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Statistical Methods in Medical Research Jan 2023We revisit several conditionally formulated Gaussian Markov random fields, known as the intrinsic conditional autoregressive model, the proper conditional autoregressive... (Review)
Review
We revisit several conditionally formulated Gaussian Markov random fields, known as the intrinsic conditional autoregressive model, the proper conditional autoregressive model, and the Leroux et al. conditional autoregressive model, as well as convolution models such as the well known Besag, York and Mollie model, its (adaptive) re-parameterization, and its scaled alternatives, for their roles of modelling underlying spatial risks in Bayesian disease mapping. Analytic and simulation studies, with graphic visualizations, and disease mapping case studies, present insights and critique on these models for their nature and capacities in characterizing spatial dependencies, local influences, and spatial covariance and correlation functions, and in facilitating stabilized and efficient posterior risk prediction and inference. It is illustrated that these models are Gaussian (Markov) random fields of different spatial dependence, local influence, and (covariance) correlation functions and can play different and complementary roles in Bayesian disease mapping applications.
Topics: Bayes Theorem; Computer Simulation; Normal Distribution; Spatial Analysis; Models, Statistical
PubMed: 36317373
DOI: 10.1177/09622802221129040 -
Biometrical Journal. Biometrische... Mar 2022In this paper, we present the Type I multivariate zero-inflated Conway-Maxwell-Poisson distribution, whose development is based on the extension of the Type I...
In this paper, we present the Type I multivariate zero-inflated Conway-Maxwell-Poisson distribution, whose development is based on the extension of the Type I multivariate zero-inflated Poisson distribution. We developed important properties of the distribution and present a regression model. The AIC and BIC criteria are used to select the best fitted model. Two real data sets have been used to illustrate the proposed model. Moreover, we conclude by stating that the Type I multivariate zero-inflated Conway-Maxwell-Poisson distribution produces a better fitted model for multivariate count data with excess of zeros.
Topics: Models, Statistical; Poisson Distribution
PubMed: 35285065
DOI: 10.1002/bimj.202000249 -
Tidsskrift For Den Norske Laegeforening... Sep 2019
Topics: Chi-Square Distribution; Data Interpretation, Statistical; Humans; Statistics, Nonparametric
PubMed: 31502782
DOI: 10.4045/tidsskr.18.0125 -
Archives of Iranian Medicine Apr 2022Statistical methods (SM) are a ubiquitous tool in research. This study aimed to review SM used in original article published in the (AIM) and assess their effect on... (Review)
Review
BACKGROUND
Statistical methods (SM) are a ubiquitous tool in research. This study aimed to review SM used in original article published in the (AIM) and assess their effect on article acceptance period.
METHODS
The original articles published in the period 2015-2019 from volumes 18 to 22 and issues 1 to 12 of the AIM were reviewed and six items such as SM, study design, statistical population, sample size, software and acceptance period were extracted. Mean (SD), frequency (percentage) and multiple response analysis (MRA) were used for description. The Kruskal-Wallis test and Spearman correlation coefficient were used for data analysis in SPSS 26 with significance level at 5%.
RESULTS
During the study period, 423 original articles were reviewed. The statistical population in most of them was patients (38.8% and 164 articles), and most studies (51.5% and 218 articles) had a sample size of less than 500 people. The study design in most of the articles was analytical-observational (55.1% and 233 articles), and 79.7% (337 articles) used SPSS for data analysis. The median (IQR) acceptance period was 194 (134.25). MRA results showed that the highest rate of use of SM was related to descriptive statistics (277 articles, 30.3%) and Chi square test (130 articles, 14.2%). In the last two years, the acceptance period had a declining trend. There was no significant relation between mentioned variables and acceptance period (>0.05).
CONCLUSION
Contrary to the researchers' misconceptions, the acceptance period was not affected by SM, study design, statistical population, sample size, or type of software.
