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Results for “Likelihood Functions”

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PubMed1991

Myxoma virus expresses a secreted protein with homology to the tumor necrosis factor receptor gene family that contributes to viral virulence.

Poxviruses are known to contain a large number of open reading frames, particularly near the termini of the viral genome, that are not required for growth in tissue culture. However, many of these gene products are believed to play important roles in determining the virulence of the virus by modulating the host immune response to the infection. Recently it has been shown that Shope fibroma virus encodes, within the terminal inverted repeats, a protein (T2) related to the cellular tumor necrosis factor receptor (TNFR) and which specifically binds both TNF alpha and TNF beta. We have sequenced the terminal regions of two other Leporipoxviruses (myxoma virus and malignant rabbit fibroma virus) that are extremely invasive and capable of inducing extensive immunosuppression in rabbits and demonstrate that they also encode a closely related T2 homolog with all the structural motifs predicted for a secreted TNF binding protein. To investigate the biological role of the T2 protein, we have inactivated the myxoma virus T2 gene within each copy of the viral TIR by the insertion of a dominant selectable marker (Escherichia coli guanosine phosphoribosyltransferase) and selection of the recombinant virus in the presence of mycophenolic acid. The success of the inactivation of both copies of T2 was confirmed by the loss a broad protein band (52-56 kDa) of the predicted size for T2 from the profile of proteins secreted from mutant virus-infected BGMK cells at early times after infection. Although the T2-minus recombinant myxoma virus grew normally in tissue culture, upon infection of susceptible rabbits the viral disease was observed to be significantly attenuated. The majority of infected rabbits were able to mount an effective immune response to the infection and completely recovered. These survivor rabbits became immune to subsequent challenge with wild type myxoma virus. We conclude that the T2 viral protein is an important secreted virulence factor and that it in all likelihood functions by compromising the antiviral effects of TNF. We propose the term "viroceptor" to describe viral-encoded homologs of cellular lymphokine receptors whose function is to intercept the activity of the cognate lymphokine in order to short circuit the host immune response to the viral infection.

Amino Acid Sequence↗

PubMed1985

Assessment of the hot-plate antinociceptive test in mice. A new method for the statistical treatment of graded data.

The use of graded data from hot-plate antinociceptive tests is complicated by the problem of how to treat responses that are greater than the cut-off time. This paper describes a statistical procedure for analysis of antinociceptive data that circumvents this problem. The method is based upon the likelihood function and the data is assumed to follow a Weibull distribution. The technique can be implemented with the use of a computer and provides a more comprehensive assessment of antinociceptive data.

Analgesics↗

PubMed1985

A computer program for an illness-death process with time-dependent covariates.

This paper presents a computer program for estimating transition probabilities between states in a stochastic model for an illness-death process which incorporates time-dependent covariates. Parameters are estimated by the method of maximum likelihood using the Newton-Raphson iterative procedure. The program provides the standard normal deviate statistics as well as the value of the maximum of the likelihood function which can be used on repeated applications to test hypotheses concerning coefficients associated with covariates. Although this program is demonstrated by using a model with two 'illness' states and two 'death' states, it is also suitable for analyzing data with models involving fewer states, such as the analysis of survival time with covariates assuming a proportional hazard model.

Computers↗

PubMed1991

An optimization strategy for a biokinetic model of inhaled radionuclides.

Models for material disposition and dosimetry involve predictions of the biokinetics of the material among compartments representing organs and tissues in the body. Because of a lack of human data for most toxicants, many of the basic data are derived by modeling the results obtained from studies using laboratory animals. Such a biomathematical model is usually developed by adjusting the model parameters to make the model predictions match the measured retention and excretion data visually. The fitting process can be very time-consuming for a complicated model, and visual model selections may be subjective and easily biased by the scale or the data used. Due to the development of computerized optimization methods, manual fitting could benefit from an automated process. However, for a complicated model, an automated process without an optimization strategy will not be efficient, and may not produce fruitful results. In this paper, procedures for, and implementation of, an optimization strategy for a complicated mathematical model is demonstrated by optimizing a biokinetic model for 144Ce in fused aluminosilicate particles inhaled by beagle dogs. The optimized results using SimuSolv were compared to manual fitting results obtained previously using the model simulation software GASP. Also, statistical criteria provided by SimuSolv, such as likelihood function values, were used to help or verify visual model selections.

