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At least 1,351 records · Page 75Linked to original sources
PubMed1999

A possible approach for researching purported spirit communication: an empirical-anecdotal investigation.

Can a digital clock that behaves erratically function as a flexible, open (random event generator-like) timing system to investigate purported communication with deceased individuals? If such theophysical effects exist, what implications do they have for mind-body and integrative medicine? Stimulated by research into spirit communication by Schwartz, Russek et al. (1999), Elayne Russek (ER) hypothesized that her deceased husband, noted cardiologist Henry I. Russek (HIR), might be influencing a digital clock that had become erratic. A repeated measures-within-subject experiment was designed such that daytime baseline data could be compared with the results of requests to slow or speed up the erratic clock during the night. Unfortunately, the AM-PM light was no longer functional, which meant that AM-PM information had to be inferred by applying an inference rule to the complete set of data. When this was done, a statistically significant effect was obtained. ER communicated to HIR that the experiment was inadequate because of the AM-PM defect and asked him for a "sign" that he was involved. She unexpectedly awoke in the middle of the night to find the AM-PM light flashing. The light became functional the following day, and subsequent data collection confirmed the inference rule. The combined empirical plus anecdotal nature of research in this area are considered, along with implications of the hypothesis for mind-body and integrative medicine.

Humans↗
PubMed2005

Models of brain function in neuroimaging.

Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamics. However, they all have to be internally consistent because they model the same thing. This consistency encompasses many levels of description and places constraints on the statistical models, adopted for data analysis, and the experimental designs they embody. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. We then turn to basic statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about where neuronal responses are expressed. By incorporating biophysical constraints, these basic models can be finessed and, in a dynamic setting, rendered causal. This allows us to infer how interactions among brain regions are mediated.

Biophysical Phenomena↗
PubMed2002

A duplication growth model of gene expression networks.

MOTIVATION: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network. RESULTS: Several whole-genome expression time series data sets from yeast microarray experiments were analyzed using a Markov-modeling method (Dewey and Galas, FUNC: Integr. Genomics, 1, 269-278, 2001) to infer an approximation to the underlying genetic network. We found that the global statistical properties of all the resulting networks are similar. The overall structure of these biological networks is distinctly different from that of other recently studied networks such as the Internet or social networks. These biological networks show hierarchical, hub-like structures that have some properties similar to a class of graphs known as small world graphs. Small world networks exhibit local cliquishness while exhibiting strong global connectivity. In addition to the small world properties, the biological networks show a power law or scale free distribution of connectivities. An inverse power law, N(k) approximately k(-3/2), for the number of vertices (genes) with k connections was observed for three different data sets from yeast. We propose network growth models based on gene duplication events. Simulations of these models yield networks with the same combination of global graphical properties that we inferred from the expression data.

Algorithms↗
PubMed2006

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Bayes Theorem↗
PubMed2004

Statistical analysis of genomic tag data.

We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies. Inferences can be made from data obtained by sequencing a subset of the library. The statistical approaches outlined in this paper are divided into three parts. First, we demonstrate the application of classical capture-recapture theory to the question of library complexity, i.e. the number of unique fragments in the library. Simulation studies verify the accuracy, for sample sizes of magnitudes typical in genomic studies, of the formulas we use to make our estimates. Second, we present a straightforward statistical cost analysis of tag experiments designed to uncover either disease-causing pathogens or new genes. Third, we develop a hidden Markov model approach to karyotyping a sample using a tag library derived from the sample's genomic DNA. While the resolution of the approach depends upon the number of tags sequenced from the library, we show via simulation that copy number alterations can be reliably detected for lengths as small as 1 Mb, even when a moderate number of tags are sequenced. Simulations predict very good specificity as well. Finally, all three of our approaches are applied to data from real tag library experiments. The hidden Markov model results are in line with what was expected from simulation, and genomic alterations found by applying the method to a cancer cell line library are confirmed using PCR. The methods and data described in this paper are contained in an R package, tagAnalysis, freely available at http://meyerson.dfci.harvard.edu/~tl974/tagAnalysis.

