Reproducibility is the essence of a scientific research. Focusing on two-sample problems we discuss in this paper the reproducibility of statistical test results based on p-values. First, demonstrating large variability of p-values it is shown that p-values lack the reproducibility, in particular, if sample sizes are not enough. Second, a sample size formula is developed to assure the reproducibility probability of p-value at given level by assuming normal distributions with known variance. Finally, the sample size formula for the reproducibility in general framework is shown equivalent to the sample size formula that has been developed in the Neyman-Pearson type testing statistical hypothesis by employing the level of significance and size of power.
{"title":"Reproducibility of statistical test results based on p-value","authors":"T. Yanagawa","doi":"10.5691/jjb.40.69","DOIUrl":"https://doi.org/10.5691/jjb.40.69","url":null,"abstract":"Reproducibility is the essence of a scientific research. Focusing on two-sample problems we discuss in this paper the reproducibility of statistical test results based on p-values. First, demonstrating large variability of p-values it is shown that p-values lack the reproducibility, in particular, if sample sizes are not enough. Second, a sample size formula is developed to assure the reproducibility probability of p-value at given level by assuming normal distributions with known variance. Finally, the sample size formula for the reproducibility in general framework is shown equivalent to the sample size formula that has been developed in the Neyman-Pearson type testing statistical hypothesis by employing the level of significance and size of power.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133766073","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}
When a causal effect between treatment and outcome variables is observed, effects on the outcome are of interest to investigate the mechanisms among the outcome and treatment. Indirect effect is defined as the causal effect of the treatment on the outcome via the mediator. Direct effect is defined as the causal effect of the treatment on the outcome that is not through the mediator. In this paper, we discuss the estimation of direct and indirect effects based on the framework of potential response models focusing on the 4-way decomposition. Direct and indirect effect estimations are illustrated with two examples where the outcome, mediator, covariate variables are continuous and categorical data. Moreover, we discuss the estimation of clausal effects and the effect decomposition in the settings that include confounder of mediator and outcome affected by treatment, multiple mediators, or time-varying treatment in the presence of time-dependent confounder. a t −1, l t
{"title":"Estimation of direct and indirect effects under the counterfactual models","authors":"Shinjo Yada, R. Uozumi, M. Taguri","doi":"10.5691/jjb.40.81","DOIUrl":"https://doi.org/10.5691/jjb.40.81","url":null,"abstract":"When a causal effect between treatment and outcome variables is observed, effects on the outcome are of interest to investigate the mechanisms among the outcome and treatment. Indirect effect is defined as the causal effect of the treatment on the outcome via the mediator. Direct effect is defined as the causal effect of the treatment on the outcome that is not through the mediator. In this paper, we discuss the estimation of direct and indirect effects based on the framework of potential response models focusing on the 4-way decomposition. Direct and indirect effect estimations are illustrated with two examples where the outcome, mediator, covariate variables are continuous and categorical data. Moreover, we discuss the estimation of clausal effects and the effect decomposition in the settings that include confounder of mediator and outcome affected by treatment, multiple mediators, or time-varying treatment in the presence of time-dependent confounder. a t −1, l t ","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126134930","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}
{"title":"Permutation Inference Methods for the MMRM (Mixed-Effects Model for Repeated Measures) in Incomplete Longitudinal Data Analysis","authors":"Yoshifumi Ukyo, H. Noma","doi":"10.5691/jjb.40.15","DOIUrl":"https://doi.org/10.5691/jjb.40.15","url":null,"abstract":"","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129323516","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}
A central problem in medical research is how to make inferences about the causal effects of treatments or exposures. In this article, we review fundamental concepts for making such inferences in randomized clinical trials or observational studies. The statistical framework consists of potential outcomes, an assignment mechanism, and probability distributions. Randomization-based and model-based methods of statistical inference are illustrated with a series of extracorporeal membrane oxygenation (ECMO) clinical trials, which are thought-provoking in that each trial used different assignment mechanisms.
