http://precedings.nature.com/tags/statistics Recently posted documents tagged with 'statistics' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3080/version/1 The multi-stage hypothesis suggests that cancers develop through a single defined series of genetic alterations. This hypothesis was first suggested over 50 years ago based upon age-specific incidence data. However, recent molecular studies of tumors indicate that multiple routes exist to the formation of cancer, not a single route. This parallel route hypothesis has not been tested with age-specific incidence data.To test the parallel route hypothesis, I formulated it in terms of a mathematical equation, confirmed this equation with computer simulations, then tested whether this equation was consistent with age-specific incidence data compiled by the Surveillance Epidemiology and End Results (SEER) cancer registries since 1973. I used the chi-squared goodness of fit test to measure consistency.I found that the age-specific incidence data from most human carcinomas, including those of the colon, lung, prostate, and breast were consistent with the parallel route hypothesis. However, this hypothesis is only consistent if an immune sub-population exists, one that will never develop carcinoma. Furthermore, breast carcinoma has two distinct forms of the disease, and one of these occurs at significantly different rates in different racial groups. http://precedings.nature.com/documents/3080/version/1 Fri, 17 Apr 2009 21:06:50 UTC Parallel routes of human carcinoma development: Implications of the age-specific incidence data hdl:10101/npre.2009.3080.1 2009-04-17 James Brody Nature Precedings 2009-04-17T21:06:50Z Manuscript Cancer Genetics & Genomics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2642.1 This one-hour lecture by Dr. Eric Turkheimer of the University of Virginia’s Department of Psychology explored the following:The contemporary era has seen a convergence of genomic technology and traditional social scientific concerns with complex human individual differences. Rather than finally turning social science into a replicable hard-scientific enterprise, genomics has gotten bogged down in the long-standing frustrations of social science. A recent report of an extensive genome wide association study of human height demonstrates the profound difficulties of explaining uncontrolled human variation at a genomic level. The statistical technologies that have been brought to bear on the problem of genomic association are simply modifications of similar methods that have been used by social scientists for decades, with little success. The motivation for the statistical methods in genomics is the same as it is in traditional social science: An attempt to discern linear causation in complex systems when experimental control is not possible.For an audio recording of Dr. Turkheimer’s lecture, please visit http://cirge.stanford.edu/activities/events.html. http://dx.doi.org/10.1038/npre.2008.2642.1 Mon, 15 Dec 2008 17:02:39 UTC The Gloomy Prospect Wins: Statistical Significance and Population Stratification in Genome Wide Association Studies doi:10.1038/npre.2008.2642.1 2008-12-15 Eric Turkheimer Nature Precedings 2008-12-15T17:02:39Z Presentation Genetics & Genomics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2002/version/3 Most of the statistical tests currently used to detect differentially expressed genes are based on asymptotic results, and perform poorly for low expression tags. Another problem is the common use of a single canonical cutoff for the significance level (p-value) of all the tags, without taking into consideration the type II error and the highly variable character of the sample size of the tags.This work reports the development of two significance tests for the comparison of digital expression profiles, based on frequentist and Bayesian points of view, respectively. Both tests are exact, and do not use any asymptotic considerations, thus producing more correct results for low frequency tags than the chi-square test. The frequentist test uses a tag-customized critical level which minimizes a linear combination of type I and type II errors. A comparison of the Bayesian and the frequentist tests revealed that they are linked by a Beta distribution function. These tests can be used alone or in conjunction, and represent an improvement over the currently available methods for comparing digital profiles. http://precedings.nature.com/documents/2002/version/3 Fri, 29 Aug 2008 21:08:06 UTC Significance tests for comparing digital gene expression profiles hdl:10101/npre.2008.2002.3 2009-09-03 Leonardo Varuzza Nature Precedings 2008-08-29T21:08:06Z Manuscript Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2007.980.2 Recording of fetal heart rate via CTG monitoring has been routinely performed as an important part of antenatal and subpartum care for several decades. The current guidelines of the FIGO (ref1) recommend a normal range of the fetal heart rate from 110 to 150 