共查询到20条相似文献，搜索用时 15 毫秒
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Meta‐analysis is an important tool for synthesizing research on a variety of topics in ecology and evolution, including molecular ecology, but can be susceptible to nonindependence. Nonindependence can affect two major interrelated components of a meta‐analysis: (i) the calculation of effect size statistics and (ii) the estimation of overall meta‐analytic estimates and their uncertainty. While some solutions to nonindependence exist at the statistical analysis stages, there is little advice on what to do when complex analyses are not possible, or when studies with nonindependent experimental designs exist in the data. Here we argue that exploring the effects of procedural decisions in a meta‐analysis (e.g. inclusion of different quality data, choice of effect size) and statistical assumptions (e.g. assuming no phylogenetic covariance) using sensitivity analyses are extremely important in assessing the impact of nonindependence. Sensitivity analyses can provide greater confidence in results and highlight important limitations of empirical work (e.g. impact of study design on overall effects). Despite their importance, sensitivity analyses are seldom applied to problems of nonindependence. To encourage better practice for dealing with nonindependence in meta‐analytic studies, we present accessible examples demonstrating the impact that ignoring nonindependence can have on meta‐analytic estimates. We also provide pragmatic solutions for dealing with nonindependent study designs, and for analysing dependent effect sizes. Additionally, we offer reporting guidelines that will facilitate disclosure of the sources of nonindependence in meta‐analyses, leading to greater transparency and more robust conclusions. 相似文献
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Meta-analysis is a statistical methodology for combining information from diverse sources so that a more reliable and efficient conclusion can be reached. It can be conducted by either synthesizing study-level summary statistics or drawing inference from an overarching model for individual participant data (IPD) if available. The latter is often viewed as the “gold standard.” For random-effects models, however, it remains not fully understood whether the use of IPD indeed gains efficiency over summary statistics. In this paper, we examine the relative efficiency of the two methods under a general likelihood inference setting. We show theoretically and numerically that summary-statistics-based analysis is at most as efficient as IPD analysis, provided that the random effects follow the Gaussian distribution, and maximum likelihood estimation is used to obtain summary statistics. More specifically, (i) the two methods are equivalent in an asymptotic sense; and (ii) summary-statistics-based inference can incur an appreciable loss of efficiency if the sample sizes are not sufficiently large. Our results are established under the assumption that the between-study heterogeneity parameter remains constant regardless of the sample sizes, which is different from a previous study. Our findings are confirmed by the analyses of simulated data sets and a real-world study of alcohol interventions. 相似文献
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Junsheng Ma Francesco C. Stingo Brian P. Hobbs 《Biometrical journal. Biometrische Zeitschrift》2019,61(4):902-917
The evolution of “informatics” technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high‐dimensionality of omics‐type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods. 相似文献
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Richard P. Duncan Richard T. Corlett Amy K. Hahs Michael A. McCarthy Mark J. McDonnell Mark W. Schwartz Ken Thompson Peter A. Vesk Nicholas S. G. Williams 《Global Ecology and Biogeography》2011,20(4):509-519
Aim Urban environments around the world share many features in common, including the local extinction of native plant species. We tested the hypothesis that similarity in environmental conditions among urban areas should select for plant species with a particular suite of traits suited to those conditions, and lead to the selective extinction of species lacking those traits. Location Eleven cities with data on the plant species that persisted and those that went locally extinct within at least the last 100 years following urbanization. Methods We compiled data on 11 plant traits for 8269 native species in the 11 cities and used hierarchical logistic regression models to identify the degree to which traits could distinguish species that persisted from those that went locally extinct in each city. The trait effects from each city were then combined in a meta‐analysis. Results The cities fell into two groups: