Bumblebees ( Bombus spp.) are important pollinators of both wildflowers and crops. In recent year, declines of population size have been reported in many Bumblebee species in various countries. In order to design effective conservation plans, nest (colony) density in a habitat is an essentially important information. In the present study, a novel method for estimating nest density using molecular genetic markers was proposed, by extending the concept of neighborhood size in population genetics. The proposed method was compared with the conventional method used in previous studies by applying to microsatellite data of Bombus hypocrite sapporoensis . Based on the obtained estimates of nest density, advantage and disadvantage of the two methods were discussed. An interpretation of estimate obtained by the proposed method was also presented from a viewpoint of conservation genetics.
{"title":"A Novel Method for Estimating Nest Density in Bumblebee Populations from Molecular Genetic Markers","authors":"T. Nomura","doi":"10.5691/JJB.39.23","DOIUrl":"https://doi.org/10.5691/JJB.39.23","url":null,"abstract":"Bumblebees ( Bombus spp.) are important pollinators of both wildflowers and crops. In recent year, declines of population size have been reported in many Bumblebee species in various countries. In order to design effective conservation plans, nest (colony) density in a habitat is an essentially important information. In the present study, a novel method for estimating nest density using molecular genetic markers was proposed, by extending the concept of neighborhood size in population genetics. The proposed method was compared with the conventional method used in previous studies by applying to microsatellite data of Bombus hypocrite sapporoensis . Based on the obtained estimates of nest density, advantage and disadvantage of the two methods were discussed. An interpretation of estimate obtained by the proposed method was also presented from a viewpoint of conservation genetics.","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":"116222956","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 a relative potency assessment, it is necessary to make assumptions about the similarities between substances and their dose-response profile. For example, in a parallel line bioassay which uses the dose-response data within the linear response range, we need to demonstrate that the dose-response slopes of the study substances are approximately parallel. When using multiple animals for testing, it is also crucial to confirm that this parallelism exists not only for the averages but also within each animal (Uehara et al. 2016a). Meanwhile, when applying a linear mixed effect model to the analysis of parallel line assays, the between-substance difference of the slopes can be treated as a random effect. Thus, under a balanced assay design, we can derive an efficient score test to assess the quantile of the slope difference (McCulloch et al. 2008), which enables us to determine whether the majority of animals have their slope difference within the acceptable range. We applied this approach to the assessment of intrasubject parallelism with the intention of ameliorating the conservatism of our previous method (Uehara et al. 2016b). We present an example that uses the proposed method, along with the results of simulation studies.
在相对效价评估中，有必要对物质之间的相似性及其剂量反应谱作出假设。例如，在使用线性响应范围内的剂量-反应数据的平行线生物测定中，我们需要证明研究物质的剂量-反应斜率近似平行。当使用多个动物进行测试时，同样重要的是要确认这种并行性不仅存在于平均值，而且存在于每个动物内部(Uehara et al. 2016a)。同时，将线性混合效应模型应用于平行线试验分析时，边坡的物质间差异可视为随机效应。因此，在平衡试验设计下，我们可以推导出一种有效的评分测试来评估坡度差异的分位数(McCulloch et al. 2008)，这使我们能够确定大多数动物的坡度差异是否在可接受的范围内。我们将这种方法应用于评估主体内并行性，目的是改善我们之前方法的保守性(Uehara et al. 2016b)。我们给出了一个使用所提出的方法的例子，以及仿真研究的结果。
{"title":"Evaluation of Intrasubject Parallelism in Balanced Ex Vivo Bioassay Using One-sided Efficient Score Tests of Slope Difference Quantile","authors":"H. Uehara","doi":"10.5691/JJB.39.9","DOIUrl":"https://doi.org/10.5691/JJB.39.9","url":null,"abstract":"In a relative potency assessment, it is necessary to make assumptions about the similarities between substances and their dose-response profile. For example, in a parallel line bioassay which uses the dose-response data within the linear response range, we need to demonstrate that the dose-response slopes of the study substances are approximately parallel. When using multiple animals for testing, it is also crucial to confirm that this parallelism exists not only for the averages but also within each animal (Uehara et al. 2016a). Meanwhile, when applying a linear mixed effect model to the analysis of parallel line assays, the between-substance difference of the slopes can be treated as a random effect. Thus, under a balanced assay design, we can derive an efficient score test to assess the quantile of the slope difference (McCulloch et al. 2008), which enables us to determine whether the majority of animals have their slope difference within the acceptable range. We applied this approach to the assessment of intrasubject parallelism with the intention of ameliorating the conservatism of our previous method (Uehara et al. 2016b). We present an example that uses the proposed method, along with the results of simulation studies.","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":"129926775","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}
On March 7th, 2016, the American Statistical Association released its “ASA Statement on Statistical Significance and P -values,” which provided 6 principles to improve the conduct or interpretation of quantitative research. Misunderstanding and misuse of statistical tests and P -values were discussed many times in the epidemiologic field. In this paper, I gave a summary of the ASA statement and its translation process into Japanese. Then, I discussed how to avoid misunderstanding or misuse of statistical tests or P -values in epidemiologic observational studies.
