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PubMed1999

Phylogenetic signals from point mutations and polymorphic Alu insertions.

Allelic frequency data derived from five polymorphic Alu insertion loci and five point mutation polymorphic loci were compared to determine their ability to infer phylogenetic relationships among human populations. While point mutation polymorphisms inferred a monophyletic Caucasian clade that is corroborated by other studies, these data failed to support the generally accepted monophyly of Orientals with native Americans. In addition, there is less statistical bootstrap support for the maximum-likelihood tree derived from the point mutation polymorphisms as compared to those generated from either the Alu insertion data or the combined Alu insertion + point mutation data. The Alu data and the combined Alu insertion + point mutation data inferred a monophyletic relationship among the Oriental and native American populations. The Alu insertion data and the combined Alu insertion + point mutation data also displayed two separate, well defined, tight clusters of the Caucasian and the Oriental + native American populations which was not inferred from the point mutation data. These findings indicate greater phylogenetic information contained in Alu insertion frequencies than in allelic frequencies derived from point-mutations.

Alleles↗
PubMed2004

Measurement in clinical vs. biological medicine: the Rasch model as a bridge on a widening gap.

In the dominant Bio-medical paradigm Medicine is mostly Biology applied to Man. Measurement in Biology stems from physical sciences and has established validity. This is not the case for whole-person variables such as behaviors and psychic conditions (disability, pain, knowledges). The very existence of these variables can only be inferred by observing samples of representative behaviors. The quantity of the inferred variable may only come from subjective and discrete counts (scores) of events (coming in a questionnaire). Contemporary statistics demonstrated that raw scores intrinsically lack fundamental properties for scientific measurement, whatever their algebraic manipulations. This adds to the stigmatization of Clinical Medicine as "soft science", compared to Bio-medicine. In the 1960s Georg Rasch inaugurated a new statistical approach allowing transformation of raw scores into objective linear measures comparable to physical measures. This may help the Bio-medical paradigm to redirect resources from laboratory bench back to bedside.

Biology↗
PubMed2025

Variable rates of SARS-CoV-2 evolution in chronic infections.

An important feature of the evolution of the SARS-CoV-2 virus has been the emergence of highly mutated novel variants, which are characterised by the gain of multiple mutations relative to viruses circulating in the general global population. Cases of chronic viral infection have been suggested as an explanation for this phenomenon, whereby an extended period of infection, with an increased rate of evolution, creates viruses with substantial genetic novelty. However, measuring a rate of evolution during chronic infection is made more difficult by the potential existence of compartmentalisation in the viral population, whereby the viruses in a host form distinct subpopulations. We here describe and apply a novel statistical method to study within-host virus evolution, identifying the minimum number of subpopulations required to explain sequence data observed from cases of chronic infection, and inferring rates for within-host viral evolution. Across nine cases of chronic SARS-CoV-2 infection in hospitalised patients we find that non-trivial population structure is relatively common, with five cases showing evidence of more than one viral population evolving independently within the host. The detection of non-trivial population structure was more common in severely immunocompromised individuals (p = 0.04, Fisher's Exact Test). We find cases of within-host evolution proceeding significantly faster, and significantly slower, than that of the global SARS-CoV-2 population, and of cases in which viral subpopulations in the same host have statistically distinguishable rates of evolution. Non-trivial population structure was associated with high rates of within-host evolution that were systematically underestimated by a more standard inference method.

Humans↗
PubMed1992

Heterogeneity in the substitution process of amino acid sites of proteins coded for by mitochondrial DNA.

Several forms of maximum likelihood models are applied to aligned amino acid sequence data coded for in the mitochondrial DNA of six species (chicken, frog, human, bovine, mouse, and rat). These models range in form from relatively simple models of the type currently used for inferring phylogenetic tree structure to models more complex than those that have been used previously. No major discrepancies between the optimal trees inferred by any of these methods are found, but there are huge differences in adequacy of fit. A very significant finding is that the fit of any of these models is vastly improved by allowing a certain proportion of the amino acid sites to be invariant. An even more important, although disquieting, finding is that none of these models fits well, as judged by standard statistical criteria. The primary reason for this is that amino acid sites undergo substitution according to a process that is very heterogeneous. Because most phylogenetic inference is accomplished by choosing the optimal tree under the assumption that a homogeneous process is acting on the sites, the potential invalidity of some such conclusions is raised by this article's results. The seriousness of this problem depends upon the robustness of the phylogenetic inferential procedure to departures from the underlying model.

Amino Acid Sequence↗
PubMed2003

Bubbles: a unifying framework for low-level statistical properties of natural image sequences.

Recently, different models of the statistical structure of natural images have been proposed. These models predict properties of biological visual systems and can be used as priors in Bayesian inference. The fundamental model is independent component analysis, which can be estimated by maximization of the sparsenesses of linear filter outputs. This leads to the emergence of principal simple cell properties. Alternatively, simple cell properties are obtained by maximizing the temporal coherence in natural image sequences. Taking account of the basic dependencies of linear filter outputs permit modeling of complex cells and topographic organization as well. We propose a unifying framework for these statistical properties, based on the concept of spatiotemporal activity "bubbles."A bubble means here an activation of simple cells (linear filters) that is contiguous both in space (the cortical surface) and in time.

