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Full-Text Articles in Physical Sciences and Mathematics

Nested Hierarchical Functional Data Modeling And Inference For The Analysis Of Functional Plant Phenotypes, Yuhang Xu, Yehua Li, Dan Nettleton Jul 2019

Nested Hierarchical Functional Data Modeling And Inference For The Analysis Of Functional Plant Phenotypes, Yuhang Xu, Yehua Li, Dan Nettleton

Dan Nettleton

In a plant science Root Image Study, the process of seedling roots bending in response to gravity is recorded using digital cameras, and the bending rates are modeled as functional plant phenotype data. The functional phenotypes are collected from seeds representing a large variety of genotypes and have a three-level nested hierarchical structure, with seeds nested in groups nested in genotypes. The seeds are imaged on different days of the lunar cycle, and an important scientific question is whether there are lunar effects on root bending. We allow the mean function of the bending rate to depend on the lunar …


Comparative Gene Expression Profiles Between Heterotic And Non-Heterotic Hybrids Of Tetraploid Medicago Sativa, Xuehui Li, Yanling Wei, Dan Nettleton, E. Charles Brummer Jul 2019

Comparative Gene Expression Profiles Between Heterotic And Non-Heterotic Hybrids Of Tetraploid Medicago Sativa, Xuehui Li, Yanling Wei, Dan Nettleton, E. Charles Brummer

Dan Nettleton

Background: Heterosis, the superior performance of hybrids relative to parents, has clear agricultural value, but its genetic control is unknown. Our objective was to test the hypotheses that hybrids expressing heterosis for biomass yield would show more gene expression levels that were different from midparental values and outside the range of parental values than hybrids that do not exhibit heterosis.

Results: We tested these hypotheses in three Medicago sativa (alfalfa) genotypes and their three hybrids, two of which expressed heterosis for biomass yield and a third that did not, using Affymetrix M. truncatula GeneChip arrays. Alfalfa hybridized to approximately 47% …


Extreme‐Phenotype Genome‐Wide Association Study (Xp‐Gwas): A Method For Identifying Trait‐Associated Variants By Sequencing Pools Of Individuals Selected From A Diversity Panel, Jinliang Yang, Haiying Jiang, Cheng-Ting Yeh, Jianming Yu, Jeffrey A. Jeddeloh, Dan Nettleton, Patrick S. Schnable Jun 2019

Extreme‐Phenotype Genome‐Wide Association Study (Xp‐Gwas): A Method For Identifying Trait‐Associated Variants By Sequencing Pools Of Individuals Selected From A Diversity Panel, Jinliang Yang, Haiying Jiang, Cheng-Ting Yeh, Jianming Yu, Jeffrey A. Jeddeloh, Dan Nettleton, Patrick S. Schnable

Dan Nettleton

Although approaches for performing genome‐wide association studies (GWAS) are well developed, conventional GWAS requires high‐density genotyping of large numbers of individuals from a diversity panel. Here we report a method for performing GWAS that does not require genotyping of large numbers of individuals. Instead XP‐GWAS (extreme‐phenotype GWAS) relies on genotyping pools of individuals from a diversity panel that have extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling discovery of associations between genetic variants and traits of interest. This method was evaluated in maize (Zea mays) using the well‐characterized kernel row number trait, which was …


Estimation And Testing Of Gene Expression Heterosis, Tieming Ji, Peng Liu, Dan Nettleton Jun 2019

Estimation And Testing Of Gene Expression Heterosis, Tieming Ji, Peng Liu, Dan Nettleton

Dan Nettleton

Heterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid offspring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between parental inbred lines and their hybrid offspring to search for evidence of gene expression heterosis. Standard statistical approaches for separately analyzing expression data for each gene can produce biased and highly variable estimates and unreliable tests of heterosis. …


Parallel Genome-Wide Expression Profiling Of Host And Pathogen During Soybean Cyst Nematode Infection Of Soybean, Nagabhushana Ithal, Justin Recknor, Dan Nettleton, Leonard Hearne, Tom Maier, Thomas J. Baum, Melissa G. Mitchum Jun 2019

Parallel Genome-Wide Expression Profiling Of Host And Pathogen During Soybean Cyst Nematode Infection Of Soybean, Nagabhushana Ithal, Justin Recknor, Dan Nettleton, Leonard Hearne, Tom Maier, Thomas J. Baum, Melissa G. Mitchum

