Pub Date : 2022-11-08DOI: 10.1080/00031305.2022.2143897
T. Hardwicke, M. Salholz-Hillel, M. Malički, Dénes Szűcs, Theiss Bendixen, J. Ioannidis
Abstract Scientific journals may counter the misuse, misreporting, and misinterpretation of statistics by providing guidance to authors. We described the nature and prevalence of statistical guidance at 15 journals (top-ranked by Impact Factor) in each of 22 scientific disciplines across five high-level domains (N = 330 journals). The frequency of statistical guidance varied across domains (Health & Life Sciences: 122/165 journals, 74%; Multidisciplinary: 9/15 journals, 60%; Social Sciences: 8/30 journals, 27%; Physical Sciences: 21/90 journals, 23%; Formal Sciences: 0/30 journals, 0%). In one discipline (Clinical Medicine), statistical guidance was provided by all examined journals and in two disciplines (Mathematics and Computer Science) no examined journals provided statistical guidance. Of the 160 journals providing statistical guidance, 93 had a dedicated statistics section in their author instructions. The most frequently mentioned topics were confidence intervals (90 journals) and p-values (88 journals). For six “hotly debated” topics (statistical significance, p-values, Bayesian statistics, effect sizes, confidence intervals, and sample size planning/justification) journals typically offered implicit or explicit endorsement and rarely provided opposition. The heterogeneity of statistical guidance provided by top-ranked journals within and between disciplines highlights a need for further research and debate about the role journals can play in improving statistical practice.
{"title":"Statistical Guidance to Authors at Top-Ranked Journals across Scientific Disciplines","authors":"T. Hardwicke, M. Salholz-Hillel, M. Malički, Dénes Szűcs, Theiss Bendixen, J. Ioannidis","doi":"10.1080/00031305.2022.2143897","DOIUrl":"https://doi.org/10.1080/00031305.2022.2143897","url":null,"abstract":"Abstract Scientific journals may counter the misuse, misreporting, and misinterpretation of statistics by providing guidance to authors. We described the nature and prevalence of statistical guidance at 15 journals (top-ranked by Impact Factor) in each of 22 scientific disciplines across five high-level domains (N = 330 journals). The frequency of statistical guidance varied across domains (Health & Life Sciences: 122/165 journals, 74%; Multidisciplinary: 9/15 journals, 60%; Social Sciences: 8/30 journals, 27%; Physical Sciences: 21/90 journals, 23%; Formal Sciences: 0/30 journals, 0%). In one discipline (Clinical Medicine), statistical guidance was provided by all examined journals and in two disciplines (Mathematics and Computer Science) no examined journals provided statistical guidance. Of the 160 journals providing statistical guidance, 93 had a dedicated statistics section in their author instructions. The most frequently mentioned topics were confidence intervals (90 journals) and p-values (88 journals). For six “hotly debated” topics (statistical significance, p-values, Bayesian statistics, effect sizes, confidence intervals, and sample size planning/justification) journals typically offered implicit or explicit endorsement and rarely provided opposition. The heterogeneity of statistical guidance provided by top-ranked journals within and between disciplines highlights a need for further research and debate about the role journals can play in improving statistical practice.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132429807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-03DOI: 10.1080/00031305.2022.2141856
Jangsun Baek, Jeong‐Soo Park
Abstract One of the challenges in clustering categorical data is the curse of dimensionality caused by the inherent sparsity of high-dimensional data, the records of which include a large number of attributes. The latent class model (LCM) assumes local independence between the variables in clusters, and is a parsimonious model-based clustering approach that has been used to circumvent the problem. The mixture of a log-linear model is more flexible but requires more parameters to be estimated. In this research, we recognize that each categorical observation can be conceived as a network with pairwise linked nodes, which are the response levels of the observation attributes. Therefore, the categorical data for clustering is considered a finite mixture of different component layer networks with distinct patterns. We apply a penalized composite likelihood approach to a finite mixture of networks for sparse multivariate categorical data to reduce the number of parameters, implement the EM algorithm to estimate the model parameters, and show that the estimates are consistent and satisfy asymptotic normality. The performance of the proposed approach is shown to be better in comparison with the conventional methods for both synthetic and real datasets.
