services perspective. RESULTS: The base case analysis found that, compared with no treatment, memantine was associated with lower costs and greater clinical effectiveness in terms of years of independence, years in the community and QALYs. Sensitivity analyses supported these findings. For each category of Alzheimer's disease patient examined, treatment with memantine was a cost-effective strategy. The greatest economic gain of memantine treatment was in independent patients with a Mini-Mental State Examination score of > or =10. CONCLUSION: This model suggests that memantine treatment is cost effective and provides cost savings compared with no pharmacological treatment. These benefits appear to result from prolonged patient independence and delayed institutionalisation for moderately severe and severe Alzheimer's disease patients on memantine compared with no pharmacological treatment.

Aged↗
PubMed2000

[Short-wavelength automated perimetry (SWAP) in patients with suspected glaucoma (II): correlation with a probabilistic multifactorial model of risk for developing glaucomatous damage].

PURPOSE: To evaluate the efficiency and predictability of a multifactorial probability model to assess the risk of developing glaucomatous visual field defects and to determine the correlation between retinal nerve fiber layer (RNFL) photographs and short-wavelength automated perimetry (SWAP) in a population of patients suspected of having glaucoma. METHODS: One hundred and sixty eyes belonging to 83 patients with ocular hypertension [ocular pressure figures over 21 mmHg and normal conventional automated perimetry (AP) (white-white)] were included in the study. The risk of developing glaucomatous defects was assessed by a multifactorial model that includes intraocular pressure. vertical cup-to-disk ratio, age and family background. RNFL photographs and SWAP tests were also performed. RESULTS: SWAPs were pathological in 57 cases (35.6% of the sample); 23% of the perimetries were abnormal in the low-risk group while the moderate and high risk groups showed abnormal perimetries 38% and 56% respectively. The RNFL defects at the different risk levels were consistent with the SWAP results. The multiple regression analysis of the four variables included in the model showed the relationship among the risk factors, evaluated globally, and the presence of glaucomatous damage evaluated by RNFL and SWAP (p<0.001). CONCLUSIONS: The usefulness of this probability model as predictive indicator of subsequent glaucomatous damage is based on the good correlation between the risk scale and the RNFL and SWAP assessments. These techniques evaluate structural (RNFL) and functional (SWAP) changes in early stages of the glaucomatous disease.

Adult↗
PubMed1998

The Choice Probabilities of the Latent-Scale Model Satisfy the Size-Independent Model When n Is Small.

Two probabilistic models for subset choices are compared. The first one, due to Marley (1993), was dubbed the latent-scale model by Regenwetter, Marley, and Joe (1996). The second one is Falmagne and Regenwetter's (1996) size-independent model of approval voting. We show that for up to five choice alternatives, the choice probabilities generated by the latent-scale model can be explained also by the size-independent model. The proof uses the König-Hall theorem of graph theory and the characterization of the size-independent model by the approval-voting polytope of Doignon and Regenwetter (1997). The problem remains open for the case with more than five choice alternatives. Copyright 1998 Academic Press.

Journal Article↗
PubMed2006

Optimal selection of individuals for BRCA mutation testing: a comparison of available methods.

PURPOSE: Several methods have been described that estimate the likelihood that a family history of cancer is a result of a mutation in the BRCA1 or BRCA2 genes. We examined the performance of six different methods with the aim of identifying an optimal strategy for selecting individuals for mutation testing in clinical practice. PATIENTS AND METHODS: Two hundred fifty-seven families who had completed BRCA1 and BRCA2 mutation screening were assessed by six models representing the major methodologies used to assess the likelihood of a pathogenic mutation. The performance of each method as a selection criterion was compared with the result of mutation testing to produce sensitivity, specificity, and receiver operating curve data. The impact of incorporating breast cancer pathology data in the assessment was also analyzed. RESULTS: The highest accuracy was achieved by the Bayesian probabilistic model (BRCAPRO). The formal probabilistic methods were significantly more accurate than clinical scoring methods. The methods were further improved by the incorporation of information on breast cancer pathology (tumor grade and estrogen receptor/progesterone receptor status). The resulting combined probability figure was highly accurate when selecting individuals for BRCA1 testing. Some BRCA2 mutation carriers were missed by all of the models examined. CONCLUSION: Formal probabilistic models provide significantly greater accuracy in the selection of families for gene testing than the use of clinical criteria or scoring methods. The accuracy is further enhanced by incorporating information on the pathology of breast cancers occurring in the families.

