Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “statistical inference”

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

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

At least 1,045 records · Page 58Linked to original sources
PubMed1995

Multireader receiver operating characteristic studies: a comparison of study designs.

RATIONALE AND OBJECTIVES: Traditionally, multireader receiver operating characteristic (ROC) studies have used a "paired-case, paired-reader" design. The statistical power of such a design for inferences about the relative accuracies of the tests was assessed and compared with alternative designs. METHODS: The noncentrality parameter of an F statistic was used to compute power as a function of the reader and patient sample sizes and the variability and correlation between readings. RESULTS: For a fixed-power and Type I error rate, the traditional design reduces the number of verified cases required. A hybrid design, in which each reader interprets a different sample of patients, reduces the number of readers, total readings, and reading required per reader. The drawback is a substantial increase in the number of verified cases. CONCLUSION: The ultimate choice of study design depends on the nature of the tests being compared, limiting resources, a priori knowledge of the magnitude of the correlations and variability and logistic complexity.

Analysis of Variance↗
PubMed1989

A knowledge-based system for data analysis and interpretation.

Traditionally, statistical packages are employed to derive or infer facts about a Universe of Discourse through data analysis and interpretation. It is analysis that serves to transform data into information. Statistical packages provide the users with relatively easy-to-use and powerful mechanics of data analysis, but up to now they do not provide much help with the design and strategies of the analysis. As such, there is a risk of misuse of these packages by statistically inexperienced users. We propose the use of knowledge-based interfaces to support this category of users in statistical evaluations. This paper discusses our experiences from the implementation of a knowledge-based system called MAXITAB. It provides guidance in the processes of data analysis and interpretation and has been programmed as an interface to the statistical package MINITAB.

Data Interpretation, Statistical↗
PubMed2002

Test scores, measurement, and the use of analysis of variance: an historical overview.

In order to establish a firmer statistical foundation from which to draw inferences from factorial design study data, transformations of raw scores are occasionally employed in order to make their distributions more generally normal or to provide linearity. To date, few studies have been conducted to determine whether or not raw scores transformed or otherwise constitute measures for the purposes of statistical analysis. In this article, the historical development of the understanding of the term "measurement" by researchers in the social sciences is traced, and the development and use of One and Two-way ANOVA by researchers in the social sciences are presented and evaluated.

Analysis of Variance↗
PubMed1995

A frequency-dependent significance test for parsimony.

We describe techniques for assessing evolutionary trees constructed by the parsimony criteria, when sequences exhibit irregular base compositions. In particular, we extend a recently described frequency-dependent significance test to handle any number of taxa and describe a modification of the Kishino-Hasegawa sites test. These modifications are useful for detecting historical signals beyond those patterns which arise purely from irregular base compositions between the compared sequences. We apply the test to extend our earlier studies on chloroplast origins using 16S rDNA sequences, where a failure to compensate for irregular base compositions between the compared sequences provides statistically significant support for unjustified phylogenetic inferences. We also describe how the techniques can be modified to determine how "tree-like" data are, given independent variation in the base frequencies.

Models, Statistical↗
PubMed2004

Tropical geometry of statistical models.

This article presents a unified mathematical framework for inference in graphical models, building on the observation that graphical models are algebraic varieties. From this geometric viewpoint, observations generated from a model are coordinates of a point in the variety, and the sum-product algorithm is an efficient tool for evaluating specific coordinates. Here, we address the question of how the solutions to various inference problems depend on the model parameters. The proposed answer is expressed in terms of tropical algebraic geometry. The Newton polytope of a statistical model plays a key role. Our results are applied to the hidden Markov model and the general Markov model on a binary tree.

Algorithms↗
PubMedDate not supplied

Confidence intervals for cost-effectiveness ratios: an application of Fieller's theorem.

Application of cost-effectiveness analysis (CEA) is growing rapidly in health care. Two general approaches to analysis are differentiated by the type of data available: (i) deterministic models based upon secondary analysis of retrospective data from one or more trials and other sources; and (ii) stochastic analyses in which the design of a randomized controlled trial is adapted to collect prospectively patient-specific data on costs and effectiveness. An important methodological difference between these two approaches is in how uncertainty is handled. Deterministic CEA models typically rely upon sensitivity analysis to determine the robustness of findings to alternative assumptions, whereas stochastic (CEA) analysis, as part of prospective studies, permits the use of conventional statistical methods on the cost and effectiveness data for both inference (hypothesis testing) and estimation. This paper presents a procedure for the statistical analysis of cost-effectiveness data, with specific application to those studies for which effectiveness is measured as a binary outcome. Specifically, Fieller's Theorem was used to calculate confidence intervals for ratios of the two random variables of between-treatment differences in observed costs and effectiveness, i.e. the incremental cost-effectiveness ratio.