{"title":"Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach","authors":"Jangsun Baek, Jeong‐Soo Park","doi":"10.1080/00031305.2022.2141856","DOIUrl":"https://doi.org/10.1080/00031305.2022.2141856","url":null,"abstract":"Abstract One of the challenges in clustering categorical data is the curse of dimensionality caused by the inherent sparsity of high-dimensional data, the records of which include a large number of attributes. The latent class model (LCM) assumes local independence between the variables in clusters, and is a parsimonious model-based clustering approach that has been used to circumvent the problem. The mixture of a log-linear model is more flexible but requires more parameters to be estimated. In this research, we recognize that each categorical observation can be conceived as a network with pairwise linked nodes, which are the response levels of the observation attributes. Therefore, the categorical data for clustering is considered a finite mixture of different component layer networks with distinct patterns. We apply a penalized composite likelihood approach to a finite mixture of networks for sparse multivariate categorical data to reduce the number of parameters, implement the EM algorithm to estimate the model parameters, and show that the estimates are consistent and satisfy asymptotic normality. The performance of the proposed approach is shown to be better in comparison with the conventional methods for both synthetic and real datasets.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124637559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-03DOI: 10.1080/00031305.2022.2141858
Roy Bower, Justin Hager, Chris Cherniakov, Samay Gupta, William Cipolli
ABSTRACT We provide a case study for motivating and teaching nonparametric statistical inference alongside traditional parametric approaches. The case consists of analyses by Bracht et al. who use analysis of variance (ANOVA) to assess the applicability of the human microfibrillar-associated protein 4 (MFAP4) as a biomarker for hepatic fibrosis in hepatitis C patients. We revisit their analyses and consider two nonparametric approaches: Mood’s median test and the Kruskal-Wallis test. We demonstrate how this case study enables instructors to discuss critical assumptions of parametric procedures while comparing and contrasting the results of multiple approaches. Interestingly, only one of the three approaches creates groupings that match the treatment recommendations of the European Association for the Study of the Liver (EASL). We provide guidance and resources to aid instructors in directing their students through this case study at various levels, including R code and novel R shiny applications for conducting the analyses in the classroom.
{"title":"A Case for Nonparametrics","authors":"Roy Bower, Justin Hager, Chris Cherniakov, Samay Gupta, William Cipolli","doi":"10.1080/00031305.2022.2141858","DOIUrl":"https://doi.org/10.1080/00031305.2022.2141858","url":null,"abstract":"ABSTRACT We provide a case study for motivating and teaching nonparametric statistical inference alongside traditional parametric approaches. The case consists of analyses by Bracht et al. who use analysis of variance (ANOVA) to assess the applicability of the human microfibrillar-associated protein 4 (MFAP4) as a biomarker for hepatic fibrosis in hepatitis C patients. We revisit their analyses and consider two nonparametric approaches: Mood’s median test and the Kruskal-Wallis test. We demonstrate how this case study enables instructors to discuss critical assumptions of parametric procedures while comparing and contrasting the results of multiple approaches. Interestingly, only one of the three approaches creates groupings that match the treatment recommendations of the European Association for the Study of the Liver (EASL). We provide guidance and resources to aid instructors in directing their students through this case study at various levels, including R code and novel R shiny applications for conducting the analyses in the classroom.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-02DOI: 10.1080/00031305.2022.2126684
Scott A. Roths
{"title":"Probability, Statistics, and Data: A Fresh Approach Using R","authors":"Scott A. Roths","doi":"10.1080/00031305.2022.2126684","DOIUrl":"https://doi.org/10.1080/00031305.2022.2126684","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"1992 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128608841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-02DOI: 10.1080/00031305.2022.2126685
J. Cui, H. Fu
{"title":"Statistical Issues in Drug Development, 3rd ed.","authors":"J. Cui, H. Fu","doi":"10.1080/00031305.2022.2126685","DOIUrl":"https://doi.org/10.1080/00031305.2022.2126685","url":null,"abstract":"","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121569231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-30DOI: 10.1080/00031305.2022.2131625
Xin Xiong, Ivor Cribben
Abstract Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics.