Adult↗
PubMed1996

Exposure-response functions in Air Force toxic risk modeling.

A new methodology for estimating the probabilistic risk from acute toxic exposures is planned as a support tool for the Air Force at the Eastern and Western Ranges. Two such methodologies are programs entitled the Launch Area Toxic Analysis program (LATRA) and the Cold Spill Toxic Risk Analysis program (COSTRA). These programs combine probabilistic models of an accident (when applicable), release cloud formation and dispersion (appropriate to the toxic substance and accounting for meteorological conditions), and new exposure-response functions (ERFs) for sensitive and normal exposed populations. These ERFs, anchored on specific exposure standards, estimate the probability of a given severity of health effect in a particular population as a function of the concentration or dose to which it is exposed. The further development and acceptance of these ERFs by the toxicology community, especially for different sensitivities, are key concerns addressed in this paper.

Accidents, Aviation↗
PubMed1995

The estimation of event related potentials affected by random shifts and scalings.

There is considerable evidence for trial to trial variability of the event related potentials (ERPs) within a given subject's recording. This variability influences the outcome of usual procedures in ERP analysis. Better results may be obtained if the sources of variability are explicitly taken into account in an appropriate model. This paper considers a probabilistic model, the random shift and scaling (RSS) model, where the response is modified by a random time shift and a random scale factor. In addition to this, an additional random scale factor which affects both the response and the background noise is taken into account. This time shift and these scale factors are handled as nuisance parameters. Maximum likelihood and least squares estimators of these parameters and the waveform of response are derived for the RSS model. It is shown that the Woody estimate of the ERP reported in earlier work can be derived by restricting the assumptions for the RSS model. Test statistics for hypotheses on means are obtained for the RSS model and a new type of discriminant function. The usefulness of the method is illustrated by means of simulation studies. Receiver operating characteristic (ROC) curves are used to demonstrate that the new type of discriminant performs better than the usual Fisher's Linear Discriminant.

Artifacts↗
PubMed2004

Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals.

We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5' (donor) and 3' (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models.

Base Sequence↗
PubMed1999

Mathematical models of diffusion-limited gas bubble dynamics in tissue.

Mathematical models of bubble evolution in tissue have recently been incorporated into risk functions for predicting the incidence of decompression sickness (DCS) in human subjects after diving and/or flying exposures. Bubble dynamics models suitable for these applications assume the bubble to be either contained in an unstirred tissue (two-region model) or surrounded by a boundary layer within a well-stirred tissue (three-region model). The contrasting premises regarding the bubble-tissue system lead to different expressions for bubble dynamics described in terms of ordinary differential equations. However, the expressions are shown to be structurally similar with differences only in the definitions of certain parameters that can be transformed to make the models equivalent at large tissue volumes. It is also shown that the two-region model is applicable only to bubble evolution in tissues of infinite extent and cannot be readily applied to bubble evolution in finite tissue volumes to simulate how such evolution is influenced by interactions among multiple bubbles in a given tissue. Two-region models that are incorrectly applied in such cases yield results that may be reinterpreted in terms of their three-region model equivalents but only if the parameters in the two-region model transform into consistent values in the three-region model. When such transforms yield inconsistent parameter values for the three-region model, results may be qualitatively correct but are in substantial quantitative error. Obviation of these errors through appropriate use of the different models may improve performance of probabilistic models of DCS occurrence that express DCS risk in terms of simulated in vivo gas and bubble dynamics.

Air Pressure↗
PubMed2004

Aminoglycoside nephrotoxicity.

