{"title":"Practical Considerations in Sample Size Determination of Japanese Subgroup for a Multi-Regional Oncology Clinical Trial","authors":"Sachio Ogawa, R. Sekiguchi, H. Uesaka","doi":"10.5691/JJB.30.81","DOIUrl":"https://doi.org/10.5691/JJB.30.81","url":null,"abstract":"For Japanese pharmaceutical companies, one of the critical issues in the design of multi-regional clinical trials (MRCT) is the number of Japanese patients. In the document issued by the Ministry of Health, Labour and Welfare (MHLW) in 2007, “Basic principles on Global Clinical Trials,” an issue was raised about the Japanese subgroup sample size and the importance of “consistency of results.” Ideas on how to calculate the sample size of a given region and the regions overall have been proposed by Uesaka (2006, 2009). For this purpose, he identified two types of consistency criteria and two types of efficacy criteria. For both efficacy and consistency these types consist of either 1) comparing the results of a specific region with those of the other regions combined, or 2) comparing the results of a specific region with those of the regions overall. Based on the efficacy criterion 2, Sekiguchi et al. (2007) examined the sample size of the Japanese subgroup in an oncology MRCT.In this study, we present a simulation that shows the relationship of Japanese subgroup sample size to both types of efficacy criteria in an oncology MRCT.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132533362","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}
T. Kusunoki, J. Matsuoka, H. Ohtsu, T. Kagimura, Hidekazu Nakamura
{"title":"Relationship between Intraclass and Concordance Correlation Coefficients: Similarities and Differences","authors":"T. Kusunoki, J. Matsuoka, H. Ohtsu, T. Kagimura, Hidekazu Nakamura","doi":"10.5691/JJB.30.35","DOIUrl":"https://doi.org/10.5691/JJB.30.35","url":null,"abstract":"The intraclass and concordance correlation coefficients (ICC and CCC) are popular indices of reliability or agreement for continuous variables. While various versions of ICC are used according to study design, the CCC is solely used as an index of agreement among fixed raters. However, we considered other versions of CCC in connection with all types of ICC, as classified by McGraw and Wong (Psychological Methods 1: 30-46, 1996), and examined the similarities and differences between ICCs and CCCs. Because they were found to be compatibly similar to each other, it is considered that CCC has a wider range of applicability and ICC shares the framework of CCC including factors of precision and accuracy as well as applicability to pairwise sub-analysis among multiple raters. Although it is said that some version of ICC should selectively be used according to study purpose and design, useful information is added by the combined use with the other ICCs and CCCs and by pairwise sub-analysis. For a better understanding of the study results, additional information concerning the possible factors which could lower or enhance precision and/or accuracy will be necessary.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132442039","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":"A Case Study on the Optimal Design of Clinical Pharmacology Trials with Restrictions on the Dosing Schedule","authors":"Kazuyo Kikuchi, C. Hamada, I. Yoshimura","doi":"10.5691/JJB.30.1","DOIUrl":"https://doi.org/10.5691/JJB.30.1","url":null,"abstract":"A clinical pharmacology trial, which examines the safety and pharmacodynamics of an investigational drug, is typically the first time that the drug is administered to humans. We are, therefore, often forced to maintain some restrictions on the trial conditions; for example, incremental doses in succeeding stages may be necessary when safety is concerned, and the repetition of the treatment on the same subject may be restricted in terms of the imposition on and convenience of subjects. The present paper investigated optimal trial designs under such restrictions, adopting Ds-optimality (D-optimality for subset) as the criterion.In order to identify the optimal design, all admissible designs that satisfy the restrictions were listed in a lexical order and their optimality was compared. As a result, it was revealed that a relatively high number of subjects were allocated lower doses in the optimal design when the increment restriction was regarded as relevant, whereas more reasonable designs were identified as optimal when the restrictions were modified.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124433845","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":"Influence of Random Effects Incorporated in the Analysis of Pharmacological Data Based on a Four-parameter Logistic Model","authors":"M. Yamada, C. Hamada, I. Yoshimura","doi":"10.5691/JJB.30.17","DOIUrl":"https://doi.org/10.5691/JJB.30.17","url":null,"abstract":"In the later phases of selecting candidate chemicals for drug development, pharmacological experiments are conducted using animal organs or human peripheral blood cells. In these experiments, data analysis is often performed on the basis of mixed-effect models so that the individuality effect can be incorporated in the dose-response relationship. This paper studied the influence of the incorporation of random effects on parameters in a logistic model intended for analysis, with the assumption that the dose-response relationship is really described by a four-parameter logistic model. Using Monte-Carlo simulation experiments, we compared the performances of eight models in which various mixed-effects were incorporated. In each of the eight analysis models, five methods of calculation, namely, the standard two-stage method (STS), first-order approximation method (FOA), Laplacian approximation method (LAP), Monte Carlo integration method (MCI), and Gaussian quadrature method (GAU), were applied to the simulation data. The eight analysis models and five estimation methods were compared, using estimability and the deviation of estimates from the true value as the criteria. The results revealed the analysis model incorporating the random effect on only the maximum response to be the best. The results also indicated that the FOA, LAP, MCI, and GAU methods had almost the same performances for this analysis model. The authors recommend LAP as the preferred method because of the simplicity of its calculation.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121425319","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":"Bayesian Design of Single-arm Clinical Trials with Binary Endpoints : A Review","authors":"S. Teramukai","doi":"10.5691/JJB.29.111","DOIUrl":"https://doi.org/10.5691/JJB.29.111","url":null,"abstract":"The aim of single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase exploratory trials or proof-of-concept studies as they take into account information that accrues during a trial. Posterior and predictive probabilities are then updated and so become more accurate as the trial progresses. If the relevant external information is available, the decision will be made with a smaller sample size. The goal of this paper is to provide a review for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, a clinical trial is presented as a real example to illustrate how to conduct a Bayesian approach for single-arm clinical trials with binary endpoints.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114949793","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}