bpm. However, there is no agreement in the medical community whether this is the correct range (ref2). We aim to address this question by computerized analysis (ref 3) of a high quality database (HQDb, ref 4) of about one billion electronically registered fetal heart rate measurements from about 10,000 pregnancies in three medical centres over seven years. In the present paper, we lay out a detailed analysis plan for this evidence-based project in the vein of the validation policy of the Sylvia Lawry Centre for Multiple Sclerosis Research (ref 5) with a split of the database into an exploratory part and a part reserved for validation. We will perform the analysis and the validation after publication of this plan in order to reduce the probability of publishing false positive research findings (ref 6-7). http://dx.doi.org/10.1038/npre.2007.980.2 Mon, 12 Nov 2007 16:48:34 UTC The Normal Fetal Heart Rate Study: Analysis Plan doi:10.1038/npre.2007.980.2 2007-11-12 Martin Daumer Nature Precedings 2007-11-12T16:48:34Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.980.1 Recording of fetal heart rate via CTG monitoring has been routinely performed as an important part of antenatal and subpartum care for several decades. The current guidelines of the FIGO (ref1) recommend a normal range of the fetal heart rate from 110 to 150 bpm. However, there is no agreement in the medical community whether this is the correct range (ref2). We aim to address this question by computerized analysis (ref 3) of a high quality database (HQDb, ref 4) of about one billion electronically registered fetal heart rate measurements from about 10,000 pregnancies in three medical centres over seven years. In the present paper, we lay out a detailed analysis plan for this evidence-based project in the vein of the validation policy of the Sylvia Lawry Centre for Multiple Sclerosis Research (ref 5) with a split of the database into an exploratory part and a part reserved for validation. We will perform the analysis and the validation after publication of this plan in order to reduce the probability of publishing false positive research findings (ref 6-7). http://dx.doi.org/10.1038/npre.2007.980.1 Mon, 17 Sep 2007 12:48:48 UTC The Normal Fetal Heart Rate Study: Analysis Plan doi:10.1038/npre.2007.980.1 2007-09-17 Martin Daumer Nature Precedings 2007-09-17T12:48:48Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://precedings.nature.com/documents/400/version/1 Correlated samples have been frequently avoided in case-controlgenetic association studies in part because the methods for handling them are either noteasily implemented or not widely known. Weadvocate one method for case-control association analysis of correlatedsamples—the effective sample size method—as a simple andaccessible approach that does not require specialized computer programs.The effective sample size method captures the variance inflationof allele frequency estimation exactly, and can be used to modify thechi-square test statistic, p-value, and 95% confidence interval ofodds-ratio simply by replacing the apparent number of allele counts with theeffective ones. For genotype frequency estimation, although a singleeffective sample size is unable to completely characterize the variance inflation,an averaged one can satisfactorily approximate the simulated result.The effective sample size method is applied to the rheumatoid arthritissiblings data collected from the North American Rheumatoid Arthritis Consortium (NARAC)to establish a significant association with the interferon-inducedhelicasel gene (IFIH1) previously being identified as a type 1 diabetessusceptibility locus. Connections between the effective sample sizemethod and other methods, such as generalized estimation equation,variance of eigenvalues for correlation matrices, and genomic controls,are also discussed. http://precedings.nature.com/documents/400/version/1 Tue, 10 Jul 2007 05:01:19 UTC Effective Sample Size: Quick Estimation of the Effect of Related Samples in Genetic Case-Control Association Analyses hdl:10101/npre.2007.400.1 2007-07-10 Wentian F. Li Nature Precedings 2007-07-10T05:01:19Z Manuscript Genetics & Genomics Immunology http://creativecommons.org/licenses/by/2.5/ http://precedings.nature.com/documents/306/version/2 Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity. http://precedings.nature.com/documents/306/version/2 Tue, 03 Jul 2007 12:18:21 UTC The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies hdl:10101/npre.2007.306.2 2007-07-03 Leming D. Shi Nature Precedings 2007-07-03T12:18:21Z Manuscript Biotechnology Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.306.1 Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity. http://dx.doi.org/10.1038/npre.2007.306.1 Mon, 02 Jul 2007 09:20:53 UTC The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies doi:10.1038/npre.2007.306.1 2007-07-02 Leming Shi Nature Precedings 2007-07-02T09:20:53Z Manuscript Biotechnology Bioinformatics http://creativecommons.org/licenses/by/2.5/