The main constraints to the administration of aminoglycosides (AG) are risks of nephrotoxicity and ototoxicity, which can lead to renal and vestibular failure. AG accumulation in the kidney may be related to the dosing schedule. As a result, administration of larger doses on a less frequent basis may reduce the drug accumulation in the renal cortex. Many methods have been proposed to reduce AG nephrotoxicity. (1) Molecular modeling and analog synthesis could lead to intrinsically less toxic AG but this approach is time consuming and expensive. Protective approaches such as the co-administration of polyaspartic acid or defferoxamine appear to be very promising in clinical practice. (2) Population pharmacokinetic computer programs, used to control AG serum concentrations, are correct predictors of efficacy but the estimated concentrations in the second compartment are not reliable predictors of nephrotoxicity because they do not take into account non-linear processes such as the AG uptake in the renal cortex or the tubuloglomerular feedback. (3) Finally, modelling the AG nephrotoxicity with probabilistic approaches and/or with deterministic approaches seems to be very promising. These two approaches appear to be not competitive but very complementary in clinical practice. The probabilistic model can be used to predict nephrotoxicity at the beginning the treatment. The deterministic model can be used to simulate and control nephrotoxicity when it is already unfolding and the treatment must be given for a long period of time.

Aminoglycosides↗
PubMed2002

Analysis of B cell receptor production and rearrangement. Part I. Light chain rearrangement.

A probabilistic model of allelic exclusion fails to explain the status of receptor genes and the receptor phenotype of most B cells. A large proportion of B cells have incompletely rearranged H and/or L chain genes (e.g. kappa0/kappa+) and most B cells express only one receptor. These properties seem to require deterministic features of B cell development such as special mechanisms that stop rearrangement. However, receptor editing has revealed that rearrangement-stop is not stable and that multi-receptor lymphocytes make up a significant fraction of certain B and T cell populations. Consequently we have revived the purely probabilistic approach in a model that now includes receptor editing and allows for some multi-receptor B cells. We find that this model can explain the observed properties of B cells when the frequency of self-reactive B cells is high. Indeed, as we illustrate for anti-DNA, this is the case. Hence the probabilistic model has life and assiduous use of the model suggests unexpected but not unrealistic features of lymphocyte development.

Algorithms↗
PubMedDate not supplied

Probability of decompression sickness in no-stop air diving and subsaturation diving.

Probabilistic models allow estimation of the probability (Pdcs) that decompression sickness (DCS) will occur in any particular dive. Our objective is to provide Pdcs estimates for no-stop diving instructions used by the U.S. Navy and various other navies. To do so, we develop statistics-based (probabilistic) and intuition-based (deterministic) models using dive-outcome data from the U.S. Navy Decompression Database. We give special attention to subsaturation dives (defined as no-stop dives shallower than 40 fswg with bottom times between 4 hr and one day), for which experimental dives are scarce. According to our models, probability of DCS is 2% or less for current U.S. Navy no-stop air dive schedules and near 1% for the navies of Great Britain, Canada, and France; also the current U.S. Navy prescriptions for subsaturation dives seem to be appropriate. Our probabilistic models fail for deep dives; they do not avoid observed DCS cases in the calibration dataset and provide longer no-stop times than allowed by tables used operationally; we advocate prescriptions by our deterministic model for deep no-stop dives.

Algorithms↗
PubMed2003

Protein family classification using sparse markov transducers.

We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Since substitutions of amino acids are common in protein families, incorporating wild-cards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. As protein databases become larger, data driven learning algorithms for probabilistic models such as SMTs will require vast amounts of memory. We therefore describe and use efficient data structures to improve the memory usage of SMTs. We evaluate SMTs by building protein family classifiers using the Pfam and SCOP databases and compare our results to previously published results and state-of-the-art protein homology detection methods. SMTs outperform previous probabilistic suffix tree methods and under certain conditions perform comparably to state-of-the-art protein homology methods.

Algorithms↗
PubMed2003

A "Long Indel" model for evolutionary sequence alignment.

