SEARCH · PubMed Health

Results for “likelihood”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 253 records · Page 14Linked to original sources

PubMed2002

Molecular systematics of the Jacks (Perciformes: Carangidae) based on mitochondrial cytochrome b sequences using parsimony, likelihood, and Bayesian approaches.

The Carangidae represent a diverse family of marine fishes that include both ecologically and economically important species. Currently, there are four recognized tribes within the family, but phylogenetic relationships among them based on morphology are not resolved. In addition, the tribe Carangini contains species with a variety of body forms and no study has tried to interpret the evolution of this diversity. We used DNA sequences from the mitochondrial cytochrome b gene to reconstruct the phylogenetic history of 50 species from each of the four tribes of Carangidae and four carangoid outgroup taxa. We found support for the monophyly of three tribes within the Carangidae (Carangini, Naucratini, and Trachinotini); however, monophyly of the fourth tribe (Scomberoidini) remains questionable. A sister group relationship between the Carangini and the Naucratini is well supported. This clade is apparently sister to the Trachinotini plus Scomberoidini but there is uncertain support for this relationship. Additionally, we examined the evolution of body form within the tribe Carangini and determined that each of the predominant clades has a distinct evolutionary trend in body form. We tested three methods of phylogenetic inference, parsimony, maximum-likelihood, and Bayesian inference. Whereas the three analyses produced largely congruent hypotheses, they differed in several important relationships. Maximum-likelihood and Bayesian methods produced hypotheses with higher support values for deep branches. The Bayesian analysis was computationally much faster and yet produced phylogenetic hypotheses that were very similar to those of the maximum-likelihood analysis.

Animals↗

PubMed2002

Phylogeographic analysis of the cornsnake (Elaphe guttata) complex as inferred from maximum likelihood and Bayesian analyses.

Most phylogeographic studies have used maximum likelihood or maximum parsimony to infer phylogeny and bootstrap analysis to evaluate support for trees. Recently, Bayesian methods using Marlov chain Monte Carlo to search tree space and simultaneously estimate tree support have become popular due to its fast search speed and ability to create a posterior distribution of parameters of interest. Here, I present a study that utilizes Bayesian methods to infer phylogenetic relationships of the cornsnake (Elaphe guttata) complex using cytochrome b sequences. Examination of the posterior probability distributions confirms the existence of three geographic lineages. Additionally, there is no support for the monophyly of the subspecies of E. guttata. Results suggest the three geographic lineages partially conform to the ranges of previously defined subspecies, although Shimodaira-Hasegawa tests suggest that subspecies-constrained trees produce significantly poorer likelihood estimates than the most likely trees reflecting the evolution of three geographic assemblages. Based on molecular support, these three geographic assemblages are recognized as species using evolutionary species criteria: E. guttata, Elaphe slowinskii, and Elaphe emoryi [phylogeographic, maximum likelihood, maximum parsimony, bootstrap, Bayesian, Markov chain Monte Carlo, cornsnake, Cytochrome b, geographic lineages, E. guttta, E. slowinskii, and E. emoryi].

Animals↗

PubMed2000

An EM algorithm for obtaining maximum likelihood estimates in the multi-phenotype variance components linkage model.

In recent years variance components models have been developed for localising genes that contribute to human quantitative variation. In typical applications one assumes a multivariate normal model for phenotypes and estimates model parameters by maximum likelihood. For the joint analysis of several correlated phenotypes, however, finding the maximum likelihood estimates for an appropriate multivariate normal model can be a difficult computational task due to complex constraints among the model parameters. We propose an algorithm for computing maximum likelihood estimates in a multi-phenotype variance components linkage model that readily accommodates these parameter constraints. Data simulated for Genetic Analysis Workshop 10 are used to demonstrate the potential increase in power to detect linkage that can be obtained if correlated phenotypes are analysed jointly rather than individually.

Algorithms↗

PubMed1998

The mirror effect and attention-likelihood theory: a reflective analysis.

The mirror effect refers to findings from studies of recognition memory consistent with the idea that the underlying "strength" distributions are symmetric around their midpoint separating studied and nonstudied items. Attention-likelihood theory assumes underlying binomial distributions of marked features and claims that old-item differences result from differential attention across conditions during study. The symmetry arises because subjects use the likelihood ratio as the basis for decision. The author analyzes the model and argues that one of the main criticisms (the complexity of the likelihood-ratio decision rule) is unwarranted. A further analysis shows that other distributions (the Poisson and the hypergeometric) can also produce a mirror effect. Even with the binomial distribution, a variety of parameter values can produce a mirror effect, and with the right combination of parameter values, differential attention across conditions is not necessary for a mirror effect to occur.