Topics: Chi-Square Distribution; Humans; Iran; Medicine; Research Design; Research Personnel
PubMed: 35942999
DOI: 10.34172/aim.2022.43 -
PloS One 2021Languages emerge and change over time at the population level though interactions between individual speakers. It is, however, hard to directly observe how a single...
Languages emerge and change over time at the population level though interactions between individual speakers. It is, however, hard to directly observe how a single speaker's linguistic innovation precipitates a population-wide change in the language, and many theoretical proposals exist. We introduce a very general mathematical model that encompasses a wide variety of individual-level linguistic behaviours and provides statistical predictions for the population-level changes that result from them. This model allows us to compare the likelihood of empirically-attested changes in definite and indefinite articles in multiple languages under different assumptions on the way in which individuals learn and use language. We find that accounts of language change that appeal primarily to errors in childhood language acquisition are very weakly supported by the historical data, whereas those that allow speakers to change incrementally across the lifespan are more plausible, particularly when combined with social network effects.
Topics: Computer Simulation; Humans; Language; Language Development; Learning; Models, Theoretical; Poisson Distribution; Population; Time Factors
PubMed: 34077472
DOI: 10.1371/journal.pone.0252582 -
Behavior Research Methods Feb 2021Ceiling and floor effects are often observed in social and behavioral science. The current study examines ceiling/floor effects in the context of the t-test and ANOVA,... (Review)
Review
Ceiling and floor effects are often observed in social and behavioral science. The current study examines ceiling/floor effects in the context of the t-test and ANOVA, two frequently used statistical methods in experimental studies. Our literature review indicated that most researchers treated ceiling or floor data as if these data were true values, and that some researchers used statistical methods such as discarding ceiling or floor data in conducting the t-test and ANOVA. The current study evaluates the performance of these conventional methods for t-test and ANOVA with ceiling or floor data. Our evaluation also includes censored regression with regard to its capacity for handling ceiling/floor data. Furthermore, we propose an easy-to-use method that handles ceiling or floor data in t-tests and ANOVA by using properties of truncated normal distributions. Simulation studies were conducted to compare the performance of the methods in handling ceiling or floor data for t-test and ANOVA. Overall, the proposed method showed greater accuracy in effect size estimation and better-controlled Type I error rates over other evaluated methods. We developed an easy-to-use software package and web applications to help researchers implement the proposed method. Recommendations and future directions are discussed.
Topics: Analysis of Variance; Humans; Normal Distribution; Research Design
PubMed: 32671580
DOI: 10.3758/s13428-020-01407-2 -
PloS One 2023The log-normal distribution, often used to model animal abundance and its uncertainty, is central to ecological modeling and conservation but its statistical properties... (Review)
Review
The log-normal distribution, often used to model animal abundance and its uncertainty, is central to ecological modeling and conservation but its statistical properties are less intuitive than those of the normal distribution. The right skew of the log-normal distribution can be considerable for highly uncertain estimates and the median is often chosen as a point estimate. However, the use of the median can become complicated when summing across populations since the median of the sum of log-normal distributions is not the sum of the constituent medians. Such estimates become sensitive to the spatial or taxonomic scale over which abundance is being summarized and the naive estimate (the median of the distribution representing the sum across populations) can become grossly inflated. Here we review the statistical issues involved and some alternative formulations that might be considered by ecologists interested in modeling abundance. Using a recent estimate of global avian abundance as a case study (Callaghan et al. 2021), we investigate the properties of several alternative methods of summing across species' abundance, including the sorted summing used in the original study (Callaghan et al. 2021) and the use of shifted log-normal distributions, truncated normal distributions, and rectified normal distributions. The appropriate method of summing across distributions was intimately tied to the use of the mean or median as the measure of central tendency used as the point estimate. Use of the shifted log-normal distribution, however, generated scale-consistent estimates for global abundance across a spectrum of contexts. Our paper highlights how seemingly inconsequential decisions regarding the estimation of abundance yield radically different estimates of global abundance and its uncertainty, with conservation consequences that are underappreciated and require careful consideration.
