83 results match your criteria American Statistician[Journal]


Statistical implications of endogeneity induced by residential segregation in small-area modelling of health inequities.

Am Stat 2022 4;76(2):142-151. Epub 2022 Jan 4.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Read More

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January 2022

A Review of Bayesian Perspectives on Sample Size Derivation for Confirmatory Trials.

Am Stat 2021 22;75(4):424-432. Epub 2021 Apr 22.

MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Sample size derivation is a crucial element of planning any confirmatory trial. The required sample size is typically derived based on constraints on the maximal acceptable Type I error rate and minimal desired power. Power depends on the unknown true effect and tends to be calculated either for the smallest relevant effect or a likely point alternative. Read More

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Sampling Strategies for Fast Updating of Gaussian Markov Random Fields.

Am Stat 2021 31;75(1):52-65. Epub 2019 May 31.

School of Mathematical and Statistical Sciences, Clemson University.

Gaussian Markov random fields (GMRFs) are popular for modeling dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by the sparse precision matrices needed for random variable generation. Typically in Bayesian computation, GMRFs are updated jointly in a block Gibbs sampler or componentwise in a single-site sampler via the full conditional distributions. The former approach can speed convergence by updating correlated variables all at once, while the latter avoids solving large matrices. Read More

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On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.

Am Stat 2020 20;74(3):243-248. Epub 2019 May 20.

Department of Epidemiology and Department of Biostatistics, Harvard T. Chan School of Public Health, Boston, MA.

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko [2016] that recommends the "D-value," which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. Read More

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Bayesian Causality.

Am Stat 2020 26;74(3):249-257. Epub 2019 Aug 26.

Department of Statistics, University of California, Irvine.

Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Read More

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Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models.

Am Stat 2019 4;73(4):350-359. Epub 2018 Jun 4.

Department of Health Outcomes and Policy, University of Florida.

Despite the popularity of the general linear mixed model for data analysis, power and sample size methods and software are not generally available for commonly used test statistics and reference distributions. Statisticians resort to simulations with homegrown and uncertified programs or rough approximations which are misaligned with the data analysis. For a wide range of designs with longitudinal and clustering features, we provide accurate power and sample size approximations for inference about fixed effects in linear models we call reversible. Read More

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Revisiting Nested Group Testing Procedures: New Results, Comparisons, and Robustness.

Am Stat 2019 4;73(2):117-125. Epub 2018 Jun 4.

Biostatistics Branch, Division of Cancer Epidemiology and Genetics National Cancer Institute, Rockville, MD.

Group testing has its origin in the identification of syphilis in the U.S. army during World War II. Read More

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The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else is Known.

Am Stat 2019 20;73(Suppl 1):271-280. Epub 2019 Mar 20.

La Trobe University, Plenty Road, Bundoora, Melbourne VIC, 3086, Australia.

The "New Statistics" emphasizes effect sizes, confidence intervals, meta-analysis, and the use of Open Science practices. We present 3 specific ways in which a New Statistics approach can help improve scientific practice: by reducing over-confidence in small samples, by reducing confirmation bias, and by fostering more cautious judgments of consistency. We illustrate these points through consideration of the literature on oxytocin and human trust, a research area that typifies some of the endemic problems that arise with poor statistical practice. Read More

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Before < 0.05 to Beyond < 0.05: Using History to Contextualize -Values and Significance Testing.

Am Stat 2019 20;73(Suppl 1):82-90. Epub 2019 Mar 20.

Department of Biostatistics, Harvard University.

As statisticians and scientists consider a world beyond < 0.05, it is important to not lose sight of how we got to this point. Although significance testing and -values are often presented as prescriptive procedures, they came about through a process of refinement and extension to other disciplines. Read More

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Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment.

Am Stat 2019 20;73(Suppl 1):152-156. Epub 2019 Mar 20.

Department of Health Care Policy, Harvard Medical School, Boston, MA 01201 and Research Associate at NBER, Cambridge, MA, 02138.

Stepwise regression building procedures are commonly used applied statistical tools, despite their well-known drawbacks. While many of their limitations have been widely discussed in the literature, other aspects of the use of individual statistical fit measures, especially in high-dimensional stepwise regression settings, have not. Giving primacy to individual fit, as is done with p-values and R, when group fit may be the larger concern, can lead to misguided decision making. Read More

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Evidence from marginally significant statistics.

Authors:
Valen E Johnson

Am Stat 2019 20;73(Suppl 1):129-134. Epub 2019 Mar 20.

Texas A&M University.

This article examines the evidence contained in statistics that are marginally significant in 5% tests. The bases for evaluating evidence are likelihood ratios and integrated likelihood ratios, computed under a variety of assumptions regarding the alternative hypotheses in null hypothesis significance tests. Likelihood ratios and integrated likelihood ratios provide a useful measure of the evidence in favor of competing hypotheses because they can be interpreted as representing the ratio of the probabilities that each hypothesis assigns to observed data. Read More

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The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making.

Am Stat 2019 20;73(1):56-68. Epub 2019 Mar 20.

Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.

This article resulted from our participation in the session on the "role of expert opinion and judgment in statistical inference" at the October 2017 ASA Symposium on Statistical Inference. We present a strong, unified statement on roles of expert judgment in statistics with processes for obtaining input, whether from a Bayesian or frequentist perspective. Topics include the role of subjectivity in the cycle of scientific inference and decisions, followed by a clinical trial and a greenhouse gas emissions case study that illustrate the role of judgments and the importance of basing them on objective information and a comprehensive uncertainty assessment. Read More

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Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action.

Am Stat 2019 10;73(1):22-31. Epub 2018 May 10.

Area of Information Systems and Quantitative Sciences, Texas Tech University.

Many Bayes factors have been proposed for comparing population means in two-sample (independent samples) studies. Recently, Wang and Liu (2015) presented an "objective" Bayes factor (BF) as an alternative to a "subjective" one presented by Gönen et al. (2005). Read More

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Modified Wilcoxon-Mann-Whitney Test and Power against Strong Null.

Am Stat 2019 10;73(1):43-49. Epub 2018 May 10.

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center.

The Wilcoxon-Mann-Whitney (WMW) test is a popular rank-based two-sample testing procedure for the strong null hypothesis that the two samples come from the same distribution. A modified WMW test, the Fligner-Policello (FP) test, has been proposed for comparing the medians of two populations. A fact that may be underappreciated among some practitioners is that the FP test can also be used to test the strong null like the WMW. Read More

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Letter to the Editor.

Am Stat 2019 5;73(3):312. Epub 2019 Aug 5.

Department of Population Health, New York University.

Hutson and Vexler (2018) demonstrate an example of aliasing with the beta and normal distribution. This letter presents another illustration of aliasing using the beta and normal distributions via an infinite mixture model, inspired by the problem of modeling placebo response. Read More

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Testing for positive quadrant dependence.

Am Stat 2019 30;2019. Epub 2019 May 30.

Department of Statistics, University of South Carolina, Columbia, SC 29208, USA.

We develop an empirical likelihood approach to test independence of two univariate random variables and versus the alternative that and are strictly positive quadrant dependent (PQD). Establishing this type of ordering between and is of interest in many applications, including finance, insurance, engineering, and other areas. Adopting the framework in Einmahl and McKeague (2003, ), we create a distribution-free test statistic that integrates a localized empirical likelihood ratio test statistic with respect to the empirical joint distribution of and . Read More

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Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-The-Losers Design.

Am Stat 2019 24;2019. Epub 2019 Jun 24.

Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS.

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II - Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. Read More

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Joint clustering with correlated variables.

Am Stat 2019 9;73(3):296-306. Epub 2018 Jul 9.

University of Southampton Faculty of Medicine, Southampton, UK.

Traditional clustering methods focus on grouping subjects or (dependent) variables assuming independence between the variables. Clusters formed through these approaches can potentially lack homogeneity. This article proposes a joint clustering method by which both variables and subjects are clustered. Read More

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Facilitating the Calculation of the Efficient Score Using Symbolic Computing.

Am Stat 2018 30;72(2):199-205. Epub 2017 Oct 30.

Biostatistics and Bioinformatics, Duke University School of Medicine Duke Cancer Institute, Duke University Medical Center.

The score statistic continues to be a fundamental tool for statistical inference. In the analysis of data from high-throughput genomic assays, inference on the basis of the score usually enjoys greater stability, considerably higher computational efficiency, and lends itself more readily to the use of resampling methods than the asymptotically equivalent Wald or likelihood ratio tests. The score function often depends on a set of unknown nuisance parameters which have to be replaced by estimators, but can be improved by calculating the efficient score, which accounts for the variability induced by estimating these parameters. Read More

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October 2017

How to share data for collaboration.

Am Stat 2018 24;72(1):53-57. Epub 2018 Apr 24.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health and Center for Computational Biology, John Hopkins University, Baltimore, MD.

Within the statistics community, a number of guiding principles for sharing data have emerged; however, these principles are not always made clear to collaborators generating the data. To bridge this divide, we have established a set of guidelines for sharing data. In these, we highlight the need to provide raw data to the statistician, the importance of consistent formatting, and the necessity of including all essential experimental information and pre-processing steps carried out to the statistician. Read More

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A Cautionary Note on Beta Families of Distributions and the Aliases Within.

Am Stat 2018 26;72(2):121-129. Epub 2018 Jan 26.

Alan D. Hutson is Professor of Oncology, Roswell Park Cancer Institute, Department of Biostatistics & Bioinformatics, Elm & Carlton Streets, Buffalo, NY 14623. Albert Vexler is Associate Professor, University at Buffalo, Department of Biostatistics, 706 Kimball Tower, 3435 Main St., Buffalo, NY 14214-3000.

In this note we examine the four parameter beta family of distributions in the context of the beta-normal and beta-logistic distributions. In the process we highlight the concept of numerical and limiting alias distributions, which in turn relate to numerical instabilities in the numerical maximum likelihood fitting routines for these families of distributions. We conjecture that the numerical issues pertaining to fitting these multiparameter distributions may be more widespread than has originally been reported across several families of distributions. Read More

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January 2018

A Guide to Teaching Data Science.