{"title":"The ASA Statement and P -values in Epidemiologic Research","authors":"Tosiya Sato","doi":"10.5691/JJB.38.109","DOIUrl":"https://doi.org/10.5691/JJB.38.109","url":null,"abstract":"On March 7th, 2016, the American Statistical Association released its “ASA Statement on Statistical Significance and P -values,” which provided 6 principles to improve the conduct or interpretation of quantitative research. Misunderstanding and misuse of statistical tests and P -values were discussed many times in the epidemiologic field. In this paper, I gave a summary of the ASA statement and its translation process into Japanese. Then, I discussed how to avoid misunderstanding or misuse of statistical tests or P -values in epidemiologic observational studies.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116859429","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}
Many clinical studies are conducted in Japan with sample sizes that are not determined statistically. Application of Neyman-Pearson type statistical tests to data from such studies is not justifiable and should be stopped. Also 5% significance level that is commonly employed in a clinical study without taking into account disease, drug and other factors is not justifiable. Alternatively, the use of p-value is recommended in this paper as a measure of showing the magnitude of difference of two treatments; it is the role of principal investigator to summarize the study results by considering disease, drug and other factors, sample sizes and p-value.
{"title":"p -value is a Useful and Excellent Measure for Reporting Results of Statistical Analysis on Clinical Data","authors":"T. Yanagawa","doi":"10.5691/JJB.38.153","DOIUrl":"https://doi.org/10.5691/JJB.38.153","url":null,"abstract":"Many clinical studies are conducted in Japan with sample sizes that are not determined statistically. Application of Neyman-Pearson type statistical tests to data from such studies is not justifiable and should be stopped. Also 5% significance level that is commonly employed in a clinical study without taking into account disease, drug and other factors is not justifiable. Alternatively, the use of p-value is recommended in this paper as a measure of showing the magnitude of difference of two treatments; it is the role of principal investigator to summarize the study results by considering disease, drug and other factors, sample sizes and p-value.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117149325","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}
The recent controversy over the use and abuse of p -values in statistical data analysis sheds a light on the epistemological diversity of scientific researches and the nature of science. Since the nineteenth century theoretical statisticians including Karl Pearson, Ronald A. Fisher, Jerzy Neyman, and Egon S.Pearson constructed the mathematical basis of modern statistics, for example, experimental design, sampling distributions, or hypothesis testing, etc. However, statistical reasoning as empirical inference is not necessarily limited to the Neyman-Pearson’s decision-making paradigm. Any kind of non-deductive inference—for example, abduction—also uses statistics as an exploratory tool for relative ranking among alternative hypotheses and models. We must understand not only the proper use of statistical methods and procedures but also the nature of each science to which statistics is applied.