We present a new probabilistic model of sequence evolution, allowing indels of arbitrary length, and give sequence alignment algorithms for our model. Previously implemented evolutionary models have allowed (at most) single-residue indels or have introduced artifacts such as the existence of indivisible "fragments." We compare our algorithm to these previous methods by applying it to the structural homology dataset HOMSTRAD, evaluating the accuracy of (1) alignments and (2) evolutionary time estimates. With our method, it is possible (for the first time) to integrate probabilistic sequence alignment, with reliability indicators and arbitrary gap penalties, in the same framework as phylogenetic reconstruction. Our alignment algorithm requires that we evaluate the likelihood of any specific path of mutation events in a continuous-time Markov model, with the event times integrated out. To this effect, we introduce a "trajectory likelihood" algorithm (Appendix A). We anticipate that this algorithm will be useful in more general contexts, such as Markov Chain Monte Carlo simulations.

Algorithms↗
PubMed2006

Continuous and tractable models for the variation of evolutionary rates.

We propose a continuous model for variation in the evolutionary rate across sites and over the phylogenetic tree. We derive exact transition probabilities of substitutions under this model. Changes in rate are modelled using the CIR process, a diffusion widely used in financial applications. The model directly extends the standard gamma distributed rates across site model, with one additional parameter governing changes in rate down the tree. The parameters of the model can be estimated directly from two well-known statistics: the index of dispersion and the gamma shape parameter of the rates across sites model. The CIR model can be readily incorporated into probabilistic models for sequence evolution. We provide here an exact formula for the likelihood of a three-taxon tree. The likelihoods of larger trees can be evaluated using Monte-Carlo methods.

Base Sequence↗
PubMed1995

HLA prediction model for extended family matches.

This article derives a probabilistic model for predicting HLA matches from a population of extended family members. (Extended family members are defined as either first cousins or blood-related aunts or uncles). The model uses family pedigree information and haplotype frequency data to estimate the likelihood of a match. Results are given for many ethnic groupings. A case study is also described. This technique is most applicable when all family members are of the same ethnic origin, which increases the likelihood of a match among a small number of family members, and the patient possesses a haplotype having a frequency exceeding 10%, within the patient's ethnic population. Under such conditions, an extended family search can frequently approximate the success rate of a single sibling search.

Adult↗
PubMed2003

Bayesian gene/species tree reconciliation and orthology analysis using MCMC.

MOTIVATION: Comparative genomics in general and orthology analysis in particular are becoming increasingly important parts of gene function prediction. Previously, orthology analysis and reconciliation has been performed only with respect to the parsimony model. This discards many plausible solutions and sometimes precludes finding the correct one. In many other areas in bioinformatics probabilistic models have proven to be both more realistic and powerful than parsimony models. For instance, they allow for assessing solution reliability and consideration of alternative solutions in a uniform way. There is also an added benefit in making model assumptions explicit and therefore making model comparisons possible. For orthology analysis, uncertainty has recently been addressed using parsimonious reconciliation combined with bootstrap techniques. However, until now no probabilistic methods have been available. RESULTS: We introduce a probabilistic gene evolution model based on a birth-death process in which a gene tree evolves 'inside' a species tree. Based on this model, we develop a tool with the capacity to perform practical orthology analysis, based on Fitch's original definition, and more generally for reconciling pairs of gene and species trees. Our gene evolution model is biologically sound (Nei et al., 1997) and intuitively attractive. We develop a Bayesian analysis based on MCMC which facilitates approximation of an a posteriori distribution for reconciliations. That is, we can find the most probable reconciliations and estimate the probability of any reconciliation, given the observed gene tree. This also gives a way to estimate the probability that a pair of genes are orthologs. The main algorithmic contribution presented here consists of an algorithm for computing the likelihood of a given reconciliation. To the best of our knowledge, this is the first successful introduction of this type of probabilistic methods, which flourish in phylogeny analysis, into reconciliation and orthology analysis. The MCMC algorithm has been implemented and, although not yet being in its final form, tests show that it performs very well on synthetic as well as biological data. Using standard correspondences, our results carry over to allele trees as well as biogeography.

Algorithms↗