Administration, Inhalation↗

PubMed1993

Optimization issues in physiological toxicokinetic modeling: a case study with benzene.

This paper compares two methods for global optimization of physiologically based toxicokinetic models: Monte Carlo optimization, which searches randomly for the optimum; and the simplex method, which updates systematically an array of parameter values. Two measures of goodness-of-fit are also contrasted: criterion function and likelihood. A 14-parameter model of benzene distribution in rats is used to illustrate these techniques. Simplex optimization yields better fits overall. However, the measurement of uncertainty offered by Monte Carlo simulations is a major argument in favor of their use.

Animals↗

PubMed1991

Bayesian image processing in magnetic resonance imaging.

In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.

Algorithms↗

PubMed2004

Novel moment closure approximations in stochastic epidemics.

Moment closure approximations are used to provide analytic approximations to non-linear stochastic population models. They often provide insights into model behaviour and help validate simulation results. However, existing closure schemes typically fail in situations where the population distribution is highly skewed or extinctions occur. In this study we address these problems by introducing novel second- and third-order moment closure approximations which we apply to the stochastic SI and SIS epidemic models. In the case of the SI model, which has a highly skewed distribution of infection, we develop a second-order approximation based on the beta-binomial distribution. In addition, a closure approximation based on mixture distribution is developed in order to capture the behaviour of the stochastic SIS model around the threshold between persistence and extinction. This mixture approximation comprises a probability distribution designed to capture the quasi-equilibrium probabilities of the system and a probability mass at 0 which represents the probability of extinction. Two third-order versions of this mixture approximation are considered in which the log-normal and the beta-binomial are used to model the quasi-equilibrium distribution. Comparison with simulation results shows: (1) the beta-binomial approximation is flexible in shape and matches the skewness predicted by simulation as shown by the stochastic SI model and (2) mixture approximations are able to predict transient and extinction behaviour as shown by the stochastic SIS model, in marked contrast with existing approaches. We also apply our mixture approximation to approximate a likelihood function and carry out point and interval parameter estimation.

Computer Simulation↗

PubMed2006

A SAS macro for parametric and semiparametric mixture cure models.

Cure models have been developed to analyze failure time data with a cured fraction. For such data, standard survival models are usually not appropriate because they do not account for the possibility of cure. Mixture cure models assume that the studied population is a mixture of susceptible individuals, who may experience the event of interest, and non-susceptible individuals that will never experience it. The aim of this paper is to propose a SAS macro to estimate parametric and semiparametric mixture cure models with covariates. The cure fraction can be modelled by various binary regression models. Parametric and semiparametric models can be used to model the survival of uncured individuals. The maximization of the likelihood function is performed using SAS PROC NLMIXED for parametric models and through an EM algorithm for the Cox's proportional hazards mixture cure model. Indications and limitations of the proposed macro are discussed and an example in the field of cancer clinical trials is shown.

Algorithms↗

PubMed2006

Solution for underflow problem in linkage and segregation analysis.

Finding genes for complex traits is one of the major challenges of modern human genetics. Current developments of molecular techniques facilitated use of large pedigrees and marker sets of thousands of single-nucleotide polymorphisms (SNPs). However, one of the problems occurring in statistical analysis of such large data sets is that the likelihood is very low and underflow may easily occur. In this work we describe a method permitting to avoid underflow during computation of a likelihood function, using different algorithms. Our method makes practically possible analysis of thousands of individuals and thousands of SNPs. The method is easy to implement without major change of the code of existing programs. It also helps to reduce the amount of computer memory used in analysis without noticeable alteration of the program running time. The algorithm was implemented in the software packages for segregation and linkage analysis, which are available from http://mga.bionet.nsc.ru/.

