Statistical Methods (statistical + methods)

Distribution by Scientific Domains
Distribution within Medical Sciences

Kinds of Statistical Methods

  • appropriate statistical methods
  • multivariate statistical methods
  • new statistical methods
  • standard statistical methods

  • Terms modified by Statistical Methods

  • statistical methods used

  • Selected Abstracts


    STATISTICAL METHODS IN (MOLECULAR) EVOLUTION1

    EVOLUTION, Issue 2 2006
    Peter Beerli
    No abstract is available for this article. [source]


    ASSOCIATIONS BETWEEN AIR POLLUTION AND HOSPITAL VISITS FOR CARDIOVASCULAR DISEASES IN THE ELDERLY IN SYDNEY USING BAYESIAN STATISTICAL METHODS

    AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 3 2009
    Hiep Duc
    Summary Using generalized linear models (GLMs), Jalaludin,et al. (2006;,J. Exposure Analysis and Epidemiology,16, 225,237) studied the association between the daily number of visits to emergency departments for cardiovascular disease by the elderly (65+) and five measures of ambient air pollution. Bayesian methods provide an alternative approach to classical time series modelling and are starting to be more widely used. This paper considers Bayesian methods using the dataset used by Jalaludin,et al.,(2006), and compares the results from Bayesian methods with those obtained by Jalaludin,et al.,(2006) using GLM methods. [source]


    Advanced Statistics:Statistical Methods for Analyzing Cluster and Cluster-randomized Data

    ACADEMIC EMERGENCY MEDICINE, Issue 4 2002
    Robert L. Wears MD
    Abstract. Sometimes interventions in randomized clinical trials are not allocated to individual patients, but rather to patients in groups. This is called cluster allocation, or cluster randomization, and is particularly common in health services research. Similarly, in some types of observational studies, patients (or observations) are found in naturally occurring groups, such as neighborhoods. In either situation, observations within a cluster tend to be more alike than observations selected entirely at random. This violates the assumption of independence that is at the heart of common methods of statistical estimation and hypothesis testing. Failure to account for the dependence between individual observations and the cluster to which they belong can have profound implications on the design and analysis of such studies. Their p-values will be too small, confidence intervals too narrow, and sample size estimates too small, sometimes to a dramatic degree. This problem is similar to that caused by the more familiar "unit of analysis error" seen when observations are repeated on the same subjects, but are treated as independent. The purpose of this paper is to provide an introduction to the problem of clustered data in clinical research. It provides guidance and examples of methods for analyzing clustered data and calculating sample sizes when planning studies. The article concludes with some general comments on statistical software for cluster data and principles for planning, analyzing, and presenting such studies. [source]


    Statistical Methods for Human Rights edited by Jana Asher, David Banks, Fritz J. Scheuren

    INTERNATIONAL STATISTICAL REVIEW, Issue 2 2008
    Susan Starkings
    No abstract is available for this article. [source]


    A Review of Statistical Methods for Genome Mapping

    INTERNATIONAL STATISTICAL REVIEW, Issue 1 2000
    Hywel B. Jones
    Summary Framework maps of the human genome are an important staging post in the on-going effort to sequence the entire genome. The existence of high quality maps is also a prerequistite for studies attempting to determine the location of genes involved in common diseases. The basic experimental approaches to constructing both genetic and physical maps are briefly described as well as their respective uses. A variety of statistical approaches to map construction are outlined including parsimony, maximum likelihood and Bayesian methodologies. The mostly widely used of these, the method of maximum likelihood, is discussed in detail, particularly in the context of physical mapping using radiation hybrids. Finally, current statistical issues and problems in the field of genome mapping are described. Résumé Des cartes squelette du génome humain sont une étape, important dans l'effort actuel pour séquecer, le génome tout entier. L'existence de cartes de bonne qualité est aussi la condition d'études visant à localiser les génes, interwenant dans des maladies courantes. Les approches expérimentales de base pour construire tant des cartes génétigues, que physiques sont briévement, décrites ainsi que leurs usages respectifs. Plusieurs méthodes, statistiques de cartographie sont mises en relief: notamment celles de parcimonie, du maximum de vraisemblance et bayésiennce.La plus largement utilisée-laméthode, du maximum de vraisemblance-est examinéen détail, particuliérement pour la cartographue physique utilisant des phybrides d'irradiation Enfin sont abordés, divers questions et probémes, courants on matiéde cartographie génétique. [source]