Dan Nettleton

Global analysis of gene expression changes in soybean (Glycine max) and Heterodera glycines (soybean cyst nematode [SCN]) during the course of infection in a compatible interaction was performed using the Affymetrix GeneChip soybean genome array. Among 35,611 soybean transcripts monitored, we identified 429 genes that showed statistically significant differential expression between uninfected and nematode-infected root tissues. These included genes encoding enzymes involved in primary metabolism; biosynthesis of phenolic compounds, lignin, and flavonoids; genes related to stress and defense responses; cell wall modification; cellular signaling; and transcriptional regulation. Among 7,431 SCN transcripts monitored, 1,850 genes showed statistically significant differential …


Sequence Mining And Transcript Profiling To Explore Cyst Nematode Parasitism, Axel A. Elling, Makedonka Mitreva, Xiaowu Gai, John Martin, Justin Recknor, Eric L. Davis, Richard S. Hussey, Dan Nettleton, James P. Mccarter, Thomas J. Baum Jun 2019

Sequence Mining And Transcript Profiling To Explore Cyst Nematode Parasitism, Axel A. Elling, Makedonka Mitreva, Xiaowu Gai, John Martin, Justin Recknor, Eric L. Davis, Richard S. Hussey, Dan Nettleton, James P. Mccarter, Thomas J. Baum

Dan Nettleton

Background: Cyst nematodes are devastating plant parasites that become sedentary within plant roots and induce the transformation of normal plant cells into elaborate feeding cells with the help of secreted effectors, the parasitism proteins. These proteins are the translation products of parasitism genes and are secreted molecular tools that allow cyst nematodes to infect plants.

Results: We present here the expression patterns of all previously described parasitism genes of the soybean cyst nematode, Heterodera glycines, in all major life stages except the adult male. These insights were gained by analyzing our gene expression dataset from experiments using the Affymetrix Soybean …


Developmental Transcript Profiling Of Cyst Nematode Feeding Cells In Soybean Roots, Nagabhushana Ithal, Justin Recknor, Dan Nettleton, Tom Maier, Thomas J. Baum, Melissa G. Mitchum Jun 2019

Developmental Transcript Profiling Of Cyst Nematode Feeding Cells In Soybean Roots, Nagabhushana Ithal, Justin Recknor, Dan Nettleton, Tom Maier, Thomas J. Baum, Melissa G. Mitchum

Dan Nettleton

Cyst nematodes of the genus Heterodera are obligate, sedentary endoparasites that have developed highly evolved relationships with specific host plant species. Successful parasitism involves significant physiological and morphological changes to plant root cells for the formation of specialized feeding cells called syncytia. To better understand the molecular mechanisms that lead to the development of nematode feeding cells, transcript profiling was conducted on developing syncytia induced by the soybean cyst nematode Heterodera glycines in soybean roots by coupling laser capture microdissection with high-density oligonucleotide microarray analysis. This approach has identified pathways that may play intrinsic roles in syncytium induction, formation, and …


Comparison Of Transcript Profiles In Wild-Type And O2 Maize Endosperm In Different Genetic Backgrounds, Hongwu Jia, Dan Nettleton, Joan M. Peterson, Gricelda Vasquez-Carrillo, Jean-Luc Jannink, M. Paul Scott Jun 2019

Comparison Of Transcript Profiles In Wild-Type And O2 Maize Endosperm In Different Genetic Backgrounds, Hongwu Jia, Dan Nettleton, Joan M. Peterson, Gricelda Vasquez-Carrillo, Jean-Luc Jannink, M. Paul Scott

Dan Nettleton

Mutations in the Opaque2 (O2) gene of maize (Zea mays L.) improve the nutritional value of maize by reducing the level of zeins in the kernel. The phenotype of o2 grain is controlled by many modifier genes and is therefore strongly dependent on genetic background. We propose two hypotheses to explain differences in phenotypic severity in different genetic backgrounds: (i) Specific genes are differentially (o2 vs. wild-type) expressed only in certain genotypes, and (ii) A set of genes are differentially expressed in all backgrounds, but the degree of differential expression differs in different backgrounds. To determine …