those with relatively low rates of extinction (less than 0.05% species per year – Adelaide, Hong Kong, Los Angeles, San Diego and San Francisco), for which no traits reliably predicted the pattern of extinction, and those with higher rates of extinction (> 0.08% species per year – Auckland, Chicago, Melbourne, New York, Singapore and Worcester, MA), where short‐statured, small‐seeded plants were more likely to go extinct. Main conclusions Our analysis reveals patterns in trait selectivity consistent with local studies, suggesting some consistency in trait selection by urbanization. Overall, however, few traits reliably predicted the pattern of plant extinction across cities, making it difficult to identify a priori the extinction‐prone species most likely to be affected by urban expansion. 相似文献
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Stephen Senn Susanne Schmitz Anna Schritz Samir Salah 《Biometrical journal. Biometrische Zeitschrift》2019,61(2):379-390
If the number of treatments in a network meta‐analysis is large, it may be possible and useful to model the main effect of treatment as random, that is to say as random realizations from a normal distribution of possible treatment effects. This then constitutes a third sort of random effect that may be considered in connection with such analyses. The first and most common models treatment‐by‐trial interaction as being random and the second, rather rarer, models the main effects of trial as being random and thus permits the recovery of intertrial information. Taking the example of a network meta‐analysis of 44 similar treatments in 10 trials, we illustrate how a hierarchical approach to modeling a random main effect of treatment can be used to produce shrunk (toward the overall mean) estimates of effects for individual treatments. As a related problem, we also consider the issue of using a random‐effect model for the within‐trial variances from trial to trial. We provide a number of possible graphical representations of the results and discuss the advantages and disadvantages of such an approach. 相似文献
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Tomoharu Eguchi 《Marine Mammal Science》2014,30(3):1122-1139
Simple Bayesian statistical models are introduced to estimate the proportion of identifiable individuals and group sizes in photographic identification, or photo‐ID, studies of animals that are found in groups. The models require a simple random photographic sampling of animals, where the photographic captures are treated as sampling with replacement within each group. The total number of images, including those that cannot be identified, and the number of images that contain identifiable individuals are used to make inference about the proportion of identifiable individuals within each group and as the population when a number of groups are sampled. The numbers of images for individuals within each group are used to make inference about the group size. Based on analyses of simulated and real data, the models perform well with respect to accuracy and precision of posterior distributions of the parameters. Widths of posterior intervals were affected by the number of groups sampled, sampling duration, and the proportion of identifiable individuals in each group that was sampled. The structure of the models can accommodate covariates, which may affect photographic efficiency, defined in this study as the probability of photographically capturing individuals. 相似文献
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Katherina A. Pietsch Kiona Ogle Johannes H. C. Cornelissen William K. Cornwell Gerhard Bönisch Joseph M. Craine Benjamin G. Jackson Jens Kattge Duane A. Peltzer Josep Penuelas Peter B. Reich David A. Wardle James T. Weedon Ian J. Wright Amy E. Zanne Christian Wirth 《Global Ecology and Biogeography》2014,23(9):1046-1057
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Bayesian analysis of molecular variance in pyrosequences quantifies population genetic structure across the genome of Lycaeides butterflies 总被引：1，自引：0，他引：1
ZACHARIAH GOMPERT MATTHEW L. FORISTER JAMES A. FORDYCE CHRIS C. NICE ROBERT J. WILLIAMSON C. ALEX BUERKLE 《Molecular ecology》2010,19(12):2455-2473