Journal Article↗
PubMed2004

When clinical description becomes statistical prediction.

This article reconsiders the issue of clinical versus statistical prediction. The term clinical is widely used to denote 1 pole of 2 independent axes: the observer whose data are being aggregated (clinician/expert vs. lay) and the method of aggregating those data (impressionistic vs. statistical). Fifty years of research suggests that when formulas are available, statistical aggregation outperforms informal, subjective aggregation much of the time. However, these data have little bearing on the question of whether, or under what conditions, clinicians can make reliable and valid observations and inferences at a level of generality relevant to practice or useful as data to be aggregated statistically. An emerging body of research suggests that clinical observations, just like lay observations, can be quantified using standard psychometric procedures, so that clinical description becomes statistical prediction.

Cognition↗
PubMed2005

Phylogenetics by likelihood: evolutionary modeling as a tool for understanding the genome.

Molecular evolutionary studies provide a means of investigating how cells function and how organisms adapt to their environment. The products of evolutionary studies provide medically important insights to the source of major diseases, such as HIV, and hold the key to understand the developing immunity of pathogenic bacteria to antibiotics. They have also helped mankind understand its place in nature, casting light on the selective forces and environmental conditions that resulted in modern humans. The use of likelihood as a framework for statistical modeling in phylogenetics has played a fundamental role in studying molecular evolution, enabling rigorous and robust conclusions to be drawn from sequence data. The first half of this article is a general introduction to the likelihood method for inferring phylogenies, the properties of the models used, and how it can be used for statistical testing. The latter half of the article focuses on the emerging new generation of phylogenetic models that describe heterogeneity in the evolutionary process along sequences, including the recoding of protein coding sequence data to amino acids and codons, and various approaches for describing dependencies between sites in a sequence. We conclude with a detailed case study examining how modern modeling approaches have been successfully employed to identify adaptive evolution in proteins.

Animals↗
PubMed2005

VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data.

Microarrays are invaluable high-throughput tools used to snapshot the gene expression profiles of cells and tissues. Among the most basic and fundamental questions asked of microarray data is whether individual genes are significantly activated or repressed by a particular stimulus. We have previously presented two Bayesian statistical methods for this level of analysis, collectively known as variance-modeled posterior inference with regional exponentials (VAMPIRE). These methods each require a sophisticated modeling step followed by integration of a posterior probability density. We present here a publicly available, web-based platform that allows users to easily load data, associate related samples and identify differentially expressed features using the VAMPIRE statistical framework. In addition, this suite of tools seamlessly integrates a novel gene annotation tool, known as GOby, which identifies statistically overrepresented gene groups. Unlike other tools in this genre, GOby can localize enrichment while respecting the hierarchical structure of annotation systems like Gene Ontology (GO). By identifying statistically significant enrichment of GO terms, Kyoto Encyclopedia of Genes and Genomes pathways, and TRANSFAC transcription factor binding sites, users can gain substantial insight into the physiological significance of sets of differentially expressed genes. The VAMPIRE microarray suite can be accessed at http://genome.ucsd.edu/microarray.

Bayes Theorem↗
PubMed2006

Bayesian theories of conditioning in a changing world.

The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.

Animals↗
PubMed1998

Regression models for multiple outcomes in large epidemiologic studies.