{"title":"Reproducibility Crisis?—Diversity of Science and the p -value Problem in Ststistics","authors":"N. Minaka","doi":"10.5691/JJB.38.117","DOIUrl":"https://doi.org/10.5691/JJB.38.117","url":null,"abstract":"The recent controversy over the use and abuse of p -values in statistical data analysis sheds a light on the epistemological diversity of scientific researches and the nature of science. Since the nineteenth century theoretical statisticians including Karl Pearson, Ronald A. Fisher, Jerzy Neyman, and Egon S.Pearson constructed the mathematical basis of modern statistics, for example, experimental design, sampling distributions, or hypothesis testing, etc. However, statistical reasoning as empirical inference is not necessarily limited to the Neyman-Pearson’s decision-making paradigm. Any kind of non-deductive inference—for example, abduction—also uses statistics as an exploratory tool for relative ranking among alternative hypotheses and models. We must understand not only the proper use of statistical methods and procedures but also the nature of each science to which statistics is applied.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114154752","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":"P -value in Medical and Agricultural Research—Beyond p < 0.05 Paradigm—","authors":"Hideki Saganami","doi":"10.5691/JJB.38.107","DOIUrl":"https://doi.org/10.5691/JJB.38.107","url":null,"abstract":"","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121398017","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}
Quantitative high throughput sreening (qHTS) is a technique which has originally developed as a powerful tool for drug discovery and lately is expanding its application to the neighboring field, e.g. toxicological screening test for environmental chemicals. A wide variety of in-vitro biological activity of a large amount of chemical materials can be assayed with a low cost and in a short time period. As a result of two large-scale pharmacogenomic studies being published in 2012, the reproducibility of the result of screening assay of cytotoxicity for 15 drugs in 471 cell lines was revealed to be unexpectedly low. The necessity of developments of statistical methods suitable for qHTS data were emphasized. In this review, the authors explain 3 statistical methods with applications to qHTS data, which has been proposed since 2013: 1. robust ridge regression estimators for nonlinear models in the purpose of testing bioactivity of chemicals; 2. Bayesian hierarchical dose-response modeling; 3. using weighted entropy to rank chemicals. Characteristics of each method were compared, and the prospects were presented.
{"title":"Statistical Methods for Evaluation of Bioactivity of Chemicals Using Quantitative High Throughput Screening","authors":"Asuka Nemoto, M. Ushijima","doi":"10.5691/JJB.38.93","DOIUrl":"https://doi.org/10.5691/JJB.38.93","url":null,"abstract":"Quantitative high throughput sreening (qHTS) is a technique which has originally developed as a powerful tool for drug discovery and lately is expanding its application to the neighboring field, e.g. toxicological screening test for environmental chemicals. A wide variety of in-vitro biological activity of a large amount of chemical materials can be assayed with a low cost and in a short time period. As a result of two large-scale pharmacogenomic studies being published in 2012, the reproducibility of the result of screening assay of cytotoxicity for 15 drugs in 471 cell lines was revealed to be unexpectedly low. The necessity of developments of statistical methods suitable for qHTS data were emphasized. In this review, the authors explain 3 statistical methods with applications to qHTS data, which has been proposed since 2013: 1. robust ridge regression estimators for nonlinear models in the purpose of testing bioactivity of chemicals; 2. Bayesian hierarchical dose-response modeling; 3. using weighted entropy to rank chemicals. Characteristics of each method were compared, and the prospects were presented.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133779607","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 2016 the American Statistical Association published “ASA Statement on Statistical Significance and P -Values.” In this statement it seems that the use of statistical tests or p -values is discouraged because they are misused and misinterpreted. I doubt whether a statistical procedure such as test of significance should be rejected because it is misused and misinterpreted. I pose some questions about the ASA statement.
{"title":"Informal Comments on the ASA 2016 Statement","authors":"Tetsuhisa Miwa","doi":"10.5691/JJB.38.163","DOIUrl":"https://doi.org/10.5691/JJB.38.163","url":null,"abstract":"In 2016 the American Statistical Association published “ASA Statement on Statistical Significance and P -Values.” In this statement it seems that the use of statistical tests or p -values is discouraged because they are misused and misinterpreted. I doubt whether a statistical procedure such as test of significance should be rejected because it is misused and misinterpreted. I pose some questions about the ASA statement.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128357551","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 planning a multi-regional clinical trial including Japan, the sample size for Japanese patients is often considered based on the probability of obtaining a consistent result between Japanese subpopulation and the overall study population, as recommended in the Japanese guideline “Basic Principles on Global Clinical Trials.” We review the commonly used existing method for Japanese sample size calculation based on obtaining a consistent result for survival endpoint. Through simulations, we note that Japanese sample size based on the existing method tended to have less actual power than the nominal power for consistency, especially when there is a large treatment effect. We propose alternative methods based on the delta method and numerical integrations. Our proposed methods give similar Japanese sample sizes, and our simulation studies show that our proposed methods provide the actual power close to the nominal power for consistency.