The distribution of genetic variation within and among populations is commonly used to infer their demographic and evolutionary histories. This endeavour has the potential to benefit substantially from high‐throughput next‐generation sequencing technologies through a rapid increase in the amount of data available and a corresponding increase in the precision of parameter estimation. Here we report the results of a phylogeographic study of the North American butterfly genus Lycaeides using 454 sequence data. This study serves the dual purpose of demonstrating novel molecular and analytical methods for population genetic analyses with 454 sequence data and expanding our knowledge of the phylogeographic history of Lycaeides. We obtained 341 045 sequence reads from 12 populations that we were able to assemble into 15 262 contigs (most of which were variable), representing one of the largest population genetic data sets for a non‐model organism to date. We examined patterns of genetic variation using a hierarchical Bayesian analysis of molecular variance model, which provides precise estimates of genome‐level φST while appropriately modelling uncertainty in locus‐specific φST. We found that approximately 36% of sequence variation was partitioned among populations, suggesting historical or current isolation among the sampled populations. Estimates of pairwise genome‐level φST were largely consistent with a previous phylogeographic model for Lycaeides, suggesting fragmentation into two to three refugia during Pleistocene glacial cycles followed by post‐Pleistocene range expansion and secondary contact leading to introgressive hybridization. This study demonstrates the potential of using genome‐level data to better understand the phylogeographic history of populations. 相似文献
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Summary It is a common practice to analyze complex longitudinal data using semiparametric nonlinear mixed-effects (SNLME) models with a normal distribution. Normality assumption of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models, but some covariates may often be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. Inferential procedures can be complicated dramatically when data with skewness, missing values, and measurement error are observed. In the literature, there has been considerable interest in accommodating either skewness, incompleteness or covariate measurement error in such models, but there has been relatively little study concerning all three features simultaneously. In this article, our objective is to address the simultaneous impact of skewness, missingness, and covariate measurement error by jointly modeling the response and covariate processes based on a flexible Bayesian SNLME model. The method is illustrated using a real AIDS data set to compare potential models with various scenarios and different distribution specifications. 相似文献
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Peter E. Levy Annette Burden Mark D. A. Cooper Kerry J. Dinsmore Julia Drewer Chris Evans David Fowler Jenny Gaiawyn Alan Gray Stephanie K. Jones Timothy Jones Niall P. McNamara Robert Mills Nick Ostle Lucy J. Sheppard Ute Skiba Alwyn Sowerby Susan E. Ward Piotr Zieliński 《Global Change Biology》2012,18(5):1657-1669
Nearly 5000 chamber measurements of CH4 flux were collated from 21 sites across the United Kingdom, covering a range of soil and vegetation types, to derive a parsimonious model that explains as much of the variability as possible, with the least input requirements. Mean fluxes ranged from ?0.3 to 27.4 nmol CH4 m?2 s?1, with small emissions or low rates of net uptake in mineral soils (site means of ?0.3 to 0.7 nmol m?2 s?1) and much larger emissions from organic soils (site means of ?0.3 to 27.4 nmol m?2 s?1). Less than half of the observed variability in instantaneous fluxes could be explained by independent variables measured. The reasons for this include measurement error, stochastic processes and, probably most importantly, poor correspondence between the independent variables measured and the actual variables influencing the processes underlying methane production, transport and oxidation. When temporal variation was accounted for, and the fluxes averaged at larger spatial scales, simple models explained up to ca. 75% of the variance in CH4 fluxes. Soil carbon, peat depth, soil moisture and pH together provided the best sub‐set of explanatory variables. However, where plant species composition data were available, this provided the highest explanatory power. Linear and nonlinear models generally fitted the data equally well, with the exception that soil moisture required a power transformation. To estimate the impact of changes in peatland water table on CH4 emissions in the United Kingdom, an emission factor of +0.4 g CH4 m?2 yr?1 per cm increase in water table height was derived from the data. 相似文献
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Lindsey N. Rich Courtney L. Davis Zach J. Farris David A. W. Miller Jody M. Tucker Sandra Hamel Mohammad S. Farhadinia Robin Steenweg Mario S. Di Bitetti Kanchan Thapa Mamadou D. Kane S. Sunarto Nathaniel P. Robinson Agustín Paviolo Paula Cruz Quinton Martins Navid Gholikhani Ateih Taktehrani Jesse Whittington Febri A. Widodo Nigel G. Yoccoz Claudia Wultsch Bart J. Harmsen Marcella J. Kelly 《Global Ecology and Biogeography》2017,26(8):918-929
