Presenting Medical Statistics from Proposal to Publication

2013 ◽  
Author(s):  
Janet Peacock ◽  
Sally Kerry
Keyword(s):  
Statistical Methods ◽  
Journal Article ◽  
Real Data ◽  
Statistical Analyses ◽  
Medical Statistics ◽  
Wide Range ◽  
Good Presentation

Presenting Medical Statistics includes a wide range of statistical analyses, and all the statistical methods are illustrated using real data. Labelled figures show the Stata and SPSS commands needed to obtain the analyses, with indications of which information should be extracted from the output for reporting. The relevant results are then presented as for a report or journal article, to illustrate the principles of good presentation.

Presenting Medical Statistics from Proposal to Publication

2017 ◽  
Author(s):  
Janet L. Peacock ◽  
Sally M. Kerry ◽  
Raymond R. Balise
Keyword(s):  
Journal Article ◽  
Real Data ◽  
Research Process ◽  
Research Report ◽  
Medical Statistics ◽  
Extensive Experience ◽  
Wide Range ◽  
Sample Size Calculations ◽  
The Usa ◽  
The Uk

Presenting Medical Statistics from Proposal to Publication (second edition) aims to show readers how to conduct a wide range of statistical analyses from sample size calculations through to multifactorial regressions that are needed in the research process. The second edition of ‘Presenting’ has been revised and updated and now includes Stata, SAS, SPSS, and R. The book shows how to interpret each computer output and illustrates how to present the results and accompanying text in a format suitable for a peer-reviewed journal article or research report. All analyses are illustrated using real data and all programming code, outputs, and datasets used in the book are available on a website for readers to freely download and use. ‘Presenting’ includes practical information and helpful tips for software, all statistical methods used, and the research process. It is written by three experienced biostatisticians, Janet Peacock, Sally Kerry, and Ray Balise from the UK and the USA, and is born out of their extensive experience conducting collaborative medical research, teaching medical students, physicians, and other health professionals, and providing researchers with advice.

Oxford Handbook of Medical Statistics

2020 ◽  
Author(s):  
Janet L. Peacock ◽  
Phil J. Peacock
Keyword(s):  
Clinical Practice ◽  
Medical Students ◽  
Medical Research ◽  
Sample Size ◽  
Statistical Methods ◽  
Medical Statistics ◽  
Wide Range ◽  
Chi Squared ◽  
Published Research ◽  
Difficult Subject

A good understanding of medical statistics is essential to evaluate medical research and to choose appropriate ways of implementing findings in clinical practice. The Oxford Handbook of Medical Statistics, second edition, has been written to provide doctors and medical students with a comprehensive yet concise account of this often difficult subject. Described by readers as a ‘statistical Bible’, this new edition maintains the accessibility and thoroughness of the original, and includes comprehensive updates including new sections on transitional medicine, cluster designs, and modern statistical packages. The handbook promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t and chi-squared tests, to complex multifactorial analyses, all using examples from published research. References and further reading are included, to allow deeper understanding on specific topics. Featuring a new chapter on how to use this book in different medical contexts, the Oxford Handbook of Medical Statistics helps readers to conduct their own research and critically appraise others' work.

Oxford Handbook of Medical Statistics

2010 ◽  
Author(s):  
Janet Peacock ◽  
Philip Peacock
Keyword(s):  
Medical Students ◽  
Sample Size ◽  
Statistical Methods ◽  
Study Design ◽  
Medical Statistics ◽  
Wide Range ◽  
Chi Squared ◽  
Published Research ◽  
Difficult Subject

Written in an easily accessible style, the Oxford Handbook of Medical Statistics provides doctors and medical students with a concise and thorough account of this often difficult subject. It promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t- and chi-squared tests, to complex multifactorial analyses, using examples from published research.

The Statistics of Causal Inference: A View from Political Methodology

2015 ◽  
Vol 23 (3) ◽  
pp. 313-335 ◽  
Author(s):  
Luke Keele
Keyword(s):  
Statistical Analysis ◽  
Causal Inference ◽  
Statistical Methods ◽  
Statistical Analyses ◽  
Causal Effects ◽  
Causal Relationships ◽  
Causal Interpretation ◽  
Political Methodology ◽  
Statistical Estimates ◽  
Wide Range

Many areas of political science focus on causal questions. Evidence from statistical analyses is often used to make the case for causal relationships. While statistical analyses can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on specific statistical methods, such as matching, I focus more on the assumptions needed to give statistical estimates a causal interpretation. Such assumptions are often referred to as identification assumptions, and these assumptions are critical to any statistical analysis about causal effects. I outline a wide range of identification assumptions and highlight the design-based approach to causal inference. I conclude with an overview of statistical methods that are frequently used for causal inference.