Attention↗

PubMed2004

Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous.

All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology--the probability of the data, given the tree--is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric--the minimum number of character state changes required to generate the data on a given tree--without assuming a specific distribution. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult.

Bayes Theorem↗

PubMed1998

Statistical confidence for likelihood-based paternity inference in natural populations.

Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic delta for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for delta that permit assignment of paternity to the most likely male with a known level of statistical confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer (Cervus elaphus) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS, described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.

Animals↗

PubMed1998

Evaluation of a restricted likelihood ratio test for mapping quantitative trait loci with extreme discordant sib pairs.

Risch and Zhang recently proposed to use extreme discordant sib pairs for mapping quantitative trait loci. Here, it is shown that the set of genetically possible distributions of the number of marker alleles in such sib-pairs is described by two inequalities. Thus, a likelihood ratio test analogous to Holmans's possible triangle test for affected sib pairs can be defined. The performance of this test is compared to the mean test considered by Risch and Zhang. For most of the genetic models considered, the mean test is slightly more powerful than the restricted likelihood ratio test. However, for models with a rare recessive gene (or equivalently a common dominant gene), the restricted likelihood ratio test is much more powerful.

Alleles↗

PubMed1995

On likelihood distance for outliers detection.

The likelihood distance has been widely used to detect outlying observations in data analysis. Cook and Weisberg (5) suggested that the likelihood distance may be compared to a chi 2 distribution for large samples. In this paper, we show that use of the chi 2 distribution is inappropriate. The results indicate that the likelihood distance does not follow an asymptotically chi 2 distribution. Instead, it converges to 0 in probability as the sample size increases. We show that for a nondegenerate limiting distribution, a multiplication factor related to the sample size n is needed. In general, the limiting distribution of this modified statistic is model-dependent.

Data Interpretation, Statistical↗

PubMed2002

Molecular systematics of the Eastern Fence Lizard (Sceloporus undulatus): a comparison of Parsimony, Likelihood, and Bayesian approaches.

Phylogenetic analysis of large datasets using complex nucleotide substitution models under a maximum likelihood framework can be computationally infeasible, especially when attempting to infer confidence values by way of nonparametric bootstrapping. Recent developments in phylogenetics suggest the computational burden can be reduced by using Bayesian methods of phylogenetic inference. However, few empirical phylogenetic studies exist that explore the efficiency of Bayesian analysis of large datasets. To this end, we conducted an extensive phylogenetic analysis of the wide-ranging and geographically variable Eastern Fence Lizard (Sceloporus undulatus). Maximum parsimony, maximum likelihood, and Bayesian phylogenetic analyses were performed on a combined mitochondrial DNA dataset (12S and 16S rRNA, ND1 protein-coding gene, and associated tRNA; 3,688 bp total) for 56 populations of S. undulatus (78 total terminals including other S. undulatus group species and outgroups). Maximum parsimony analysis resulted in numerous equally parsimonious trees (82,646 from equally weighted parsimony and 335 from weighted parsimony). The majority rule consensus tree derived from the Bayesian analysis was topologically identical to the single best phylogeny inferred from the maximum likelihood analysis, but required approximately 80% less computational time. The mtDNA data provide strong support for the monophyly of the S. undulatus group and the paraphyly of "S. undulatus" with respect to S. belli, S. cautus, and S. woodi. Parallel evolution of ecomorphs within "S. undulatus" has masked the actual number of species within this group. This evidence, along with convincing patterns of phylogeographic differentiation suggests "S. undulatus" represents at least four lineages that should be recognized as evolutionary species.

Animals↗

PubMed2006

Fundamental differences between the methods of maximum likelihood and maximum posterior probability in phylogenetics.

Using a four-taxon example under a simple model of evolution, we show that the methods of maximum likelihood and maximum posterior probability (which is a Bayesian method of inference) may not arrive at the same optimal tree topology. Some patterns that are separately uninformative under the maximum likelihood method are separately informative under the Bayesian method. We also show that this difference has impact on the bootstrap frequencies and the posterior probabilities of topologies, which therefore are not necessarily approximately equal. Efron et al. (Proc. Natl. Acad. Sci. USA 93:13429-13434, 1996) stated that bootstrap frequencies can, under certain circumstances, be interpreted as posterior probabilities. This is true only if one includes a non-informative prior distribution of the possible data patterns, and most often the prior distributions are instead specified in terms of topology and branch lengths. [Bayesian inference; maximum likelihood method; Phylogeny; support.].

Bayes Theorem↗

PubMed2006

Efficient likelihood computations with nonreversible models of evolution.