Topics: Animals; Normal Distribution; Statistical Distributions; Birds
PubMed: 36634090
DOI: 10.1371/journal.pone.0280351 -
Urology Feb 2023To evaluate female author representation in urologic literature as compared to the proportion of female practicing urologists. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To evaluate female author representation in urologic literature as compared to the proportion of female practicing urologists.
METHODS
A cross-sectional study was designed to analyze trends in women authorship of urology publications in 2019 as compared to AUA 2019 census data. The 5 highest impact urologic journals in 2019 were identified using the publicly available SCImago Journal Rank (SJR) indices. Author genders and study categorization were independently determined by 2 authors. Chi-squared test was used for statistical analyses.
RESULTS
The 5 highest impact urologic journals in 2019 as per SJR were European Urology, Journal of Urology, British Journal of Urology International, Prostate Cancer and Prostatic Diseases, and Nature Reviews Urology. A total of 501 publications were included for analysis. Women comprised 22.1% of first authors and 14.6% of senior authors. The proportion of publications authored by women was significantly higher than would be expected based on population proportions from the AUA 2019 census data for women as both first (P < .0001) and senior author (P =.0005). Similarly, women authorship was significantly higher than expected for basic science (P < .0001), clinical medicine (P <.0001), economics/practice management (P =.0002), editorial (P =.0027), and review/meta-analysis (P <.0001) publications.
CONCLUSION
The present study demonstrates that women contribute to the urologic literature significantly more than would be expected based on the proportion of practicing female urologists. However, with the persistence of gender gap in academic medicine promotions, further research into contributing factors and strategies for improvement are needed to promote greater women representation in academia.
Topics: Humans; Male; Female; Urology; Authorship; Cross-Sectional Studies; Censuses; Chi-Square Distribution
PubMed: 36535363
DOI: 10.1016/j.urology.2022.11.040 -
Biometrics Sep 2021Gaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges...
Gaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges occur as the number of the clusters is often unknown a priori. This paper focuses on clustering non-Gaussian functional data without the prior information of the number of clusters. We introduce a semiparametric mixed normal transformation model to accommodate non-Gaussian functional data, and propose a penalized approach to simultaneously estimate the parameters, transformation function, and the number of clusters. The estimators are shown to be consistent and asymptotically normal. The practical utility of the methods is confirmed via simulations as well as an application of the analysis of Alzheimer's disease study. The proposed method yields much less classification error than the existing methods. Data used in preparation of this paper were obtained from the Alzheimer's Disease Neuroimaging Initiative database.
Topics: Alzheimer Disease; Cluster Analysis; Humans; Neuroimaging; Normal Distribution
PubMed: 32749677
DOI: 10.1111/biom.13349 -
ENeuro 2022Model selection is often implicit: when performing an ANOVA, one assumes that the normal distribution is a good model of the data; fitting a tuning curve implies that an... (Review)
Review
Model selection is often implicit: when performing an ANOVA, one assumes that the normal distribution is a good model of the data; fitting a tuning curve implies that an additive and a multiplicative scaler describes the behavior of the neuron; even calculating an average implicitly assumes that the data were sampled from a distribution that has a finite first statistical moment: the mean. Model selection may be explicit, when the aim is to test whether one model provides a better description of the data than a competing one. As a special case, clustering algorithms identify groups with similar properties within the data. They are widely used from spike sorting to cell type identification to gene expression analysis. We discuss model selection and clustering techniques from a statistician's point of view, revealing the assumptions behind, and the logic that governs the various approaches. We also showcase important neuroscience applications and provide suggestions how neuroscientists could put model selection algorithms to best use as well as what mistakes should be avoided.
Topics: Algorithms; Cluster Analysis; Neurons; Normal Distribution
PubMed: 35835556
DOI: 10.1523/ENEURO.0066-22.2022