Computer Simulation↗
PubMed2004

False-positive selection identified by ML-based methods: examples from the Sig1 gene of the diatom Thalassiosira weissflogii and the tax gene of a human T-cell lymphotropic virus.

Sexually induced gene 1 (Sig1) in the centric diatom Thalassiosira weissflogii is considered to encode a gamete recognition protein. Sorhannus (2003) analyzed nucleotide sequences of Sig1 using parsimony analysis and the maximum-likelihood (ML)-based Bayesian method for inferring positive selection at single amino acid sites and reported that positively selected sites were detected by the latter method but not by the former. He then concluded that for this type of study, the ML-based method is more reliable than parsimony analysis. Here we show that his results apparently represent false-positive cases of the ML-based method and that there is no solid evidence that this gene contains positively selected sites. We further demonstrate that in the tax gene of human T-cell lymphotropic virus type I (HTLV-I), all codon sites, including invariable sites, can be inferred as positively selected sites by the ML-based method. These observations indicate that the ML-based method may produce many false-positive sites. One of the main reasons for the occurrence of false positives is that in the ML-based method, codon sites are grouped into several categories, with different nonsynonymous/synonymous rate ratios (omegas), on a purely statistical basis, and positive selection is inferred indirectly by examining whether the average omega for each category is greater than 1. In parsimony analysis, however, the evolutionary change of nucleotides at each codon site is examined. For this reason, parsimony-based methods rarely produce false positives and are safer than ML-based methods for detecting positive selection at individual codon sites, although a large number of sequences are necessary.

Bayes Theorem↗
PubMed2004

Applications of Bayesian statistical methods in microarray data analysis.

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data. Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Bayes Theorem↗
PubMed1997

Inference for phylogenies under a hybrid parsimony method: evolutionary-symmetric transversion parsimony.

A new method is proposed for inferring topology for evolutionary trees. Existing methods have complementary strengths and weaknesses. Maximum and transversion parsimony are powerful methods, but they lack statistical consistency, that is, they do not always infer the correct tree as the sequence length becomes very large. Evolutionary parsimony overcomes this deficiency, but it may lack sufficient power when sequence length is small (less than 1000 aligned nucleotides; Sinsheimer, Lake, and Little, 1996, Biometrics 52, 193-210). Our proposed method, evolutionary-symmetric transversion parsimony, is a hybrid that retains the consistency of evolutionary parsimony, while increasing power by incorporating a modified form of transversion parsimony within a statistical model. The method requires choice of a parameter gamma that represents the prior probability that symmetric transversion parsimony yields consistent results. Properties of the method are assessed for a variety of choices of gamma in a large simulation study. In general, inference under the evolutionary-symmetric transversion parsimony has more discriminating power than inference under evolutionary parsimony and is better calibrated than inference under symmetric transversion parsimony. The results are quite robust to the choice of gamma, indicating a value of 0.90 as a reasonable overall choice when the true value of gamma ranges between 0.85 to 1.00. Our method is, like evolutionary parsimony and maximum parsimony, computationally straightforward. The same statistical approach can be applied to combine evolutionary parsimony with other inconsistent methods, such as maximum parsimony, but at the expense of more difficult computations.

Animals↗
PubMed1978

Application of statistical methods in quantitative microscopy.

The purpose of the paper is twofold: first, to describe the aspects of quantitative microscopy where statistical ideas are being applied today; and second, to describe some ways in which more complete use of statistics could improve quantitative microscopy. The typical estimation problem in quantitative microscopy is described with emphasis on the modelling of variability in estimation. A Poisson field model for imbedded particulates is used to illustrate the value of theoretical treatments of variability. Sampling, estimation and multivariate analysis are cited as areas of statistics presently used by quantitative microscopists. The potential role of statistics in quantitative microscopy is discussed. Examples from particle sizing and characterization of alveolar lung structure illustrate the value of statistical ideas. Automatic image analysers, while making the collection of large amounts of data feasible, have complicated statistical estimation. An investigation of the size distribution of random chord lengths through alveolar chambers illustrates how statistical methodology must be modified for valid inference.

Animals↗
PubMed1992

Inference of horizontal genetic transfer from molecular data: an approach using the bootstrap.