Recent advances in heuristics have made maximum likelihood phylogenetic tree estimation tractable for hundreds of sequences. Noticeably, these algorithms are currently limited to reversible models of evolution, in which Felsenstein's pulley principle applies. In this paper we show that by reorganizing the way likelihood is computed, one can efficiently compute the likelihood of a tree from any of its nodes with a nonreversible model of DNA sequence evolution, and hence benefit from cutting-edge heuristics. This computational trick can be used with reversible models of evolution without any extra cost. We then introduce nhPhyML, the adaptation of the nonhomogeneous nonstationary model of Galtier and Gouy (1998; Mol. Biol. Evol. 15:871-879) to the structure of PhyML, as well as an approximation of the model in which the set of equilibrium frequencies is limited. This new version shows good results both in terms of exploration of the space of tree topologies and ancestral G+C content estimation. We eventually apply it to rRNA sequences slowly evolving sites and conclude that the model and a wider taxonomic sampling still do not plead for a hyperthermophilic last universal common ancestor.

Algorithms↗

PubMed1999

Maximum likelihood analysis of gene-based and structure-based process partitions, using mammalian mitochondrial genomes.

Aligned protein-coding genes from 19 completely sequenced mammalian mitochondrial genomes were examined by parsimony and maximum likelihood analyses. Particular attention is given to a comparison between gene-based and structure-based data partitions. Because actual structures are not known for most of the mitochondrially encoded proteins, three different surrogate partitioning schemes were examined, each based on the identity of the consensus amino acid at a specific homologous position. One of the amino-acid-based partitioning schemes gave the highest likelihood, but that scheme was based on concordance with a well-corroborated phylogeny from an earlier parsimony analysis. The gene-based partitioning scheme gave a significantly higher likelihood compared to the only structure-based scheme examined that could be generated without prior assumptions about the phylogeny. Two contrasting phylogenetic inferences were supported by the analyses. Both unpartitioned analyses and analyses in which all partitions were constrained to have identical patterns of branch lengths supported ((Artiodactyla, Cetacea) (Perissodactyla, Carnivora)), whereas all analyses with that constraint relaxed supported (((Artiodactyla, Cetacea) Carnivora) Perissodactyla).

Amino Acid Sequence↗

PubMed1999

The distribution of the maximum likelihood estimator in up-and-down experiments for quantal dose-response data.

Standard maximum likelihood logistic or probit regression has been used in biopharmaceutical practice for inference about tolerance threshold distributions in situations where subjects (patients) have been allocated doses according to an up-and-down design. For example, a steeper dose-response curve than expected was reported in one such study. This article demonstrates that the maximum likelihood estimator systematically and considerably exaggerates the regression parameter with moderately large sample sizes. Thus a probable explanation for finding a steeper curve than expected is the method used to analyze the experiment, that is, the bias in the maximum likelihood estimator. An additional consequence of this bias is that the mean/median/ED50 are estimated with a misleading precision. In particular, confidence intervals are much too narrow. As a conclusion, we warn against conventional logistic or probit regression in combination with up-and-down designs.

Animals↗

PubMed2003

A likelihood-based, counterfactual approach to accounting for treatment failures in clinical trials.

Consider a two-armed, placebo-controlled trial in which subjects may experience treatment failure. For ethical reasons, it is necessary to administer emergency or rescue medications for such subjects. However, the emergency medications may bias the set of response measurements. When analyzing the data from clinical trials, the standard approach is to perform an intent-to-treat (ITT) analysis, wherein the data are analyzed according to treatment assignment. Secondary statistical analyses that supplement the ITT analysis can be performed to account for the impact of treatment failures and emergency medications. A likelihood-based, counterfactual approach to supplemental analyses uses the expectation maximization (EM) algorithm for parameter estimation and a likelihood ratio test to test the equality of the placebo and experimental treatment means for subjects who would not fail under either treatment assignment. A simulation study is performed to assess the operating characteristics of the likelihood ratio test. An example from the Asthma Clinical Research Network (ACRN) is used to draw comparisons between the standard ITT procedure and the developed supplemental analysis.

Clinical Trials as Topic↗

PubMed2000

Detection of disease genes by use of family data. I. Likelihood-based theory.