In situations in which one cannot specify a single primary outcome, epidemiologic analyses often examine multiple associations between outcomes and explanatory covariates or risk factors. To compare alternative approaches to the analysis of multiple outcomes in regression models, I used generalized estimating equations (GEE) models, a multivariate extension of generalized linear models, to incorporate the dependence among the outcomes from the same subject and to provide robust variance estimates of the regression coefficients. I applied the methods in a hospital-population-based study of complications of surgical anaesthesia, using GEE model fitting and quasi-likelihood score and Wald tests. In one GEE model specification, I allowed the associations between each of the outcomes and a covariate to differ, yielding a regression coefficient for each of the outcome and covariate combinations; I obtained the covariances among the set of outcome-specific regression coefficients for each covariate from the robust 'sandwich' variance estimator. To address the problem of multiple inference, I used simultaneous methods that make adjustments to the test statistic p-values and the confidence interval widths, to control type I error and simultaneous coverage, respectively. In a second model specification, for each of the covariates I assumed a common association between the outcomes and the covariate, which eliminates the problem of multiplicity by use of a global test of association. In an alternative approach to multiplicity, I used empirical Bayes methods to shrink the outcome-specific coefficients toward a pooled mean that is similar to the common effect coefficient. GEE regression models can provide a flexible framework for estimation and testing of multiple outcomes.

Anesthesia↗
PubMed1995

Computer simulation modelling and visualization of 3D architecture of biological tissues. Simulation of the evolution of normal, metaplastic and dysplastic states of the nasal epithelium.

Recent technical improvements, such as 3D microscopy imaging, have shown the necessity of studying 3D biological tissue architecture during carcinogenesis. In the present paper a computer simulation model is developed allowing the visualization of the microscopic biological tissue architecture during the development of metaplastic and dysplastic lesions. The static part of the model allows the simulation of the normal, metaplastic and dysplastic architecture of an external epithelium. This model is associated to a knowledge base which contains only data on the nasal epithelium. The latter has been well studied by numerous authors and its lesional states are well known. An inference engine allows the initialization of the static model parameters. A statistical comparison between simulated epithelia and real epithelia is achieved by adjusting the parameter values during the simulation. The dynamic part of the model allows the simulation of a growth process on a 3D representation based on the static model. The main hypothesis is that nasal epithelium is submitted to a continuous transformation from normal to cancer through metaplasia and dysplasia. The evolution of each cell (represented by its nucleus) depends on its local environment and also on its heritage from its mother-cell. Simulation of tissue renewal of the nasal pseudostratified epithelium has been achieved. The evolution from normal to hyperplasia has been simulated. After modification of the cell cycle modelling, the simulation of the development of metaplastic foci has been obtained.

Cell Cycle↗
PubMed2026

Shared genetic architecture and cellular convergence between female reproductive disorders and pulmonary function: a genome-wide cross-trait analysis.

Female reproductive disorders (FRDs), including polycystic ovary syndrome, endometriosis, uterine leiomyomata, and infertility, have been epidemiologically associated with impaired pulmonary function. However, it remains unclear whether this cross-organ link reflects shared genetic etiology and, if so, which cellular mechanisms mediate it. We performed a systematic genome-wide cross-trait analysis of three FRDs and lung function traits (FEV₁, FVC, FEV₁/FVC) using GWAS summary statistics from individuals of European ancestry, integrating genetic correlation, bidirectional causal inference, pleiotropy mapping, and single-cell enrichment analyses. We identified significant negative genetic correlations between FRDs and lung volume traits, most prominently for FVC (rg range: - 0.077 to - 0.178). Bidirectional causal analyses indicated that FRDs have a detrimental effect on lung volume, with higher FRD genetic liability associated with reduced lung volume. Cross-trait meta-analysis identified 17 pleiotropic variants across 11 loci, with the 19q13.2 (LTBP4) and 12q13.13 (HOXC6/HOXC9) loci showing strong evidence of shared causal variants. Critically, single-cell analyses revealed that shared genetic risk converged on mesenchymal lineages across organs, specifically alveolar adventitial fibroblasts in the lung and stromal/smooth muscle cells in the endometrium. Transcriptome-wide analyses further nominated the estrogen-responsive gene RERG as a convergent gene linking these conditions with lung function. Our study revealed a shared genetic architecture between female reproductive disorders and lung function traits, providing a basis for further mechanistic investigations and potential clinical evaluation. Furthermore, our findings suggest that shared fibroproliferative and hormone-responsive pathways may offer insights into the biological mechanisms underlying these conditions.