    A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

    JOURNAL OF RISK AND INSURANCE, Issue 3 2006
    Patrick L. Brockett
    This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons. [source]


    Robust Statistical Methods with R by J. Jure,ková and J. Picek

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2007
    Andreas Karlsson
    No abstract is available for this article. [source]


    Statistical Methods in Molecular Evolution

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 2 2006
    Chris Cannings
    No abstract is available for this article. [source]


    Single Particle Detection and Size Analysis with Statistical Methods from Particle Imaging Data

    PARTICLE & PARTICLE SYSTEMS CHARACTERIZATION, Issue 2 2006
    Benno Wessely
    Abstract The application of imaging techniques for particle characterization in the micrometer range is often advantageous under process conditions. Particle imaging analysis of disperse systems not only allows the evaluation of the particle size, but also the concentration of particles and particle shape. Under process conditions online imaging techniques have to deal with high particle concentrations and sometimes with high velocities. Two general principles for the evaluation of particle images are discussed in this paper: particle analysis by means of object identification and a statistical method to analyze overlapping clusters of particles. [source]


    Statistical Methods in Diagnostic Medicine Zhou X-H, Obuchowski N, McClish D (2002) ISBN 0471347728, £66.95, ,95.20, $99.95 Wiley; http://www.wileyeurope.com/WileyCDA/WileyTitle/productCd-0471347728.html, The Statistical Evaluation of Medical Tests for Classification and Prediction Pepe MS (2003) ISBN 0198509847, £65.00, $115.00 Oxford University Press; http://www.oup.co.uk/isbn/0-19-850984-7

    PHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 4 2003
    Paul Silcocks
    No abstract is available for this article. [source]


    Statistical Methods for the Analysis of Repeated Measurements Davis CS (2002) ISBN 0387953701; 415 pages; £59.50, ,84.95, $79.95 Springer; http://www.springer.de/cgi/svcat/search_book.pl?isbn=0387953701

    PHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 3 2003
    Fiona Holland
    No abstract is available for this article. [source]


    Statistical Methods for the Analysis of Genetic Association Studies

    ANNALS OF HUMAN GENETICS, Issue 2 2006
    G. Y. Zou
    Summary This paper applies a retrospective logistic regression model (Prentice, 1976) using a sandwich variance estimator (White, 1982; Zeger et al. 1985) to genetic association studies in which alleles are treated as dependent variables. The validity of switching the positions of allele and trait variables in the regression model is ensured by the invariance property of the odds ratio. The approach is shown to be able to accommodate many commonly seen designs, matched or unmatched alike, having either binary or quantitative traits. The resultant score statistic has potentially higher power than those that have previously appeared in the genetics literature. As a regression model in general, this approach may also be applied to incorporate covariates. Numerical examples implemented with standard software are presented. [source]


    Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model

    BIOMETRICS, Issue 2 2010
    Jing Qin
    Summary Length-biased time-to-event data are commonly encountered in applications ranging from epidemiological cohort studies or cancer prevention trials to studies of labor economy. A longstanding statistical problem is how to assess the association of risk factors with survival in the target population given the observed length-biased data. In this article, we demonstrate how to estimate these effects under the semiparametric Cox proportional hazards model. The structure of the Cox model is changed under length-biased sampling in general. Although the existing partial likelihood approach for left-truncated data can be used to estimate covariate effects, it may not be efficient for analyzing length-biased data. We propose two estimating equation approaches for estimating the covariate coefficients under the Cox model. We use the modern stochastic process and martingale theory to develop the asymptotic properties of the estimators. We evaluate the empirical performance and efficiency of the two methods through extensive simulation studies. We use data from a dementia study to illustrate the proposed methodology, and demonstrate the computational algorithms for point estimates, which can be directly linked to the existing functions in S-PLUS or R. [source]


    Translational Medicine: Strategies and Statistical Methods edited by COSMATOS, D. and CHOW, S.-C.