Algorithms↗

PubMed2006

Towards quantitative analysis of retinal features in optical coherence tomography.

The purpose of this paper was to propose a new computer method for quantitative evaluation of representative features of the retina using optical coherence tomography (OCT). A multi-step approach was devised and positively tested for segmentation of the three main retinal layers: the vitreo-retinal interface and the inner and outer retina. Following a preprocessing step, three regions of interest were delimited. Significant peaks corresponding to high and low intensity strips were located along the OCT A-scan lines and accurate boundaries between different layers were obtained by maximizing an edge likelihood function. For a quantitative description, thickness measurement, densitometry, texture and curvature analyses were performed. As a first application, the effect of intravitreal injection of triamcinolone acetonide (IVTA) for the treatment of vitreo-retinal interface syndrome was evaluated. Almost all the parameters, measured on a set of 16 pathologic OCT images, were statistically different before and after IVTA injection (p<0.05). Shape analysis of the internal limiting membrane confirmed the reduction of the pathological traction state. Other significant parameters, such as reflectivity and texture contrast, exhibited relevant changes both at the vitreo-retinal interface and in the inner retinal layers. Texture parameters in the inner and outer retinal layers significantly correlated with the visual acuity restoration. According to these findings an IVTA injection might be considered a possible alternative to surgery for selected patients. In conclusion, the proposed approach appeared to be a promising tool for the investigation of tissue changes produced by pathology and/or therapy.

Anti-Inflammatory Agents↗

PubMed2005

An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours.

In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out.

Bayes Theorem↗

PubMed1997

Analyses on the temporal patterns of spikes of auditory neurons of the macaque monkey by means of an artificial neural network and tree-based models.

The time scale over which information in the primary auditory cortex is processed was estimated. An artificial neural network was used to learn the temporal patterns of spikes. After learning, test patterns were input to the network. Comparison of the accuracy of the network with that of the maximum likelihood function computed from the spike count reveals that the temporal patterns of spikes are closely related to stimulus discrimination. Next, a tree-based model from a subset of the spike trains with a fixed time resolution was constructed and validated the model with another. By repeating this for different bin widths, it was found that there are no simple models for the time bin width larger than 50 ms. This indicates that the time scale in the auditory cortex is not larger than 50 ms.

Animals↗

PubMed1976

Point and interval estimation in the combination of bioassay results.

A procedure for combining evidence from different biological assays is shown to be equivalent both to generalized least-squares and to maximum-likelihood estimation. By appropriate nesting of hypotheses, the likelihood function can be used to test the agreement between the assays and to obtain probability limits for the combined estimate of potency. The properties of these limits are examined, with particular reference to the situation, unusual but not impossible in practice, in which the values of relative potency that they define consist of several disjoint segments instead of a single interval. The connection with general theory of estimating linear functional relations is pointed out.

Biological Assay↗

PubMed1990

A Bayesian method for synthesizing evidence. The Confidence Profile Method.

This article describes a collection of meta-analysis techniques based on Bayesian statistics for interpreting, adjusting, and combining evidence to estimate parameters and outcomes important to the assessment of health technologies. The result of an analysis by the Confidence Profile Method is a joint posterior probability distribution for the parameters of interest, from which marginal distributions for any particular parameter can be calculated. The method can be used to analyze problems involving a variety of types of outcomes, a variety of measures of effect, and a variety of experimental designs. This article presents the elements necessary for analysis, including prior distributions, likelihood functions, and specific models for experimental designs that include adjustment for biases.

Bayes Theorem↗

PubMed2001

Introduction to Bayesian reasoning.