It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

2020 ◽  
Author(s):  
Luis Anunciacao ◽  
janet squires ◽  
J. Landeira-Fernandez
Keyword(s):  
Internal Structure ◽  
Statistical Methods ◽  
Principal Component ◽  
Psychological Theory ◽  
Published Data ◽  
Multivariate Statistical ◽  
Exact Answer ◽  
Wide Range ◽  
Ages And Stages Questionnaire ◽  
The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

scholarly journals Editorial, special issue on “Advances in Robust Statistics”

METRON ◽  
2021 ◽  
Author(s):  
Marco Riani ◽  
Mia Hubert
Keyword(s):  
Statistical Methods ◽  
Robust Statistics ◽  
Statistical Analyses ◽  
Striking Feature ◽  
Special Issue ◽  
Current State ◽  
Editorial Special Issue ◽  
Data Points ◽  
Robust Statistical Methods

AbstractStarting with 2020 volume, the journal Metron has decided to celebrate the centenary since its foundation with three special issues. This volume is dedicated to robust statistics. A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Robust statistical methods provide useful tools for reducing this sensitivity, through the detection of the outliers by first fitting the majority of the data and then by flagging deviant data points. The six papers in this issue cover a wide orientation in all fields of robustness. This editorial first provides some facts about the history and current state of robust statistics and then summarizes the contents of each paper.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

scholarly journals CAMISIM: Simulating metagenomes and microbial communities

2018 ◽  
Author(s):  
Adrian Fritz ◽  
Peter Hofmann ◽  
Stephan Majda ◽  
Eik Dahms ◽  
Johannes Dröge ◽  
...  
Keyword(s):  
Microbial Communities ◽  
De Novo ◽  
Real Data ◽  
Small Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Taxonomic Profiling ◽  
Benchmark Data ◽  
Sequencing Technologies ◽  
Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

scholarly journals Efficient estimation of stereo thresholds: what slope should be assumed for the psychometric function?

2019 ◽  
Author(s):  
Ignacio Serrano-Pedraza ◽  
Kathleen Vancleef ◽  
William Herbert ◽  
Nicola Goodship ◽  
Maeve Woodhouse ◽  
...  
Keyword(s):  
Psychometric Function ◽  
Population Distribution ◽  
Real Data ◽  
Logistic Function ◽  
Efficient Estimation ◽  
Threshold Estimate ◽  
Bayesian Procedure ◽  
Detection Thresholds ◽  
Bayesian Procedures ◽  
Wide Range

Bayesian staircases are widely used in psychophysics to estimate detection thresholds. Simulations have revealed the importance of the parameters selected for the assumed subject’s psychometric function in enabling thresholds to be estimated with small bias and high precision. One important parameter is the slope of the psychometric function, or equivalently its spread. This is often held fixed, rather than estimated for individual subjects, because much larger numbers of trials are required to estimate the spread as well as the threshold. However, if this fixed value is wrong, the threshold estimate can be biased. Here we determine the optimal slope to minimize bias and maximize precision when measuring stereoacuity with Bayesian staircases. We performed 2- and 4AFC disparity detection stereo experiments in order to measure the spread of the disparity psychometric function in human observers assuming a Logistic function. We found a wide range, between 0.03 and 3.5 log10 arcsec, with little change with age. We then ran simulations to examine the optimal spread using the real data. From our simulations and for three different experiments, we recommend selecting assumed spread values between the percentiles 60-80% of the population distribution of spreads (these percentiles can be extended to other type of thresholds). For stereo thresholds, we recommend a spread σ=1.7 log10 arcsec for 2AFC (slope 𝛽 = 4.3/log10 arcsec), and σ=1.5 log10 arcsec for 4AFC (𝛽 = 4.9/log10 arcsec). Finally, we compared a Bayesian procedure (ZEST using the optimal σ) with five Bayesian procedures that are versions of ZEST-2D, Psi, and Psi-marginal. In general, our recommended procedure showed the lowest threshold bias and highest precision.

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