    BIOMETRICS, Issue 2 2010
    Yongzhao Shao
    No abstract is available for this article. [source]


    Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health by PENG, R. D. and DOMINICI, F.

    BIOMETRICS, Issue 3 2009
    David Buckeridge
    No abstract is available for this article. [source]


    Statistical Methods for Analysis of Radiation Effects with Tumor and Dose Location-Specific Information with Application to the WECARE Study of Asynchronous Contralateral Breast Cancer

    BIOMETRICS, Issue 2 2009
    Bryan Langholz
    Summary Methods for the analysis of individually matched case-control studies with location-specific radiation dose and tumor location information are described. These include likelihood methods for analyses that just use cases with precise location of tumor information and methods that also include cases with imprecise tumor location information. The theory establishes that each of these likelihood based methods estimates the same radiation rate ratio parameters, within the context of the appropriate model for location and subject level covariate effects. The underlying assumptions are characterized and the potential strengths and limitations of each method are described. The methods are illustrated and compared using the WECARE study of radiation and asynchronous contralateral breast cancer. [source]


    SAS for Data Analysis: Intermediate Statistical Methods by MARASINGHE, M. G. and KENNEDY, W. J.

    BIOMETRICS, Issue 2 2009
    Article first published online: 28 MAY 200
    No abstract is available for this article. [source]


    A Study on Variances in Multivariate Analyses of Oral Implant Outcome

    CLINICAL IMPLANT DENTISTRY AND RELATED RESEARCH, Issue 1 2007
    Irene Herrmann LDS, Odont Lic
    ABSTRACT Background:, Elaborate studies have shown that interdependency exists between implants being placed in the same patient/jaw. Therefore, interdependency ought to be an important aspect to address, whenever performing statistical analyses of oral implant outcomes. A Jackknife method could be an option when conducting statistical evaluations of oral implant failure prognoses. Purpose:, The aim of this study was to evaluate whether a statistical difference can be detected by using the Jackknife method in conjunction with life table analyses and/or a log rank test of four different combinations of jaw density and quantity. Materials and Methods:, Four multicenter studies were pooled and adjusted in order to create a research database consisting of 486 patients and 1,737 implants in preparation for the Jackknife resampling method. Combinations of jaw shapes and bone qualities were constructed to select at-risk patients. Statistical Methods:, Life tables with confidence intervals were calculated and a log rank test was used to determine whether a statistical difference between the combinations could be established. Results:, Both statistical analyses, after the Jackknife resampling method, showed that patients with poor bone quality and resorbed jaws (combination IV) had a statistically higher risk of implant failure. Conclusion:, By rearranging data using the Jackknife method, standardized statistical tests seem to work well even when the study population tested was affected by interdependency. [source]


    Associations of risk factors obesity and occupational airborne exposures with CDKN2A/p16 aberrant DNA methylation in esophageal cancer patients

    DISEASES OF THE ESOPHAGUS, Issue 7 2010
    S. Mohammad Ganji
    SUMMARY It is known that obesity and occupational airborne exposure such as dust are among risk factors of esophageal cancer development, in particular squamous cell carcinoma (SCC) of esophagus. Here, we tested whether these factors could also affect aberrant DNA methylation. DNAs from 44 fresh tumor tissues and 19 non-tumor adjacent normal tissues, obtained from 44 patients affected by SCC of esophagus (SCCE), were studied for methylation at the CDKN2A/p16 gene promoter by methylation-specific polymerase chain reaction assay. Statistical methods were used to assess association of promoter methylation with biopathological, clinical, and personal information data, including obesity and airborne exposures. Methylation at the CDKN2A/p16 gene promoter was detected in 12 out of 44 tumor samples. None of the non-tumor tissues exhibited the aberrant methylation. Our results confirmed previously described significant association with low tumor stage (P= 0.002); in addition, we found that obesity (P= 0.001) and occupational exposure (P= 0.008) were both significantly associated with CDKN2A/p16 promoter methylation. This study provides evidence that obesity and occupational exposure increase the risk of developing esophageal cancer through an enhancement of CDKN2A/p16 promoter methylation. [source]


    An evaluation of European air pollution regulations for particulate matter monitored from a heterogeneous network