Am Stat 2018 14;72(4):382-391. Epub 2018 Nov 14.

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA.

Demand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the Statistics curriculum. The unifying recommendation is that computing should play a more prominent role. Read More

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November 2018

Efficient Computation of Reduced Regression Models.

Am Stat 2017;71(2):171-176. Epub 2017 Feb 28.

Department of Urology, Weill Cornell Medical College, New York, NY.

We consider settings where it is of interest to fit and assess regression submodels that arise as various explanatory variables are excluded from a larger regression model. The larger model is referred to as the full model; the submodels are the reduced models. We show that a computationally efficient approximation to the regression estimates under any reduced model can be obtained from a simple weighted least squares (WLS) approach based on the estimated regression parameters and covariance matrix from the full model. Read More

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February 2017

Case study in evaluating time series prediction models using the relative mean absolute error.

Am Stat 2016 10;70(3):285-292. Epub 2016 Aug 10.

Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA.

Statistical prediction models inform decision-making processes in many real-world settings. Prior to using predictions in practice, one must rigorously test and validate candidate models to ensure that the proposed predictions have sufficient accuracy to be used in practice. In this paper, we present a framework for evaluating time series predictions that emphasizes computational simplicity and an intuitive interpretation using the relative mean absolute error metric. Read More

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Strategies for Success: Early-Stage Collaborating Biostatistics Faculty in an Academic Health Center.

Am Stat 2017 18;71(3):220-230. Epub 2017 Oct 18.

Biostatistics at the Departments of Population Health Science Policy and Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. She is the director of the Institute for Healthcare Delivery Science, Mount Sinai Health System and also directs the Biostatistics Shared Resource Facility of Tisch Cancer Institute.

Collaborative biostatistics faculties (CBF) are increasingly valued by academic health centers (AHCs) for their role in increasingsuccess rates of grants and publications, and educating medical students and clinical researchers. Some AHCs have a biostatistics department that consists of only biostatisticians focused on methodological research, collaborative research, and education. Others may have a biostatistics unit within an interdisciplinary department, or statisticians recruited into clinical departments. Read More

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October 2017

A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations.

Am Stat 2017 11;71(4):344-353. Epub 2018 Jan 11.

Department of Statistics, College of Arts and Sciences, University of Kentucky.

Correlated data are commonly analyzed using models constructed using population-averaged generalized estimating equations (GEEs). The specification of a population-averaged GEE model includes selection of a structure describing the correlation of repeated measures. Accurate specification of this structure can improve efficiency, whereas the finite-sample estimation of nuisance correlation parameters can inflate the variances of regression parameter estimates. Read More

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January 2018

Weighing Evidence "Steampunk" Style via the Meta-Analyser.

Am Stat 2016 Oct 21;70(4):385-394. Epub 2016 Nov 21.

MRC Biostatistics Unit, University of Cambridge , Cambridge , UK.

The funnel plot is a graphical visualization of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modeling assumptions. Although perhaps not widely appreciated, a simple extension of the funnel plot can help to facilitate an intuitive interpretation of the mathematics underlying a meta-analysis at a more fundamental level, by equating it to determining the center of mass of a physical system. We used this analogy to explain the concepts of weighing evidence and of biased evidence to a young audience at the Cambridge Science Festival, without recourse to precise definitions or statistical formulas and with a little help from Sherlock Holmes! Following on from the science fair, we have developed an interactive web-application (named the Meta-Analyser) to bring these ideas to a wider audience. Read More

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October 2016

Best Practices for Biostatistical Consultation and Collaboration in Academic Health Centers.

Am Stat 2016 9;70(2):187-194. Epub 2016 Jun 9.

Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA.

Given the increasing level and scope of biostatistics expertise needed at academic health centers today, we developed best practices guidelines for biostatistics units to be more effective in providing biostatistical support to their institutions, and in fostering an environment in which unit members can thrive professionally. Our recommendations focus on the key areas of: 1) funding sources and mechanisms; 2) providing and prioritizing access to biostatistical resources; and 3) interacting with investigators. We recommend that the leadership of biostatistics units negotiate for sufficient long-term infrastructure support to ensure stability and continuity of funding for personnel, align project budgets closely with actual level of biostatistical effort, devise and consistently apply strategies for prioritizing and tracking effort on studies, and clearly stipulate with investigators prior to project initiation policies regarding funding, lead time, and authorship. Read More

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An Example of an Improvable Rao-Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator.

Am Stat 2016 Jan 31;70(1):108-113. Epub 2016 Mar 31.

The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Read More

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January 2016

The Central Role of Bayes' Theorem for Joint Estimation of Causal Effects and Propensity Scores.

Am Stat 2016 Mar 14;70(1):47-54. Epub 2015 Dec 14.

Department of Biostatistics, Harvard T.H. Chan School of Public Health.

Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Read More

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