{"title":"The State of Play of Reproducibility in Statistics: An Empirical Analysis","authors":"Xin Xiong, Ivor Cribben","doi":"10.1080/00031305.2022.2131625","DOIUrl":"https://doi.org/10.1080/00031305.2022.2131625","url":null,"abstract":"Abstract Reproducibility, the ability to reproduce the results of published papers or studies using their computer code and data, is a cornerstone of reliable scientific methodology. Studies where results cannot be reproduced by the scientific community should be treated with caution. Over the past decade, the importance of reproducible research has been frequently stressed in a wide range of scientific journals such as Nature and Science and international magazines such as The Economist. However, multiple studies have demonstrated that scientific results are often not reproducible across research areas such as psychology and medicine. Statistics, the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data, prides itself on its openness when it comes to sharing both computer code and data. In this article, we examine reproducibility in the field of statistics by attempting to reproduce the results in 93 published papers in prominent journals using functional magnetic resonance imaging (fMRI) data during the 2010–2021 period. Overall, from both the computer code and the data perspective, among all the 93 examined papers, we could only reproduce the results in 14 (15.1%) papers, that is, the papers provide both executable computer code (or software) with the real fMRI data, and our results matched the results in the paper. Finally, we conclude with some author-specific and journal-specific recommendations to improve the research reproducibility in statistics.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127833819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-27DOI: 10.1080/00031305.2022.2127896
M. McGee, Benjamin Williams, Jacy Sparks
Abstract Conventional wisdom dispersed by fans and coaches in the stands at almost any high school track meet suggests female athletes typically peak around 10th grade or earlier (15 years of age), particularly for distance runners, and male athletes continuously improve. Given that universities in the United States typically recruit track and field athletes from high school teams, it is important to understand the age of peak performance at the high school level. Athletes are often recruited starting in their sophomore year of high school and individuals develop at different rates during adolescence; however, the individual development factor is usually not taken into account during recruitment. In this study, we curate data on event times for high school track and field athletes from the years 2011 to 2019 to determine the trajectory of fastest times for male and female athletes in the 200m, 400m, 800m, and 1600m races. We show, through visualizations and models, that, for most athletes, the sophomore peak is a myth. Performance is mostly dependent on the individual athlete. That said, the trajectories cluster into four or five types, depending on the race distance. We explain the significance of the types for future recruitment.
{"title":"Athlete Recruitment and the Myth of the Sophomore Peak","authors":"M. McGee, Benjamin Williams, Jacy Sparks","doi":"10.1080/00031305.2022.2127896","DOIUrl":"https://doi.org/10.1080/00031305.2022.2127896","url":null,"abstract":"Abstract Conventional wisdom dispersed by fans and coaches in the stands at almost any high school track meet suggests female athletes typically peak around 10th grade or earlier (15 years of age), particularly for distance runners, and male athletes continuously improve. Given that universities in the United States typically recruit track and field athletes from high school teams, it is important to understand the age of peak performance at the high school level. Athletes are often recruited starting in their sophomore year of high school and individuals develop at different rates during adolescence; however, the individual development factor is usually not taken into account during recruitment. In this study, we curate data on event times for high school track and field athletes from the years 2011 to 2019 to determine the trajectory of fastest times for male and female athletes in the 200m, 400m, 800m, and 1600m races. We show, through visualizations and models, that, for most athletes, the sophomore peak is a myth. Performance is mostly dependent on the individual athlete. That said, the trajectories cluster into four or five types, depending on the race distance. We explain the significance of the types for future recruitment.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117213132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-23DOI: 10.1080/00031305.2022.2127897
Gang Han, T. Santner, Haiqun Lin, Ao Yuan
Abstract The Bayesian-frequentist hybrid model and associated inference can combine the advantages of both Bayesian and frequentist methods and avoid their limitations. However, except for few special cases in existing literature, the computation under the hybrid model is generally nontrivial or even unsolvable. This article develops a computation algorithm for hybrid inference under any general loss functions. Three simulation examples demonstrate that hybrid inference can improve upon frequentist inference by incorporating valuable prior information, and also improve Bayesian inference based on non-informative priors where the latter leads to biased estimates for the small sample sizes used in inference. The proposed method is illustrated in applications including a biomechanical engineering design and a surgical treatment of acral lentiginous melanoma.