    ENVIRONMETRICS, Issue 8 2009
    Sujit K. Sahu
    Abstract Statistical methods are needed for evaluating many aspects of air pollution regulations increasingly adopted by many different governments in the European Union. The atmospheric particulate matter (PM) is an important air pollutant for which regulations have been issued recently. A challenging task here is to evaluate the regulations based on data monitored on a heterogeneous network where PM has been observed at a number of sites and a surrogate has been observed at some other sites. This paper develops a hierarchical Bayesian joint space,time model for the PM measurements and its surrogate between which the exact relationship is unknown, and applies the methods to analyse spatio -temporal data obtained from a number of sites in Northern Italy. The model is implemented using MCMC techniques and methods are developed to meet the regulatory demands. These enablefull inference with regard to process unknowns, calibration, validation, predictions in time and space and evaluation of regulatory standards. Copyright © 2008 John Wiley & Sons, Ltd. [source]


    Statistical methods for the evaluation of health effects of prenatal mercury exposure

    ENVIRONMETRICS, Issue 2 2003
    Esben Budtz-Jørgensen
    Abstract Environmental risk assessment based on epidemiological data puts stringent demands on the statistical procedures. First, convincing evidence has to be established that there is a risk at all. In practice this endeavor requires prudent use of the observational epidemiological information with delicate balancing between utilizing the information optimally but not over-interpreting it. If a case for an environmental risk has been made, the second challenge is to provide useful input that regulatory authorities can use to set standards. This article surveys some of these issues in the concrete case of neurobehavioral effects in Faroese children prenatally exposed to methylmercury. A selection of modern, appropriate methods has been applied in the analysis of this material that may be considered typical of environmental epidemiology today. In particular we emphasize the potential of structural equation models for improving standard multiple regression analysis of complex environmental epidemiology data. Copyright © 2003 John Wiley & Sons, Ltd. [source]


    Modeling maternal-offspring gene-gene interactions: the extended-MFG test

    GENETIC EPIDEMIOLOGY, Issue 5 2010
    Erica J. Childs
    Abstract Maternal-fetal genotype (MFG) incompatibility is an interaction between the genes of a mother and offspring at a particular locus that adversely affects the developing fetus, thereby increasing susceptibility to disease. Statistical methods for examining MFG incompatibility as a disease risk factor have been developed for nuclear families. Because families collected as part of a study can be large and complex, containing multiple generations and marriage loops, we create the Extended-MFG (EMFG) Test, a model-based likelihood approach, to allow for arbitrary family structures. We modify the MFG test by replacing the nuclear-family based "mating type" approach with Ott's representation of a pedigree likelihood and calculating MFG incompatibility along with the Mendelian transmission probability. In order to allow for extension to arbitrary family structures, we make a slightly more stringent assumption of random mating with respect to the locus of interest. Simulations show that the EMFG test has appropriate type-I error rate, power, and precise parameter estimation when random mating holds. Our simulations and real data example illustrate that the chief advantages of the EMFG test over the earlier nuclear family version of the MFG test are improved accuracy of parameter estimation and power gains in the presence of missing genotypes. Genet. Epidemiol. 34: 512,521, 2010.© 2010 Wiley-Liss, Inc. [source]


    Evaluation of predictors of mortality in Frontotemporal Dementia,methodological aspects

    INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 7 2003
    A. Gräsbeck
    Abstract Objectives To retrospectively evaluate pre-diagnostic clinical features (predictors) of mortality in frontotemporal dementia (FTD). The main aim was to investigate if there were indications against interpreting missing data as signs of absence. Material and methods 96 cases with FTD, here defined as Dementia in Pick's disease according to ICD-10. The predictors were behavioural/psychiatric features, language impairment and neurological deficits up to the date of diagnosis. Each predictor was rated as present (Yes), absent (No) or not recorded (Missing), and evaluated according to its distribution and mortality pattern: if a feature was not recorded because it was absent, the mortality of the Missing and the No-category should hypothetically be close. Statistical methods included Kaplan-Meier survival curves and Cox regression analyses. Results Neurological deficits and language impairments were frequently recorded as present or absent, while non-recordings were more prevalent among the behavioural/psychiatric features. Some features were excluded as predictors because they showed too little variation. Analyses of the survival pattern indicated that in some features, the observations of the Missing-category could be interpreted as absence of the symptoms. In other features these observations had to be regarded as truly missing. Conclusions In the retrospective evaluation of predictors of mortality a method for treating missing data was applied. The interpretation of non-recordings as signs of absence was supported by the analyses of the survival patterns in some of the studied features. However, the study underscores the importance of systematic estimations of pre-diagnostic clinical features in dementia. Copyright © 2003 John Wiley & Sons, Ltd. [source]