Inconsistencies in taxonomic relationships implicit in different sets of nucleic acid sequences potentially result from horizontal transfer of genetic material between genomes. A nonparametric method is proposed to determine whether such inconsistencies are statistically significant. A similarity coefficient is calculated from ranked pairwise identities and evaluated against a distribution of similarity coefficients generated from resampled data. Subsequent analyses of partial data sets, obtained by the elimination of individual taxa, identify particular taxa to which the significance may be attributed, and can sometimes help in distinguishing horizontal genetic transfer from inconsistencies due to convergent evolution or variation in evolutionary rate. The method was successfully applied to data sets that were not found to be significantly different with existing methods that use comparisons of phylogenetic trees. The new statistical framework is also applicable to the inference of horizontal transfer from restriction fragment length polymorphism distributions and protein sequences.

Animals↗
PubMed2003

Inference on haplotype effects in case-control studies using unphased genotype data.

A variety of statistical methods exist for detecting haplotype-disease association through use of genetic data from a case-control study. Since such data often consist of unphased genotypes (resulting in haplotype ambiguity), such statistical methods typically apply the expectation-maximization (EM) algorithm for inference. However, the majority of these methods fail to perform inference on the effect of particular haplotypes or haplotype features on disease risk. Since such inference is valuable, we develop a retrospective likelihood for estimating and testing the effects of specific features of single-nucleotide polymorphism (SNP)-based haplotypes on disease risk using unphased genotype data from a case-control study. Our proposed method has a flexible structure that allows, among other choices, modeling of multiplicative, dominant, and recessive effects of specific haplotype features on disease risk. In addition, our method relaxes the requirement of Hardy-Weinberg equilibrium of haplotype frequencies in case subjects, which is typically required of EM-based haplotype methods. Also, our method easily accommodates missing SNP information. Finally, our method allows for asymptotic, permutation-based, or bootstrap inference. We apply our method to case-control SNP genotype data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) Genetics study and identify two haplotypes that appear to be significantly associated with type 2 diabetes. Using the FUSION data, we assess the accuracy of asymptotic P values by comparing them with P values obtained from a permutation procedure. We also assess the accuracy of asymptotic confidence intervals for relative-risk parameters for haplotype effects, by a simulation study based on the FUSION data.

Algorithms↗
PubMed1983

Hospital statistics that contribute to epidemiology.

Can a hospital population be used as a set of reference to a general population? The purpose of the present paper is to underline several conditions for which hospital statistics might be used for epidemiological inference, e.g. to map-out the pattern and the frequency of diseases in a general population. In several countries the population base for hospital in-patients is known and well-documented. Under this assumption, the frequency of diseases that require hospitalization, such as meningitis or some birth defects might be estimated in relation to the general population. In other instances, the population base remains unknown, all hospitals from a given region not being able to provide uniform statistics. There might be false associations called Berkson's bias. However, a study of the frequency of some diseases estimated from a large population of in-patients gave surprisingly similar results to those obtained from well defined populations. It is concluded that, even when the population base is unknown, hospital statistics might already contribute largely to epidemiology under two conditions: (1) the hospital sample must be large enough (above 10 000 patients) and include a case mix from several specialties (e.g., all cases from the departments of internal medicine and surgery), (2) discharge abstracts should be available, reliable and comparable for all inpatients and include all diagnoses relevant to each case. The importance of accuracy and uniformity in registration is stressed as well as the need to develop more widely population-based hospital statistics.

Diagnosis-Related Groups↗
PubMed2004

Comparison of haplotype inference methods using genotypic data from unrelated individuals.

OBJECTIVE: Haplotypes are gaining popularity in studies of human genetics because they contain more information than does a single gene locus. However, current high-throughput genotyping techniques cannot produce haplotype information. Several statistical methods have recently been proposed to infer haplotypes based on unphased genotypes at several loci. The accuracy, efficiency, and computational time of these methods have been under intense scrutiny. In this report, our aim was to evaluate haplotype inference methods for genotypic data from unrelated individuals. METHODS: We compared the performance of three haplotype inference methods that are currently in use--HAPLOTYPER, hap, and PHASE--by applying them to a large data set from unrelated individuals with known haplotypes. We also applied these methods to coalescent-based simulation studies using both constant size and exponential growth models. The performance of these methods, along with that of the expectation-maximization algorithm, was further compared in the context of an association study. RESULTS: While the algorithm implemented in the software PHASE was found to be the most accurate in both real and simulated data comparisons, all four methods produced good results in the association study.

Algorithms↗
PubMed2005

Haplotype effects on human survival: logistic regression models applied to unphased genotype data.