{"title":"Bayesian-Frequentist Hybrid Inference in Applications with Small Sample Sizes","authors":"Gang Han, T. Santner, Haiqun Lin, Ao Yuan","doi":"10.1080/00031305.2022.2127897","DOIUrl":"https://doi.org/10.1080/00031305.2022.2127897","url":null,"abstract":"Abstract The Bayesian-frequentist hybrid model and associated inference can combine the advantages of both Bayesian and frequentist methods and avoid their limitations. However, except for few special cases in existing literature, the computation under the hybrid model is generally nontrivial or even unsolvable. This article develops a computation algorithm for hybrid inference under any general loss functions. Three simulation examples demonstrate that hybrid inference can improve upon frequentist inference by incorporating valuable prior information, and also improve Bayesian inference based on non-informative priors where the latter leads to biased estimates for the small sample sizes used in inference. The proposed method is illustrated in applications including a biomechanical engineering design and a surgical treatment of acral lentiginous melanoma.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115216103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-16DOI: 10.1080/00031305.2023.2179664
N. Alon, Y. Malinovsky
Abstract What is the number of rolls of fair six-sided dice until the first time the total sum of all rolls is a prime? We compute the expectation and the variance of this random variable up to an additive error of less than . This is a solution to a puzzle suggested by DasGupta in the Bulletin of the Institute of Mathematical Statistics, where the published solution is incomplete. The proof is simple, combining a basic dynamic programming algorithm with a quick Matlab computation and basic facts about the distribution of primes.
{"title":"Hitting a Prime in 2.43 Dice Rolls (On Average)","authors":"N. Alon, Y. Malinovsky","doi":"10.1080/00031305.2023.2179664","DOIUrl":"https://doi.org/10.1080/00031305.2023.2179664","url":null,"abstract":"Abstract What is the number of rolls of fair six-sided dice until the first time the total sum of all rolls is a prime? We compute the expectation and the variance of this random variable up to an additive error of less than . This is a solution to a puzzle suggested by DasGupta in the Bulletin of the Institute of Mathematical Statistics, where the published solution is incomplete. The proof is simple, combining a basic dynamic programming algorithm with a quick Matlab computation and basic facts about the distribution of primes.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"386 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125001980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-03DOI: 10.1080/00031305.2022.2128874
Chris Rohlfs
Abstract Overfitting in linear regression is broken down into two main causes. First, the formula for the estimator includes “forbidden knowledge” about training observations’ residuals, and it loses this advantage when deployed out-of-sample. Second, the estimator has “specialized training” that makes it particularly capable of explaining movements in the predictors that are idiosyncratic to the training sample. An out-of-sample counterpart is introduced to the popular “leverage” measure of training observations’ importance. A new method is proposed to forecast out-of-sample fit at the time of deployment, when the values for the predictors are known but the true outcome variable is not. In Monte Carlo simulations and in an empirical application using MRI brain scans, the proposed estimator performs comparably to Predicted Residual Error Sum of Squares (PRESS) for the average out-of-sample case and unlike PRESS, also performs consistently across different test samples, even those that differ substantially from the training set.
{"title":"Forbidden Knowledge and Specialized Training: A Versatile Solution for the Two Main Sources of Overfitting in Linear Regression","authors":"Chris Rohlfs","doi":"10.1080/00031305.2022.2128874","DOIUrl":"https://doi.org/10.1080/00031305.2022.2128874","url":null,"abstract":"Abstract Overfitting in linear regression is broken down into two main causes. First, the formula for the estimator includes “forbidden knowledge” about training observations’ residuals, and it loses this advantage when deployed out-of-sample. Second, the estimator has “specialized training” that makes it particularly capable of explaining movements in the predictors that are idiosyncratic to the training sample. An out-of-sample counterpart is introduced to the popular “leverage” measure of training observations’ importance. A new method is proposed to forecast out-of-sample fit at the time of deployment, when the values for the predictors are known but the true outcome variable is not. In Monte Carlo simulations and in an empirical application using MRI brain scans, the proposed estimator performs comparably to Predicted Residual Error Sum of Squares (PRESS) for the average out-of-sample case and unlike PRESS, also performs consistently across different test samples, even those that differ substantially from the training set.","PeriodicalId":342642,"journal":{"name":"The American Statistician","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123100300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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