Arca Controls Metabolism, Chemotaxis, And Motility Contributing To The Pathogenicity Of Avian Pathogenic Escherichia Coli, Fengwei Jiang, Chunxia An, Yinli Bao, Xuefeng Zhao, Robert L. Jernigan, Andrew Lithio, Dan Nettleton, Ling Li, Eve S. Wurtele, Lisa K. Nolan, Chengping Lu, Ganwu Li Jun 2019

Arca Controls Metabolism, Chemotaxis, And Motility Contributing To The Pathogenicity Of Avian Pathogenic Escherichia Coli, Fengwei Jiang, Chunxia An, Yinli Bao, Xuefeng Zhao, Robert L. Jernigan, Andrew Lithio, Dan Nettleton, Ling Li, Eve S. Wurtele, Lisa K. Nolan, Chengping Lu, Ganwu Li

Dan Nettleton

Avian pathogenic Escherichia coli (APEC) strains cause one of the three most significant infectious diseases in the poultry industry and are also potential food-borne pathogens threating human health. In this study, we showed that ArcA (aerobic respiratory control), a global regulator important for E. coli's adaptation from anaerobic to aerobic conditions and control of that bacterium's enzymatic defenses against reactive oxygen species (ROS), is involved in the virulence of APEC. Deletion of arcA significantly attenuates the virulence of APEC in the duck model. Transcriptome sequencing (RNA-Seq) analyses comparing the APEC wild type and the arcA mutant indicate that ArcA regulates …


A Clade-Specific Arabidopsis Gene Connects Primary Metabolism And Senescence, Dallas C. Jones, Wenguang Zheng, Sheng Huang, Chuanlong Du, Xuefeng Zhao, Ragothaman M. Yennamalli, Taner Z. Sen, Dan Nettleton, Eve S. Wurtele, Ling Li Jun 2019

A Clade-Specific Arabidopsis Gene Connects Primary Metabolism And Senescence, Dallas C. Jones, Wenguang Zheng, Sheng Huang, Chuanlong Du, Xuefeng Zhao, Ragothaman M. Yennamalli, Taner Z. Sen, Dan Nettleton, Eve S. Wurtele, Ling Li

Dan Nettleton

Nearly immobile, plants have evolved new components to be able to respond to changing environments. One example is Qua Quine Starch (QQS, AT3G30720), an Arabidopsis thaliana-specific orphan gene that integrates primary metabolism with adaptation to environment changes. SAQR (Senescence-Associated and QQS-Related, AT1G64360), is unique to a clade within the family Brassicaceae; as such, the gene may have arisen about 20 million years ago. SAQR is up-regulated in QQS RNAi mutant and in the apx1 mutant under light-induced oxidative stress. SAQR plays a role in carbon allocation: overexpression lines of SAQR have significantly decreased starch content; …


Inversion Copulas From Nonlinear State Space Models With An Application To Inflation Forecasting, Michael S. Smith, Worapree Ole Maneesoonthorn May 2018

Inversion Copulas From Nonlinear State Space Models With An Application To Inflation Forecasting, Michael S. Smith, Worapree Ole Maneesoonthorn

Michael Stanley Smith

We propose the construction of copulas through the inversion of nonlinear state space models. These copulas allow for new time series models that have the same serial dependence structure as a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas …


Multivariate Spectral Analysis Of Crism Data To Characterize The Composition Of Mawrth Vallis, Melissa Luna Mar 2018

Multivariate Spectral Analysis Of Crism Data To Characterize The Composition Of Mawrth Vallis, Melissa Luna

Melissa Luna

No abstract provided.


Time Series Copulas For Heteroskedastic Data, Ruben Loaiza-Maya, Michael S. Smith, Worapree Maneesoonthorn Dec 2017

Time Series Copulas For Heteroskedastic Data, Ruben Loaiza-Maya, Michael S. Smith, Worapree Maneesoonthorn

Michael Stanley Smith

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first-order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co-movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture
their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value-at-risk forecasts.