{"title":"Fundamental concepts for causal inference in medicine","authors":"Shiro Tanaka","doi":"10.5691/jjb.40.35","DOIUrl":"https://doi.org/10.5691/jjb.40.35","url":null,"abstract":"A central problem in medical research is how to make inferences about the causal effects of treatments or exposures. In this article, we review fundamental concepts for making such inferences in randomized clinical trials or observational studies. The statistical framework consists of potential outcomes, an assignment mechanism, and probability distributions. Randomization-based and model-based methods of statistical inference are illustrated with a series of extracorporeal membrane oxygenation (ECMO) clinical trials, which are thought-provoking in that each trial used different assignment mechanisms.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133509460","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}
We consider multiple comparison test procedures among treatment effects in a randomized block design. We propose closed testing procedures based on signed rank statistics and Friedman test statistics for all pairwise comparisons of treatment effects. Although anyone has been failed to discuss a distribution-free method except Bonferroni procedures as a multiple comparison test, the proposed procedures are exactly distribution-free. Next we consider the randomized block design under simple ordered restrictions of treatment effects. We propose distribution-free closed testing procedures based on one-sided signed rank statistics and rank statistics of Chacko (1963) for all pairwise comparisons. Simulation studies are performed under the null hypothesis and some alternative hypotheses. In this studies, the proposed procedures show a good performance. We also illustrate an application to death rates by using proposed procedures.
{"title":"Nonparametric Closed Testing Procedures for All Pairwise Comparisons in a Randomized Block Design","authors":"T. Shiraishi, Shin-ichi Matsuda","doi":"10.5691/jjb.40.1","DOIUrl":"https://doi.org/10.5691/jjb.40.1","url":null,"abstract":"We consider multiple comparison test procedures among treatment effects in a randomized block design. We propose closed testing procedures based on signed rank statistics and Friedman test statistics for all pairwise comparisons of treatment effects. Although anyone has been failed to discuss a distribution-free method except Bonferroni procedures as a multiple comparison test, the proposed procedures are exactly distribution-free. Next we consider the randomized block design under simple ordered restrictions of treatment effects. We propose distribution-free closed testing procedures based on one-sided signed rank statistics and rank statistics of Chacko (1963) for all pairwise comparisons. Simulation studies are performed under the null hypothesis and some alternative hypotheses. In this studies, the proposed procedures show a good performance. We also illustrate an application to death rates by using proposed procedures.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130356749","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}
Nowadays, many methods that employ the 16S ribosomal RNA gene (16S rRNA sequencing data) have been proposed for the analysis of gut microbial compositional data. 16S rRNA sequencing data is statistically multivariate count data. When multivariate data analysis methods are used for association analysis with a disease, 16S rRNA sequencing data is generally normalized before analysis models are fitted, because the total sequence read counts of the subjects are different. However, proper methods for normalization have not yet been discussed or proposed. Rarefying is one such normalization method that equals the total counts of subjects by subsampling a certain amount of counts from each subject. It was thought that if rarefying were combined with ensemble learning, performance improvement could be achieved. Then, we proposed an association analysis method by combining rarefying with ensemble learning and evaluated its performance by simulation experiment using several multivariate data analysis methods. The proposed method showed superior performance compared with other analysis methods, with regard to the identification ability of response-associated variables and the classification ability of a response variable. We also used each evaluated method to analyze the gut microbial data of Japanese people, and then compared these results.