    Statistical methods in animal breeding and Bach's harpsichord concertos

    JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2010
    R. J. C. Cantet
    No abstract is available for this article. [source]


    Regression modelling of correlated data in ecology: subject-specific and population averaged response patterns

    JOURNAL OF APPLIED ECOLOGY, Issue 5 2009
    John Fieberg
    Summary 1.,Statistical methods that assume independence among observations result in optimistic estimates of uncertainty when applied to correlated data, which are ubiquitous in applied ecological research. Mixed effects models offer a potential solution and rely on the assumption that latent or unobserved characteristics of individuals (i.e. random effects) induce correlation among repeated measurements. However, careful consideration must be given to the interpretation of parameters when using a nonlinear link function (e.g. logit). Mixed model regression parameters reflect the change in the expected response within an individual associated with a change in that individual's covariates [i.e. a subject-specific (SS) interpretation], which may not address a relevant scientific question. In particular, a SS interpretation is not natural for covariates that do not vary within individuals (e.g. gender). 2.,An alternative approach combines the solution to an unbiased estimating equation with robust measures of uncertainty to make inferences regarding predictor,outcome relationships. Regression parameters describe changes in the average response among groups of individuals differing in their covariates [i.e. a population-averaged (PA) interpretation]. 3.,We compare these two approaches [mixed models and generalized estimating equations (GEE)] with illustrative examples from a 3-year study of mallard (Anas platyrhynchos) nest structures. We observe that PA and SS responses differ when modelling binary data, with PA parameters behaving like attenuated versions of SS parameters. Differences between SS and PA parameters increase with the size of among-subject heterogeneity captured by the random effects variance component. Lastly, we illustrate how PA inferences can be derived (post hoc) from fitted generalized and nonlinear-mixed models. 4.,Synthesis and applications. Mixed effects models and GEE offer two viable approaches to modelling correlated data. The preferred method should depend primarily on the research question (i.e. desired parameter interpretation), although operating characteristics of the associated estimation procedures should also be considered. Many applied questions in ecology, wildlife management and conservation biology (including the current illustrative examples) focus on population performance measures (e.g. mean survival or nest success rates) as a function of general landscape features, for which the PA model interpretation, not the more commonly used SS model interpretation may be more natural. [source]


    Evaluation of the relationship between smoking during pregnancy and subgingival microbiota

    JOURNAL OF CLINICAL PERIODONTOLOGY, Issue 1 2005
    Nurcan Buduneli
    Abstract Background: Numerous studies have shown that smoking negatively affects periodontal health. Hormonal changes, which occur during pregnancy have also been reported to have adverse effects on the periodontal tissues or indirectly through alterations in the subgingival bacterial flora. At present, no knowledge exists concerning possible effects of smoking on the composition of subgingival plaque in pregnancy. The purpose of the present study was to evaluate the effects of smoking during pregnancy on the subgingival plaque bacteria most commonly associated with periodontal disease. Methods: A total number of 181 women were examined within 72 h post-partum. Smoking status was recorded by means of a self-reported questionnaire and the study population was divided into three groups; non-smokers, light smokers, and heavy smokers. In each woman, two subgingival plaque samples were obtained from mesio- or disto-buccal aspect of randomly selected one molar and one incisor tooth by sterile paperpoints. Clinical periodontal recordings comprising presence of dental plaque, bleeding on probing (BOP), and probing pocket depth (PPD) were performed at six sites per each tooth at all teeth. Plaque samples were analysed by checkerboard DNA,DNA hybridization with respect to 12 bacterial species. In all analyses, the individual subject was the computational unit. Thus, mean values for all clinical parameters were calculated and bacterial scores from each individual sample were averaged. Statistical methods included ,2 test, Kruskal,Wallis test and Mann,Whitney U -test. Results: Mean ages were similar in the study groups. Plaque, BOP and PPD recordings were lower in the heavy-smoker group, but the differences were not statistically significant (p>0.05). The detection rates and bacterial loads of the specific subgingival bacteria exhibited no significant differences between the groups. No correlation could be found between smoking status and detection rates and bacterial loads of various bacterial species. Conclusion: The present findings suggest that smoking during pregnancy does not have a significant effect on the composition of subgingival plaque bacteria. [source]