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When novel scientific questions arise after longitudinal binary data have been collected, the subsequent selection of subjects from the cohort for whom further detailed assessment will be undertaken is often necessary to efficiently collect new information. Key examples of additional data collection include retrospective questionnaire data, novel data linkage, or evaluation of stored biological specimens. In such cases, all data required for the new analyses are available except for the new target predictor or exposure. We propose a class of longitudinal outcome-dependent sampling schemes and detail a design corrected conditional maximum likelihood analysis for highly efficient estimation of time-varying and time-invariant covariate coefficients when resource limitations prohibit exposure ascertainment on all participants. Additionally, we detail an important study planning phase that exploits available cohort data to proactively examine the feasibility of any proposed substudy as well as to inform decisions regarding the most desirable study design. The proposed designs and associated analyses are discussed in the context of a study that seeks to examine the modifying effect of an interleukin-10 cytokine single nucleotide polymorphism on asthma symptom regression in adolescents participating Childhood Asthma Management Program Continuation Study. Using this example we assume that all data necessary to conduct the study are available except subject-specific genotype data. We also assume that these data would be ascertained by analyzing stored blood samples, the cost of which limits the sample size. 相似文献
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R.W. Payne 《The Annals of applied biology》2014,164(1):11-17
The collaborations between statisticians and biologists during the 100 years since AAB was founded have led to a very impressive list of statistical techniques, whose use now goes well beyond agriculture and biology. One example is the maximum likelihood methodology for probit analysis, arising from the collaboration between Sir Ronald Fisher and Chester Bliss. Others include analysis of variance, design of experiments, generalized linear models and the residual, or restricted, maximum likelihood (REML) algorithm for fitting unbalanced linear mixed models. 相似文献
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Mário Ferreira Ana Filipa Filipe David C. Bardos Maria Filomena Magalhães Pedro Beja 《Ecology and evolution》2016,6(15):5530-5541
Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy‐detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time‐to‐detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time‐to‐first detection conditional on occupancy in relation to local factors, using modified interval‐censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time‐to‐detection model provided unbiased parameter estimates despite interval‐censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P‐values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval‐censored time‐to‐detection model provides a practical solution to model occupancy‐detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists. 相似文献
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Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous‐time process potentially as a function of time‐varying covariates. We develop a continuous‐time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation‐maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates. 相似文献
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Summary The aim of this article is to develop a spatial model for multi‐subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi‐subject data, some work on spatial modeling of single‐subject data, and some recent work on spatial modeling of multi‐subject data. However, there has been no work on spatial models that explicitly account for inter‐subject variability in activation locations. In this article, we use the idea of activation centers and model the inter‐subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass‐univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data. 相似文献
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Christian Damgaard 《Journal of Plant Ecology》2022,15(2):253
特定环境和土地利用因素对酸性草原植被影响的时空建模酸性草原受到了农业集约化作业(伴随着养分添加)、牲畜密度增加以及土地撂荒等多种因素的威胁。为了认识和量化所选环境和土地利用因子对酸性草地植被观测变化的影响,本研究采用结构方程模型拟合了大尺度时空精度植被覆盖监测数据。通过分层模型结构将测量和采样不确定性的重要来源纳入其中。此外,本研究也将测量和采样的不确定性与过程的不确定性分离,这在生成可能反馈给当地保护管理决策的生态预测时有着重要的意义。研究结果表明,一般而言,大气氮沉降的增加会导致非禾本草本植物的盖度,取而代之的会是更多的以禾草植物为主的酸性草原生境。沙质土壤的酸性相对较强,而土壤类型既会对植被构成直接的影响,也会通过影响土壤pH值的方式对植被产生间接影响。土壤的类型和土壤的pH值都会对酸性草原上的植被造成影响。对于植被覆盖情况在时间上的变化,尽管该模型仅解释了其中相对较小的一部分,但在使用该模型对局部生态状况进行预测并制定具有自适应性的管理计划时,对不确定性的量化仍然是有价值的。 相似文献