{"title":"A New Association Analysis Method for Gut Microbial Compositional Data Using Ensemble Learning","authors":"T. Okui, Y. Matsuyama, S. Nakaji","doi":"10.5691/JJB.39.55","DOIUrl":"https://doi.org/10.5691/JJB.39.55","url":null,"abstract":"Nowadays, many methods that employ the 16S ribosomal RNA gene (16S rRNA sequencing data) have been proposed for the analysis of gut microbial compositional data. 16S rRNA sequencing data is statistically multivariate count data. When multivariate data analysis methods are used for association analysis with a disease, 16S rRNA sequencing data is generally normalized before analysis models are fitted, because the total sequence read counts of the subjects are different. However, proper methods for normalization have not yet been discussed or proposed. Rarefying is one such normalization method that equals the total counts of subjects by subsampling a certain amount of counts from each subject. It was thought that if rarefying were combined with ensemble learning, performance improvement could be achieved. Then, we proposed an association analysis method by combining rarefying with ensemble learning and evaluated its performance by simulation experiment using several multivariate data analysis methods. The proposed method showed superior performance compared with other analysis methods, with regard to the identification ability of response-associated variables and the classification ability of a response variable. We also used each evaluated method to analyze the gut microbial data of Japanese people, and then compared these results.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124858834","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}
A. Hirakawa, J. Asano, Hiroyuki Sato, H. Hashimoto, S. Teramukai
平川晃弘∗1・浅野淳一∗2・佐藤宏征∗3・橋本大哉∗4・手良向 聡∗5 Akihiro Hirakawa∗1 , Junichi Asano∗2 , Hiroyuki Sato∗3 , Hiroya Hashimoto∗4 and Satoshi Teramukai∗5 ∗1東京大学大学院 医学系研究科 生物統計情報学講座 ∗2独立行政法人 医薬品医療機器総合機構 新薬審査第四部 ∗3独立行政法人 医薬品医療機器総合機構 新薬審査第五部 ∗4国立病院機構 名古屋医療センター 臨床研究センター 臨床研究企画管理部 ∗5京都府立医科大学大学院医学研究科 生物統計学 ∗1Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan ∗2Office of New Drug IV, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗3Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗4Department of Clinical Research Management, Clinical Research Center, National Hospital Organization Nagoya Medical Center, Nagoya 460-0001, Japan ∗5Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan e-mail:hirakawa@m.u-tokyo.ac.jp
平川晃弘∗1・浅野淳一∗2・佐藤宏征∗3・橋本大哉∗4・手良向 聡∗5 Akihiro Hirakawa∗1 , Junichi Asano∗2 , Hiroyuki Sato∗3 , Hiroya Hashimoto∗4 and Satoshi Teramukai∗5 ∗1東京大学大学院 医学系研究科 生物統計情報学講座 ∗2独立行政法人 医薬品医療機器総合機構 新薬審査第四部 ∗3独立行政法人 医薬品医療機器総合機構 新薬審査第五部 ∗4国立病院機構 名古屋医療センター 臨床研究センター 臨床研究企画管理部 ∗5京都府立医科大学大学院医学研究科 生物統計学 ∗1Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan ∗2Office of New Drug IV, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗3Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗4Department of Clinical Research Management, Clinical Research Center, National Hospital Organization Nagoya Medical Center, Nagoya 460-0001, Japan ∗5Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan e-mail:hirakawa@m.u-tokyo.ac.jp
{"title":"Bayesian Basket Designs for Cancer Clinical Trials","authors":"A. Hirakawa, J. Asano, Hiroyuki Sato, H. Hashimoto, S. Teramukai","doi":"10.5691/JJB.39.103","DOIUrl":"https://doi.org/10.5691/JJB.39.103","url":null,"abstract":"平川晃弘∗1・浅野淳一∗2・佐藤宏征∗3・橋本大哉∗4・手良向 聡∗5 Akihiro Hirakawa∗1 , Junichi Asano∗2 , Hiroyuki Sato∗3 , Hiroya Hashimoto∗4 and Satoshi Teramukai∗5 ∗1東京大学大学院 医学系研究科 生物統計情報学講座 ∗2独立行政法人 医薬品医療機器総合機構 新薬審査第四部 ∗3独立行政法人 医薬品医療機器総合機構 新薬審査第五部 ∗4国立病院機構 名古屋医療センター 臨床研究センター 臨床研究企画管理部 ∗5京都府立医科大学大学院医学研究科 生物統計学 ∗1Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan ∗2Office of New Drug IV, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗3Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗4Department of Clinical Research Management, Clinical Research Center, National Hospital Organization Nagoya Medical Center, Nagoya 460-0001, Japan ∗5Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan e-mail:hirakawa@m.u-tokyo.ac.jp","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133265271","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}