Bayes Theorem↗
PubMed2006

Meehl's contribution to clinical versus statistical prediction.

Paul E. Meehl's work on the clinical versus statistical prediction controversy is reviewed. His contributions included the following: putting the controversy center stage in applied psychology; clarifying concepts underpinning the debate (especially his crucial distinction between ways of gathering data and ways of combining them) as well as establishing that the controversy was real and not concocted, analyzing clinical inference from both theoretical and probabilistic points of view, and reviewing studies that compared the accuracy of these 2 methods of data combination. Meehl's (1954/1996) conclusion that statistical prediction consistently outperforms clinical judgment has stood up extremely well for half a century. His conceptual analyses have not been significantly improved since he published them in the 1950s and 1960s. His work in this area contains several citation classics, which are part of the working knowledge of all competent applied psychologists today.

Famous Persons↗
PubMed2005

Comparative genome analyses of Arabidopsis spp.: inferring chromosomal rearrangement events in the evolutionary history of A. thaliana.

Comparative genome analysis is a powerful tool that can facilitate the reconstruction of the evolutionary history of the genomes of modern-day species. The model plant Arabidopsis thaliana with its n = 5 genome is thought to be derived from an ancestral n = 8 genome. Pairwise comparative genome analyses of A. thaliana with polyploid and diploid Brassicaceae species have suggested that rapid genome evolution, manifested by chromosomal rearrangements and duplications, characterizes the polyploid, but not the diploid, lineages of this family. In this study, we constructed a low-density genetic linkage map of Arabidopsis lyrata ssp. lyrata (A. l. lyrata; n = 8, diploid), the closest known relative of A. thaliana (MRCA approximately 5 Mya), using A. thaliana-specific markers that resolve into the expected eight linkage groups. We then performed comparative Bayesian analyses using raw mapping data from this study and from a Capsella study to infer the number and nature of rearrangements that distinguish the n = 8 genomes of A. l. lyrata and Capsella from the n = 5 genome of A. thaliana. We conclude that there is strong statistical support in favor of the parsimony scenarios of 10 major chromosomal rearrangements separating these n = 8 genomes from A. thaliana. These chromosomal rearrangement events contribute to a rate of chromosomal evolution higher than previously reported in this lineage. We infer that at least seven of these events, common to both sets of data, are responsible for the change in karyotype and underlie genome reduction in A. thaliana.

Arabidopsis↗
PubMed2002

Statistical estimation of parameters in a disease transmission model: analysis of a Cryptosporidium outbreak.

Population dynamic models, commonly used tools in the study of epidemics and other complex population processes, are implicit non-linear mathematical equations. Inference based on such models can be difficult due to the problems associated with high dimensional parameters that may be non-identified and complex likelihood functions that are difficult to maximize. To address a problem of non-identifiability due to collinearity of parameter estimates in a mathematical model of the 1993 Milwaukee Cryptosporidium parvum outbreak, we examined the utility of a constrained profile likelihood approach. This method was used to study two parameters of interest from the mathematical model: (i). the rate of secondary transmission; (ii). the proportional increase in primary transmission due to water treatment failure. The estimated values of these parameters were shown to depend strongly on poorly understood aspects of Cryptosporidium epidemiology such as asymptomatic proportion and the population immune status. Our analysis demonstrated that the combination of a disease transmission model and a constrained profile likelihood procedure provides an effective approach for inference and estimation of important parameters regulating infectious disease outbreaks.

Animals↗
PubMed1981

When doctors meet numbers.

A Statistical Skills Self-Assessment Questionnaire (SAQ) was developed using hypothetical clinical questions to explore respondents' mastery of vocabulary and rules of inference that seem relevant to the use of quantitative information. The SAQ was administered to 281 subjects, including 36 medical students, 45 interns and residents, 49 physicians engaged in research and 151 physicians in full-time practice. All groups of subjects showed frequent lack of consensus on the meaning of terms in common use (e.g., "false-positive rate" and "p values") and unfamiliarity with some important principles in quantitative inference (e.g., the Central Limit Theorem and Regression to the Mean). Subjects often seemed willing to draw conclusions unsupported by available data. Performance on the SAQ was inversely correlated with length of time since graduation from medical school, and practicing physicians tended to err more frequently than the other three groups.

Education, Medical↗
PubMed2004

MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment.

With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.

Databases, Genetic↗
PubMed1999

Instrumental variables when evaluating screening trials: estimating the benefit of detecting cancer by screening.