Implicit Copulas From Bayesian Regularized Regression Smoothers, Nadja Klein, Michael S. Smith Dec 2017

Implicit Copulas From Bayesian Regularized Regression Smoothers, Nadja Klein, Michael S. Smith

Michael Stanley Smith

We show how to extract the implicit copula of a response vector from a Bayesian regularized regression smoother with Gaussian disturbances. The copula can be used to compare smoothers that employ different shrinkage priors and function bases. We illustrate with three popular choices of shrinkage priors --- a pairwise prior, the horseshoe prior and a g prior augmented with a point mass as employed for Bayesian variable selection --- and both univariate and multivariate function bases. The implicit copulas are high-dimensional and unavailable in closed form. However, we show how to evaluate them by first constructing a Gaussian copula conditional on the regularization parameters, …


Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith Nov 2017

Variational Bayes Estimation Of Discrete-Margined Copula Models With Application To Ime Series, Ruben Loaiza-Maya, Michael S. Smith

Michael Stanley Smith

We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension $rT$, where $T$ is the number of observations and $r$ is the number of series, and are difficult to estimate using previous methods. 
The vine pair-copulas are carefully selected to allow for heteroskedasticity, which is a feature of most ordinal …


Methods For Scalar-On-Function Regression, Philip T. Reiss, Jeff Goldsmith, Han Lin Shang, R. Todd Ogden Jul 2017

Methods For Scalar-On-Function Regression, Philip T. Reiss, Jeff Goldsmith, Han Lin Shang, R. Todd Ogden

Philip T. Reiss

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.


Propensity Score Analysis With Matching Weights, Liang Li Jun 2017

Propensity Score Analysis With Matching Weights, Liang Li

Liang Li

The propensity score analysis is one of the most widely used methods for studying the causal treatment effect in observational studies. This paper studies treatment effect estimation with the method of matching weights. This method resembles propensity score matching but offers a number of new features including efficient estimation, rigorous variance calculation, simple asymptotics, statistical tests of balance, clearly identified target population with optimal sampling property, and no need for choosing matching algorithm and caliper size. In addition, we propose the mirror histogram as a useful tool for graphically displaying balance. The method also shares some features of the inverse …


Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen Feb 2017

Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen

Laura B. Balzer

WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral …


Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua Dec 2016

Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua

Philip T. Reiss

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. The core idea is to regress the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, the proposed …


Auxiliary Likelihood-Based Approximate Bayesian Computation In State Space Models, Worapree Ole Maneesoonthorn Dec 2015

Auxiliary Likelihood-Based Approximate Bayesian Computation In State Space Models, Worapree Ole Maneesoonthorn

Worapree Ole Maneesoonthorn

A new approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics computed from observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a 'match' between observed and simulated summaries are retained, and used to estimate the inaccessible posterior; exact inference being feasible only if the statistics are suffi#14;cient. With no reduction to su#14;fficiency being possible in the state space setting, we pursue summaries via the maximization of
an auxiliary likelihood function. …


Shrinkage Estimation For Multivariate Hidden Markov Mixture Models, Mark Fiecas, Jürgen Franke, Rainer Von Sachs, Joseph Tadjuidje Dec 2015

Shrinkage Estimation For Multivariate Hidden Markov Mixture Models, Mark Fiecas, Jürgen Franke, Rainer Von Sachs, Joseph Tadjuidje

Mark Fiecas

Motivated from a changing market environment over time, we consider high-dimensional data such as financial returns, generated by a hidden Markov model which allows for switching between different regimes or states. To get more stable estimates of the covariance matrices of the different states, potentially driven by a number of observations which is small compared to the dimension, we apply shrinkage and combine it with an EM-type algorithm. This approach will yield better estimates a more stable estimates of the covariance matrix, which allows for improved reconstruction of the hidden Markov chain. In addition to a simulation study and the …


Modeling The Evolution Of Dynamic Brain Processes During An Associative Learning Experiment, Mark Fiecas, Hernando Ombao Dec 2015

Modeling The Evolution Of Dynamic Brain Processes During An Associative Learning Experiment, Mark Fiecas, Hernando Ombao

Mark Fiecas

Our goal is to use local field potentials (LFPs) to rigorously study changes in neuronal activity in the hippocampus and the nucleus accumbens over the course of an associative learning experiment. We show that the spectral properties of the LFPs changed during the experiment. While many statistical models take into account nonstationarity within a single trial of the experiment, the evolution of brain dynamics across trials is often ignored. In this paper, we developed a novel time series model that captures both sources of nonstationarity. Under the proposed model we rigorously define the spectral density matrix so that it evolves …


Flexible Penalized Regression For Functional Data...And Other Complex Data Objects, Philip T. Reiss Oct 2015

Flexible Penalized Regression For Functional Data...And Other Complex Data Objects, Philip T. Reiss

Philip T. Reiss

No abstract provided.