Interest in Bayesian analyses has increased recently, in part as a response to policy makers wanting sound scientific bases for health technology assessments, and associated healthcare funding decisions. This paper provides a brief and simplified description of Bayesian reasoning. Bayes is illustrated in a clinical setting of an expert helping a woman understand the potential risk of passing on an inheritable disease (hemophilia) to her next child, based on disease occurrence in two living children. The illustration describes fundamental concepts and derivations, such as Bayes theorem, likelihood functions, prior probability, and posterior probability. A second illustration shows the use of Bayes for interpreting clinical trial results. The uncertainty in the clinical effect before and after the trial analyses has been completed is characterized by the Bayes prior and posterior probabilities, respectively. Techniques are also shown for estimating the potential loss (e.g., in lives lost) for making the wrong decision with and without knowledge of the trial results, an estimation that cannot be carried out using techniques of hypotheses testing associated with the frequentist school of statistics. Information from Bayes analysis then may be used to help policy makers decide, or justify, whether the analyses provides a sufficient basis for making a treatment recommendation, or whether there remains a need to request more information. Subsequent papers in this volume offer additional examples and clarification of the use of Bayes in clinical practice and in interpretation of clinical studies.

Bayes Theorem↗

PubMed1998

A study of interval censoring in parametric regression models.

Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation. Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding non-parametric models.

Animals↗

PubMed2002

Segregation analysis of prostate cancer in 1,719 white, African-American and Asian-American families in the United States and Canada.

UNLABELLED: Some data suggest that brothers of prostate cancer patients have higher disease risk than their fathers, supporting an X-linked or recessive mode of inheritance. However, higher observed frequencies in brothers than fathers may merely reflect the strong temporal changes in US incidence rates. OBJECTIVES: (a) to evaluate the fit of X-linked, recessive, and dominant modes of inheritance to prostate cancer incidence, specific for calendar year, age, and race, in population-based samples of US and Canadian families; and (b) to evaluate a simple multifactorial model for familial aggregation of prostate cancer due to shared low-penetrance variants of many genes or shared lifestyle factors. METHODS: The data consist of reported prostate cancer incidence in first-degree relatives of 1,719 white, African-American, and Asian-American men with and without prostate cancer at ages <70 years. Model parameters were estimated by maximizing a pseudo-likelihood function of the data, and goodness of model fit was assessed by evaluating discrepancies between observed and expected numbers of pairs of relatives with prostate cancer. RESULTS: After adjusting for temporal trends in prostate cancer incidence rates we found that the X-linked model fit poorly. underpredicting the observed number of affected father-son pairs. This also was true of the recessive model, although the evidence for poor fit did not achieve statistical significance. In contrast, the dominant model provided adequate fit to the data. In this model the race-specific penetrance estimates for carriers of deleterious genotypes were similar among African-Americans and whites, but lower among Asian-Americans: risk by age 80 years for carriers born in 1900 was estimated as 75.3% for African-Americans and whites, and 44.4% for Asian-Americans. None of the Mendelian models fit the data better than did the simple multifactorial model. CONCLUSIONS: The good fit of the multifactorial model suggests that multiple genes, each having low penetrance, may be responsible for most inherited prostate cancer susceptibility, and that the contribution of rare highly penetrant mutations is small.

Adult↗

PubMed1998

A use of Monte Carlo integration for population pharmacokinetics with multivariate population distribution.

This paper describes a use of Monte Carlo integration for population pharmacokinetics with multivariate population distribution. In the proposed approach, a multivariate lognormal distribution is assumed for a population distribution of pharmacokinetic (PK) parameters. The maximum likelihood method is employed to estimate the population means, variances, and correlation coefficients of the multivariate lognormal distribution. Instead of a first-order Taylor series approximation to a nonlinear PK model, the proposed approach employs a Monte Carlo integration for the multiple integral in maximizing the log likelihood function. Observations below the lower limit of detection, which are usually included in Phase 1 PK data, are also incorporated into the analysis. Applications are given to a simulated data set and an actual Phase 1 trial to show how the proposed approach works in practice.

Algorithms↗