When evaluating the benefit of detecting cancer by screening we try to answer the question, 'what would a screen detected subject's outcome have been if his/her cancer had progressed to clinical detection'. By 'outcome' we mean survival time, cancer size and stage, lead time effects and more. Because only an unethical study can answer it directly, researchers have attempted to answer the question indirectly using data from randomized cancer screening studies (subjects randomized to study (screened) or control (not screened)). Inferences are made by first selecting the cancer cohort (those subjects who are found to have cancer), then comparing subjects having screen detected cancers to subjects having clinically detected cancers. However, there are two difficulties with this approach: (i) because screening (intends to) detect cancers early, at the trial's end the study group contains more cancer cases than the control group and so the cancer cohort has some unidentified control subjects missing (that is, subjects having cancer during the screening period that have not yet been clinically detected); (ii) because screen detected cancers (may) differ from clinically detected cancers, the comparison group should include only a (non-identified) subset of the cancer cohort's control subjects (that is, only those control subjects having cancers that would have been screen detected). Statistical literature acknowledges these difficulties and attempts to solve them separately, but without success; those methods do not yield meaningful causal inferences and admit substantial bias. Recently, Angrist, Imbens and Rubin and Imbens and Rubin provide a framework for instrumental variable methods that we interpret as allowing us to make causal inferences with incompletely identified comparison groups. We apply their framework to evaluating cancer screening trials and find that we may simultaneously accommodate both difficulties while giving a meaningful answer to the question posed above. Using data from a breast cancer screening trial we demonstrate the general method with a variety of outcome measures and extensions.

Adult↗
PubMed2006

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.

Chromosome Mapping↗
PubMed2003

REDUCE: An online tool for inferring cis-regulatory elements and transcriptional module activities from microarray data.

REDUCE is a motif-based regression method for microarray analysis. The only required inputs are (i) a single genome-wide set of absolute or relative mRNA abundances and (ii) the DNA sequence of the regulatory region associated with each gene that is probed. Currently supported organisms are yeast, worm and fly; it is an open question whether in its current incarnation our approach can be used for mouse or human. REDUCE uses unbiased statistics to identify oligonucleotide motifs whose occurrence in the regulatory region of a gene correlates with the level of mRNA expression. Regression analysis is used to infer the activity of the transcriptional module associated with each motif. REDUCE is available online at http://bussemaker.bio.columbia.edu/reduce/. This web site provides functionality for the upload and management of microarray data. REDUCE analysis results can be viewed and downloaded, and optionally be shared with other users or made publicly accessible.

Algorithms↗
PubMed1994

Estimation of chronic phase duration in Ph-positive chronic myelocytic leukemia treated with busulfan: statistical analysis on Japanese patients.

Chronic phase duration was analyzed in 148 Japanese patients with Ph-positive chronic myelocytic leukemia (CML) treated only with busulfan, and multivariate proportional hazard models were constructed to infer the prognosis of a given patient, and to evaluate the prognostic importance of the clinical findings. To make an accurate estimation of an individual patient's prognosis, we introduced a piecewise linear model for describing a hazard function in each prognostic variable. Of the 11 prognostic variables initially analyzed, the absolute count of peripheral basophils was revealed as the most important prognostic factor. A high basophil count was clearly a factor in cases with poor prognosis. The statistical model selection based on the criteria of model approximation finally suggested a model with three covariates; absolute count of peripheral basophils, platelet count, and presence or absence of additional chromosome abnormalities superimposed on the translocation of 9;22. By analyses based on the above model, we estimated that the median of the chronic phase duration for the patients with poor and good prognosis was 12 and 75 months, respectively. The piecewise linear model was well suited for an accurate estimation of an individual patient's prognosis. Thus, the model constructed here would become a reliable control to evaluate investigational approaches, such as hydroxyurea, interferons, and/or bone marrow transplantation.

Adolescent↗
PubMed1999

Toward evidence-based medical statistics. 1: The P value fallacy.

An important problem exists in the interpretation of modern medical research data: Biological understanding and previous research play little formal role in the interpretation of quantitative results. This phenomenon is manifest in the discussion sections of research articles and ultimately can affect the reliability of conclusions. The standard statistical approach has created this situation by promoting the illusion that conclusions can be produced with certain "error rates," without consideration of information from outside the experiment. This statistical approach, the key components of which are P values and hypothesis tests, is widely perceived as a mathematically coherent approach to inference. There is little appreciation in the medical community that the methodology is an amalgam of incompatible elements, whose utility for scientific inference has been the subject of intense debate among statisticians for almost 70 years. This article introduces some of the key elements of that debate and traces the appeal and adverse impact of this methodology to the P value fallacy, the mistaken idea that a single number can capture both the long-run outcomes of an experiment and the evidential meaning of a single result. This argument is made as a prelude to the suggestion that another measure of evidence should be used--the Bayes factor, which properly separates issues of long-run behavior from evidential strength and allows the integration of background knowledge with statistical findings.

Bayes Theorem↗
PubMed2004

Recent developments in genetic data analysis: what can they tell us about human demographic history?

Over the last decade, a number of new methods of population genetic analysis based on likelihood have been introduced. This review describes and explains the general statistical techniques that have recently been used, and discusses the underlying population genetic models. Experimental papers that use these methods to infer human demographic and phylogeographic history are reviewed. It appears that the use of likelihood has hitherto had little impact in the field of human population genetics, which is still primarily driven by more traditional approaches. However, with the current uncertainty about the effects of natural selection, population structure and ascertainment of single-nucleotide polymorphism markers, it is suggested that likelihood-based methods may have a greater impact in the future.

DNA, Satellite↗