We present a class of likelihood-based score statistics that accommodate genotypes of both unrelated individuals and families, thereby combining the advantages of case-control and family-based designs. The likelihood extends the one proposed by Schaid and colleagues (Schaid and Sommer 1993, 1994; Schaid 1996; Schaid and Li 1997) to arbitrary family structures with arbitrary patterns of missing data and to dense sets of multiple markers. The score statistic comprises two component test statistics. The first component statistic, the nonfounder statistic, evaluates disequilibrium in the transmission of marker alleles from parents to offspring. This statistic, when applied to nuclear families, generalizes the transmission/disequilibrium test to arbitrary numbers of affected and unaffected siblings, with or without typed parents. The second component statistic, the founder statistic, compares observed or inferred marker genotypes in the family founders with those of controls or those of some reference population. The founder statistic generalizes the statistics commonly used for case-control data. The strengths of the approach include both the ability to assess, by comparison of nonfounder and founder statistics, the potential bias resulting from population stratification and the ability to accommodate arbitrary family structures, thus eliminating the need for many different ad hoc tests. A limitation of the approach is the potential power loss and/or bias resulting from inappropriate assumptions on the distribution of founder genotypes. The systematic likelihood-based framework provided here should be useful in the evaluation of both the relative merits of case-control and various family-based designs and the relative merits of different tests applied to the same design. It should also be useful for genotype-disease association studies done with the use of a dense set of multiple markers.

Alleles↗

PubMed1992

Truncation artifact suppression in cone-beam radionuclide transmission CT using maximum likelihood techniques: evaluation with human subjects.

Transverse image truncation can be a serious problem for human imaging using cone-beam transmission CT (CB-CT) implemented on a conventional rotating gamma camera. If this problem can be solved, CB-CT will be useful for attenuation compensation of SPECT images. This paper presents a reconstruction method to reduce or eliminate the artifacts resulting from the truncation. The method uses a previously published transmission maximum likelihood EM algorithm, adapted to the cone-beam geometry. The reconstruction method is evaluated qualitatively using three human subjects of various dimensions and various degrees of truncation. For the two smaller subjects, with moderate truncation, the maximum likelihood method is very successful, nearly eliminating the artifacts seen with conventional filtered backprojection of truncated geometries. The use of an expanded reconstructed space, which contains the entire transverse slice of the subject, is necessary for optimal truncation removal. For the largest subject investigated, the truncation was substantial, and the artifacts were only partially removed by the maximum likelihood reconstruction. Nonetheless, the images were qualitatively superior to those obtained with filtered backprojection. An added elliptical support prior moderately increased the rate of convergence, and helped to force a reasonable body contour.

Adult↗

PubMed1998

Maximum likelihood as a common computational framework in tomotherapy.

Tomotherapy is a dose delivery technique using helical or axial intensity modulated beams. One of the strengths of the tomotherapy concept is that it can incorporate a number of processes into a single piece of equipment. These processes include treatment optimization planning, dose reconstruction and kilovoltage/megavoltage image reconstruction. A common computational technique that could be used for all of these processes would be very appealing. The maximum likelihood estimator, originally developed for emission tomography, can serve as a useful tool in imaging and radiotherapy. We believe that this approach can play an important role in the processes of optimization planning, dose reconstruction and kilovoltage and/or megavoltage image reconstruction. These processes involve computations that require comparable physical methods. They are also based on equivalent assumptions, and they have similar mathematical solutions. As a result, the maximum likelihood approach is able to provide a common framework for all three of these computational problems. We will demonstrate how maximum likelihood methods can be applied to optimization planning, dose reconstruction and megavoltage image reconstruction in tomotherapy. Results for planning optimization, dose reconstruction and megavoltage image reconstruction will be presented. Strengths and weaknesses of the methodology are analysed. Future directions for this work are also suggested.

Algorithms↗

PubMed2025

vcfgl: a flexible genotype likelihood simulator for VCF/BCF files.

MOTIVATION: Accurate quantification of genotype uncertainty is pivotal in ensuring the reliability of genetic inferences drawn from NGS data. Genotype uncertainty is typically modeled using Genotype Likelihoods (GLs), which can help propagate measures of statistical uncertainty in base calls to downstream analyses. However, the effects of errors and biases in the estimation of GLs, introduced by biases in the original base call quality scores or the discretization of quality scores, as well as the choice of the GL model, remain under-explored. RESULTS: We present vcfgl, a versatile tool for simulating genotype likelihoods associated with simulated read data. It offers a framework for researchers to simulate and investigate the uncertainties and biases associated with the quantification of uncertainty, thereby facilitating a deeper understanding of their impacts on downstream analytical methods. Through simulations, we demonstrate the utility of vcfgl in benchmarking GL-based methods. The program can calculate GLs using various widely used genotype likelihood models and can simulate the errors in quality scores using a Beta distribution. It is compatible with modern simulators such as msprime and SLiM, and can output data in pileup, Variant Call Format (VCF)/BCF, and genomic VCF file formats, supporting a wide range of applications. The vcfgl program is freely available as an efficient and user-friendly software written in C/C++. AVAILABILITY AND IMPLEMENTATION: vcfgl is freely available at https://github.com/isinaltinkaya/vcfgl.

Software↗