    Capitation funding in the public sector

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 2 2001
    Peter C. Smith
    A fundamental requirement of government at all levels,national and local,is to distribute the limited funds that it wishes to spend on particular public services between geographical areas or institutions, which are effectively competitors for such funds. Increasing use is now being made of capitation methods for such purposes, in which a standard estimate of expected expenditure is attached to a citizen with given characteristics. Statistical methods are playing an important role in determining such capitations, but they give rise to profound methodological problems. This paper examines the rationale for capitation and discusses the associated methodological issues. It illustrates the issues raised with two examples taken from the UK public sector: in personal social services and hospital care. Severe limitations of the data mean that small area data are used as the unit of observation, giving rise to considerable complexity in the model to be estimated. As a result, a range of methodologies including two-stage least squares and multilevel modelling methods are deployed. The paper concludes with a suggestion for an approach which would represent an improvement on current capitation methods, but which would require data on individuals rather than on small areas. [source]


    Statistical methods for regular monitoring data

    JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 5 2005
    Michael L. Stein
    Summary., Meteorological and environmental data that are collected at regular time intervals on a fixed monitoring network can be usefully studied combining ideas from multiple time series and spatial statistics, particularly when there are little or no missing data. This work investigates methods for modelling such data and ways of approximating the associated likelihood functions. Models for processes on the sphere crossed with time are emphasized, especially models that are not fully symmetric in space,time. Two approaches to obtaining such models are described. The first is to consider a rotated version of fully symmetric models for which we have explicit expressions for the covariance function. The second is based on a representation of space,time covariance functions that is spectral in just the time domain and is shown to lead to natural partially nonparametric asymmetric models on the sphere crossed with time. Various models are applied to a data set of daily winds at 11 sites in Ireland over 18 years. Spectral and space,time domain diagnostic procedures are used to assess the quality of the fits. The spectral-in-time modelling approach is shown to yield a good fit to many properties of the data and can be applied in a routine fashion relative to finding elaborate parametric models that describe the space,time dependences of the data about as well. [source]


    Statistical methods in spatial genetics

    MOLECULAR ECOLOGY, Issue 23 2009
    GILLES GUILLOT
    Abstract The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls. [source]


    Why Does head form change in children of immigrants?

    AMERICAN JOURNAL OF HUMAN BIOLOGY, Issue 5 2010
    A reappraisal
    Objectives: We test two specific hypotheses that explain the cranial changes Boas observed in Hebrews and Sicilians, namely that Hebrew change results from abandoning cradling of infants in America, while in Sicilians it results from impaired growth in America. Methods: Boas's (1928) data were used to test these hypotheses. The role of cradling in cranial shape was examined by comparing cranial indices of U.S.-born and foreign children between 1.5 and 5 years of age. Age changes in cranial index of Hebrew and Sicilian children ages five to eighteen were examined to demonstrate differing patterns of age changes, which could be explained by environmental differences. Statistical methods employed were t -tests, least squares, and loess regression. Results: The difference between American and foreign-born Hebrew children arose prior to five years of age, after which it remained constant. American and foreign-born Sicilians, on the other hand, had similar cranial indices at age five, and diverged during the growing years, primarily because American-born children did not exhibit the reduction in cranial index with age seen in the other groups. Conclusions: The results support the two hypotheses tested. Change in Hebrew cranial indices resulted from abandoning the practice of cradling infants in America. U.S.-born Sicilian children experienced an environment worse than the one in Europe, and consequently experienced impaired growth. We conclude that the changes Boas observed resulted from specific behavioral and economic conditions unique to each group, rather than a homogeneous American environment. Am. J. Hum. Biol. 22:702-707, 2010. © 2010Wiley-Liss, Inc. [source]