Haplotype based linkage disequilibrium (LD) mapping exhibits higher power than the single locus approach because it makes use of the LD information contained in the flanking markers. New statistical methods have been proposed to help to infer haplotype effects on human diseases using multi-locus genotype data collected from unrelated individuals. In this paper, we introduce a statistical procedure for measuring haplotype effects on human survival using the popular logistic regression model with haplotype based parameterizations. By modeling haplotype frequency as a function of age, our model infers haplotype effects by estimating and testing the slope parameters under different genetic mechanisms (multiplicative, dominant, or recessive). In addition, by estimating the sex-specific slope parameters, our model allows the detection of sex-specific haplotype effects or haplotype-sex interactions. As an example, we apply our model to an empirical dataset on a stress related gene, interleukin-6, to look for haplotypes that affect individual survival and for haplotype-sex interactions. We show that our logistic regression based haplotype model can be a helpful tool for researchers interested in the genetics of human aging and longevity.

Female↗
PubMed2004

A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm.

It is now widely accepted that haplotypic information can be of great interest for investigating the role of a candidate gene in the etiology of complex diseases. In the absence of family data, haplotypes cannot be deduced from genotypes, except for individuals who are homozygous at all loci or heterozygous at only one site. Statistical methodologies are therefore required for inferring haplotypes from genotypic data and testing their association with a phenotype of interest. Two maximum likelihood algorithms are often used in the context of haplotype-based association studies, the Newton-Raphson (NR) and the Expectation-Maximisation (EM) algorithms. In order to circumvent the limitations of both algorithms, including convergence to local minima and saddle points, we here described how a stochastic version of the EM algorithm, referred to as SEM, could be used for testing haplotype-phenotype association. Statistical properties of the SEM algorithm were investigated through a simulation study for a large range of practical situations, including small/large samples and rare/frequent haplotypes, and results were compared to those obtained by use of the standard NR algorithm. Our simulation study indicated that the SEM algorithm provides results similar to those of the NR algorithm, making the SEM algorithm of great interest for haplotype-based association analysis, especially when the number of polymorphisms is quite large.

Algorithms↗
PubMed2000

On testing for drug/chemical interactions: definitions and inference.

The notion of zero interaction in the statistical literature is not always equivalent to what is found in the toxicology literature. A discussion about when they are the same is provided here. Design issues are of paramount importance in the analysis of drug combinations (mixtures of chemicals) when the number of constituents in the combination is larger than, say, three as the usual factorial designs are not feasible. An economical design necessary and sufficient to support the estimation of an additivity model is single drug (chemical) dose-response data. Once estimated, the additivity surface can be used to make comparisons to the observed data at combination points of interest. Examples are provided to demonstrate the methods.

Algorithms↗
PubMed2006

Comparative analysis of haplotype association mapping algorithms.

BACKGROUND: Finding the genetic causes of quantitative traits is a complex and difficult task. Classical methods for mapping quantitative trail loci (QTL) in miceuse an F2 cross between two strains with substantially different phenotype and an interval mapping method to compute confidence intervals at each position in the genome. This process requires significant resources for breeding and genotyping, and the data generated are usually only applicable to one phenotype of interest. Recently, we reported the application of a haplotype association mapping method which utilizes dense genotyping data across a diverse panel of inbred mouse strains and a marker association algorithm that is independent of any specific phenotype. As the availability of genotyping data grows in size and density, analysis of these haplotype association mapping methods should be of increasing value to the statistical genetics community. RESULTS: We describe a detailed comparative analysis of variations on our marker association method. In particular, we describe the use of inferred haplotypes from adjacent SNPs, parametric and nonparametric statistics, and control of multiple testing error. These results show that nonparametric methods are slightly better in the test cases we study, although the choice of test statistic may often be dependent on the specific phenotype and haplotype structure being studied. The use of multi-SNP windows to infer local haplotype structure is critical to the use of a diverse panel of inbred strains for QTL mapping. Finally, because the marginal effect of any single gene in a complex disease is often relatively small, these methods require the use of sensitive methods for controlling family-wise error. We also report our initial application of this method to phenotypes cataloged in the Mouse Phenome Database. CONCLUSION: The use of inbred strains of mice for QTL mapping has many advantages over traditional methods. However, there are also limitations in comparison to the traditional linkage analysis from F2 and RI lines. Application of these methods requires careful consideration of algorithmic choices based on both theoretical and practical factors. Our findings suggest general guidelines, though a complete evaluation of these methods can only be performed as more genetic data in complex diseases becomes available.

Algorithms↗