公的統計における欠測値補定の研究:多重代入法と単一代入法(高橋将宜), Masayoshi Takahashi Jun 2015

公的統計における欠測値補定の研究:多重代入法と単一代入法(高橋将宜), Masayoshi Takahashi

Masayoshi Takahashi

No abstract provided.


Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee Jun 2015

Set-Based Tests For Genetic Association In Longitudinal Studies, Zihuai He, Min Zhang, Seunggeun Lee, Jennifer A. Smith, Xiuqing Guo, Walter Palmas, Sharon L.R. Kardia, Ana V. Diez Roux, Bhramar Mukherjee

Jennifer McMahon

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint eff#11;ect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set …


Matching Methods For Biomarker Evaluation: A Mapping With Causal Inference, Debashis Ghosh, Michael Sabel Dec 2014

Matching Methods For Biomarker Evaluation: A Mapping With Causal Inference, Debashis Ghosh, Michael Sabel

Debashis Ghosh

In many medical settings, there is interest in evaluating the predictive ability of a candidate biomarker while adjusting appropriately for confounding factors. Recently, Janes and Pepe (2008, {\it Biometrics} 64: 1 -- 9) evaluated the effects of matching on classification accuracy for biomarkers. In this article, we note an analogy between the use of matching in causal inference with its role in the biomarker evaluation problem. This leads us to be able to import much of the literature on matching from causal inferential settings to the biomarker evaluation problem. This leads to a theoretical characterization of the bias properties of …


A Review Of Frequentist Tests For The 2x2 Binomial Trial, Chris Lloyd Dec 2014

A Review Of Frequentist Tests For The 2x2 Binomial Trial, Chris Lloyd

Chris J. Lloyd

The 2x2 binomial trial is the simplest of data structures yet its statistical analysis and the issues it raises have been debated and revisited for over 70 years. Which analysis should biomedical researchers use in applications? In this review, we consider frequentist tests only, specifically tests with control size either exactly or very close to exactly. These procedures can be classified as conditional and unconditional. Amongst tests motivated by a conditional model, Lancaster’s mid-p and Liebermeister’s test are less conservative than Fisher’s classical test, but do not control type 1 error. Within the conditional framework, only Fisher’s test can be …


Cross-Validation And Hypothesis Testing In Neuroimaging: An Irenic Comment On The Exchange Between Friston And Lindquist Et Al., Philip T. Reiss Dec 2014

Cross-Validation And Hypothesis Testing In Neuroimaging: An Irenic Comment On The Exchange Between Friston And Lindquist Et Al., Philip T. Reiss

Philip T. Reiss

The “ten ironic rules for statistical reviewers” presented by Friston (2012) prompted a rebuttal by Lindquist et al. (2013), which was followed by a rejoinder by Friston (2013). A key issue left unresolved in this discussion is the use of cross-validation to test the significance of predictive analyses. This note discusses the role that cross-validation-based and related hypothesis tests have come to play in modern data analyses, in neuroimaging and other fields. It is shown that such tests need not be suboptimal and can fill otherwise-unmet inferential needs.


欠測値補定の診断手法としての多重代入法(高橋将宜), Masayoshi Takahashi Sep 2014

欠測値補定の診断手法としての多重代入法(高橋将宜), Masayoshi Takahashi

Masayoshi Takahashi

No abstract provided.


Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr. Aug 2014

Depicting Estimates Using The Intercept In Meta-Regression Models: The Moving Constant Technique, Blair T. Johnson Dr., Tania B. Huedo-Medina Dr.

Blair T. Johnson

In any scientific discipline, the ability to portray research patterns graphically often aids greatly in interpreting a phenomenon. In part to depict phenomena, the statistics and capabilities of meta-analytic models have grown increasingly sophisticated. Accordingly, this article details how to move the constant in weighted meta-analysis regression models (viz. “meta-regression”) to illuminate the patterns in such models across a range of complexities. Although it is commonly ignored in practice, the constant (or intercept) in such models can be indispensible when it is not relegated to its usual static role. The moving constant technique makes possible estimates and confidence intervals at …