A. Hirakawa, J. Asano, Hiroyuki Sato, S. Teramukai
平川晃弘∗1・浅野淳一∗2・佐藤宏征∗3・手良向 聡∗4 Akihiro Hirakawa∗1 , Junichi Asano∗2 , Hiroyuki Sato∗3 and Satoshi Teramukai∗4 ∗1東京大学大学院 医学系研究科 生物統計情報学講座 ∗2独立行政法人 医薬品医療機器総合機構 新薬審査第四部 ∗3独立行政法人 医薬品医療機器総合機構 新薬審査第五部 ∗4京都府立医科大学大学院医学研究科 生物統計学 ∗1Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan ∗2Office of New Drug IV, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗3Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗4Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan e-mail:hirakawa@m.u-tokyo.ac.jp
平川晃弘∗1・浅野淳一∗2・佐藤宏征∗3・手良向 聡∗4 Akihiro Hirakawa∗1 , Junichi Asano∗2 , Hiroyuki Sato∗3 and Satoshi Teramukai∗4 ∗1東京大学大学院 医学系研究科 生物統計情報学講座 ∗2独立行政法人 医薬品医療機器総合機構 新薬審査第四部 ∗3独立行政法人 医薬品医療機器総合機構 新薬審査第五部 ∗4京都府立医科大学大学院医学研究科 生物統計学 ∗1Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan ∗2Office of New Drug IV, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗3Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗4Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan e-mail:hirakawa@m.u-tokyo.ac.jp
{"title":"Cancer Clinical Trials Based on Master Protocol","authors":"A. Hirakawa, J. Asano, Hiroyuki Sato, S. Teramukai","doi":"10.5691/JJB.39.85","DOIUrl":"https://doi.org/10.5691/JJB.39.85","url":null,"abstract":"平川晃弘∗1・浅野淳一∗2・佐藤宏征∗3・手良向 聡∗4 Akihiro Hirakawa∗1 , Junichi Asano∗2 , Hiroyuki Sato∗3 and Satoshi Teramukai∗4 ∗1東京大学大学院 医学系研究科 生物統計情報学講座 ∗2独立行政法人 医薬品医療機器総合機構 新薬審査第四部 ∗3独立行政法人 医薬品医療機器総合機構 新薬審査第五部 ∗4京都府立医科大学大学院医学研究科 生物統計学 ∗1Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan ∗2Office of New Drug IV, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗3Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo 100-0013, Japan ∗4Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan e-mail:hirakawa@m.u-tokyo.ac.jp","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129577442","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}
In recent years, analysis methods of microbiome data are developing rapidly, and many methods for the microbial compositional data which uses the 16S ribosomal RNA gene (16S rRNA data) are proposed. But, methods of association analysis for longitudinally measured 16S rRNA data are not studied well. Latent dirichlet allocation model (LDA) which is used mainly in natural language processing and has high expansion possibilities came to be applied to 16S rRNA data analysis in the past few years. Then, we propose an association analysis method by modifying existing LDA: topic tracking model for longitudinal 16S rRNA data. As the result of predictive performance evaluation, proposed method showed superior performance compared with topic tracking model with regard to perplexity. We applied this method to microbial data of rural Japanese people and identified topics associated with obesity.
{"title":"A New Association Analysis Method for Longitudinally Measured Microbial Compositional Data Using Latent Dirichlet Allocation Model","authors":"T. Okui, S. Nakaji","doi":"10.5691/JJB.39.37","DOIUrl":"https://doi.org/10.5691/JJB.39.37","url":null,"abstract":"In recent years, analysis methods of microbiome data are developing rapidly, and many methods for the microbial compositional data which uses the 16S ribosomal RNA gene (16S rRNA data) are proposed. But, methods of association analysis for longitudinally measured 16S rRNA data are not studied well. Latent dirichlet allocation model (LDA) which is used mainly in natural language processing and has high expansion possibilities came to be applied to 16S rRNA data analysis in the past few years. Then, we propose an association analysis method by modifying existing LDA: topic tracking model for longitudinal 16S rRNA data. As the result of predictive performance evaluation, proposed method showed superior performance compared with topic tracking model with regard to perplexity. We applied this method to microbial data of rural Japanese people and identified topics associated with obesity.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127819968","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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