{"title":"A Re-examination of Japanese Sample Size Calculation for Multi-regional Clinical Trial Evaluating Survival Endpoint","authors":"N. Hayashi, Y. Itoh","doi":"10.5691/JJB.38.79","DOIUrl":"https://doi.org/10.5691/JJB.38.79","url":null,"abstract":"In planning a multi-regional clinical trial including Japan, the sample size for Japanese patients is often considered based on the probability of obtaining a consistent result between Japanese subpopulation and the overall study population, as recommended in the Japanese guideline “Basic Principles on Global Clinical Trials.” We review the commonly used existing method for Japanese sample size calculation based on obtaining a consistent result for survival endpoint. Through simulations, we note that Japanese sample size based on the existing method tended to have less actual power than the nominal power for consistency, especially when there is a large treatment effect. We propose alternative methods based on the delta method and numerical integrations. Our proposed methods give similar Japanese sample sizes, and our simulation studies show that our proposed methods provide the actual power close to the nominal power for consistency.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130031873","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 this article, we discuss the role of P values in multiple testing to associate a large number of genetic or molecular features with a phenotypic variable of interest in biomedical omics studies. For multiple tests in such association analyses, we distinguish those conducted for confirmatory purpose, as seen in genome-wide association studies to determine disease-associated variants, from those for exploratory screening of associated features. For the latter, exploratory analysis, we discuss application of the ROC curve analysis used in diagnostic medicine, as an alternative, but more relevant framework, rather than the standard framework based on multiple testing that controls false positives only. Finally, partly based on arguments made in the field of omics studies, we make some comments on future endeavors by statisticians to disseminate discussions given in the ASA’s Statement on P -Values (Wasserstein and Lazar, 2016, The American Statistician, 70, 129-133) to improve statistical practice in various scientific fields.
在本文中，我们讨论了P值在多重测试中的作用，将大量遗传或分子特征与生物医学组学研究中感兴趣的表型变量联系起来。对于这种关联分析中的多个测试，我们区分了那些为确认目的而进行的测试，如在确定疾病相关变异的全基因组关联研究中看到的测试，以及那些为探索性筛选相关特征的测试。对于后者，探索性分析，我们讨论了用于诊断医学的ROC曲线分析的应用，作为一种替代的，但更相关的框架，而不是基于多个测试的标准框架，仅控制假阳性。最后，部分基于组学研究领域的论点，我们对统计学家未来的努力进行了一些评论，以传播ASA关于P值的声明中给出的讨论(Wasserstein和Lazar, 2016, the American statistical, 70,129 -133)，以改善各个科学领域的统计实践。
{"title":"Confirmatory and Exploratory Analyses in Omics Studies with Particular Focus on Multiple Testing and P -value","authors":"S. Matsui","doi":"10.5691/JJB.38.127","DOIUrl":"https://doi.org/10.5691/JJB.38.127","url":null,"abstract":"In this article, we discuss the role of P values in multiple testing to associate a large number of genetic or molecular features with a phenotypic variable of interest in biomedical omics studies. For multiple tests in such association analyses, we distinguish those conducted for confirmatory purpose, as seen in genome-wide association studies to determine disease-associated variants, from those for exploratory screening of associated features. For the latter, exploratory analysis, we discuss application of the ROC curve analysis used in diagnostic medicine, as an alternative, but more relevant framework, rather than the standard framework based on multiple testing that controls false positives only. Finally, partly based on arguments made in the field of omics studies, we make some comments on future endeavors by statisticians to disseminate discussions given in the ASA’s Statement on P -Values (Wasserstein and Lazar, 2016, The American Statistician, 70, 129-133) to improve statistical practice in various scientific fields.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128476828","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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