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.

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 largescale 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.

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.

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.

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.

Many clinical studies are conducted in Japan with sample sizes that are not deter-mined 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.

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.