Female↗
PubMed1987

Effects of mailed preoperative instructions on learning and anxiety.

This study sought to contrast mailing a booklet to accomplish preoperative instruction with teaching performed by nurses after hospital admission. Sixty-six patients were studied in a 700-bed medical center and were assigned to three teaching groups and a control group which did not receive teaching. The researcher compared the four groups preoperatively after admission to determine the patients' knowledge and ability to perform surgical exercises, as well as their anxiety levels. Findings were that patients who received a booklet by mail preadmission learned surgical exercises equally well compared to patients taught postadmission, and mastered significantly more exercise behaviors than those receiving no instruction. No statistical difference in anxiety level was found among groups. One may infer that mailing preadmission material should be considered as an effective teaching mode and alternative to postadmission instruction.

Adolescent↗
PubMed2004

Bayesian inference of the metazoan phylogeny; a combined molecular and morphological approach.

Metazoan phylogeny remains one of evolutionary biology's major unsolved problems. Molecular and morphological data, as well as different analytical approaches, have produced highly conflicting results due to homoplasy resulting from more than 570 million years of evolution. To date, parsimony has been the only feasible combined approach but is highly sensitive to long-branch attraction. Recent development of stochastic models for discrete morphological characters and computationally efficient methods for Bayesian inference has enabled combined molecular and morphological data analysis with rigorous statistical approaches less prone to such inconsistencies. We present the first statistically founded analysis of a metazoan data set based on a combination of morphological and molecular data and compare the results with a traditional parsimony analysis. Interestingly, the Bayesian analyses demonstrate a high degree of congruence between morphological and molecular data, and both data sets contribute to the result of the combined analysis. Additionally, they resolve several irregularities obtained in previous studies and show high credibility values for controversial groups such as the ecdysozoans and lophotrochozoans. Parsimony, on the contrary, shows conflicting results, with morphology being congruent to the Bayesian results and the molecular data set producing peculiarities that are largely reflected in the combined analysis.

Animals↗
PubMed2005

Large-scale randomised trials--a misguided approach to clinical research.

Large-scale randomised trials influence the clinical management of millions of patients throughout the world and are believed to be the most reliable source of evidence on which to base therapeutic decisions. But do they really deserve the accolades bestowed upon them? The decision to perform these studies implies that the treatment difference is expected to be small and, thus, that large numbers of patients are required to achieve statistical significance. This small treatment difference is a direct consequence of limited knowledge of the subject matter which precludes the formation of homogeneous classes of patients with respect to the outcome. In fact, the majority of patients recruited are redundant in the sense that they would not develop the outcome regardless of treatment and, hence, could not participate in testing the efficacy of the drug in question. The conventional view is that randomisation satisfactorily addresses the issues resulting from heterogeneous study populations. However, the statistical approach to causation--which ignores the fundamental features of causal inference characteristic of both everyday discourse and the scientific method--that is used in large-scale randomised trials fails to deliver reliable generalisations even if the many potential obstacles to internal validity are set aside. Moreover, the results of large-scale randomised trials are not open to independent verification. Given the enormous profits to be made from the long-term treatment of common chronic diseases, the absence of any satisfactory method to detect research fraud is of some concern. Fewer than 5% of patients given treatment on the basis of large-scale randomised trials derive any benefit whatsoever. Arguments based on the benefits of such treatment to the wider community of patients are weakened by the dubious external validity of these studies but, in any case, cannot be used to promote the treatment of individual patients. Interestingly, when patients are provided with detailed information about the size of the benefits, most decline treatment. This is hardly surprising as it is debatable whether or not such meagre treatment effects could have any meaning to an individual patient. Large-scale randomised trials continue to be regarded as the gold standard of clinical research. This, of course, merely reflects the ability of powerful vested interests--in particular the pharmaceutical industry--to defy the sound arguments which demonstrate that the methodology of these studies is deeply flawed. Sooner or later, though, common sense must prevail.

Bias↗