Seitaro Yoshida, Y. Matsuyama, Y. Ohashi, H. Ueshima
{"title":"A Poisson Mixed Effects Model for Investigating the Exposure-by-Cohort Interaction: A Gibbs Sampling Approach","authors":"Seitaro Yoshida, Y. Matsuyama, Y. Ohashi, H. Ueshima","doi":"10.5691/JJB.29.61","DOIUrl":"https://doi.org/10.5691/JJB.29.61","url":null,"abstract":"A meta-analysis is a useful method for taking the findings of many studies and combining them in the hopes of identifying consistent patterns and sources of disagreement among those findings. While we interpret the average exposure effect, it is necessary to examine the homogeneity of the observed exposure effects across cohort, that is, exposure-by-cohort interaction. If the homogeneity is confirmed, the conclusions concerning exposure effects can be generalized to a broader population. In this paper, a Poisson mixed effects model is used to investigate the cohort effects on the exposure as well as on the baseline risk. The marginal posterior distributions are estimated by a Markov Chain Monte Carlo method, i.e. the Gibbs sampling, to overcome current computational limitations. We illustrate the methods with analyses of data from the Japan Arteriosclerosis Longitudinal Study, in which the effects of smoking on stroke events are examined based on the individual data of 23,860 subjects among 10 cohorts.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125238946","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":"A Spatial Scan Statistic with a Restricted Likelihood Ratio","authors":"T. Tango","doi":"10.5691/JJB.29.75","DOIUrl":"https://doi.org/10.5691/JJB.29.75","url":null,"abstract":"Kulldorff (1997) developed a circular spatial scan statistic for identifying the most likely cluster of disease that maximizes the likelihood ratio and his software SaTScan has been widely used for geographical disease cluster detection and disease surveillance. To detect non-circular clusters which cannot be detected by Kulldorff's circular spatial scan statistic, several non-circular spatial scan statistics have been proposed. However, it does not seem to be well recognized that these spatial scan statistics tend to detect the most likely cluster much larger than the true cluster by swallowing neighbouring regions with non-elevated risk. This paper proposes a new spatial scan statistic free from such an undesirable property by modifying the likelihood ratio so that it scans only the regions with elevated risk. Monte Carlo Simulation study shows that the proposed circular spatial scan statistic is shown to have better ability to identify the true cluster compared with Kulldorff's one in all the cluster models considered. The proposed circular spatial scan statisitc is illustrated with mortality data from cerebrovascular disease in Tokyo Metropolitan area, Japan.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"68 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115675861","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":"REML Estimation of Genetic Correlations between Sexes on Beef Carcass Traits Using a Procedure of the Average Information Algorithm","authors":"A. Arakawa, H. Iwaisaki","doi":"10.5691/JJB.29.97","DOIUrl":"https://doi.org/10.5691/JJB.29.97","url":null,"abstract":"In animal breeding and genetics applications, one topic is the evaluation of sexual dimorphism and genetic correlation between sexes. Also, in some cases, variances may be heterogeneous between levels of factors such as breed and herd as well as sex. Researches about these topics need the method for estimating relevant components of (co)variances. The objectives of this study are to derive a computational procedure of the average information algorithm for the restricted maximum likelihood estimation of the relevant (co)variances, and to estimate genetic correlations between sexes on beef carcass traits. In the current computational procedure, a derived expression for the average information matrix is used, whose elements are expressed using the solutions to the mixed model equations in a bivariate mixed linear model with heterogeneous variance and nil covariance of residuals assumed. For the current procedure, replacing the Hessian matrix by the derived average information matrix, a quasi-Newton type procedure is defined for the iterative estimation. Using simulated datasets, computing performance of the current procedure is investigated comparing with the expectation-maximization algorithm, the current procedure is applied to beef carcass traits data to estimate heritability for each sex and genetic correlation between sexes, and then the characteristics of the current procedure is concisely discussed.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125868237","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":"An Application of an Adaptive Design to a Clinical Trial","authors":"S. Morita","doi":"10.5691/JJB.29.S53","DOIUrl":"https://doi.org/10.5691/JJB.29.S53","url":null,"abstract":"This paper presents an application of an adaptive design to a randomized phase II clinical trial and discuss several practical issues that could occur when applying an adaptive design. The discussion is based on presentations with respect to adaptive designs, which were given at the 2006 Japanese Joint Statistical Meeting.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123842402","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":"Discussion on Morikawa's Paper","authors":"T. Tango","doi":"10.5691/JJB.29.S107","DOIUrl":"https://doi.org/10.5691/JJB.29.S107","url":null,"abstract":"","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115749121","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}