Japanese Journal of Biometrics
Online ISSN : 2185-6494
Print ISSN : 0918-4430
ISSN-L : 0918-4430
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  • Tomohiro Shinozaki, Isao Yokota, Koji Oba, Kayoko Kozuma, Kentaro Saka ...
    2020 Volume 41 Issue 1 Pages 1-35
    Published: 2020
    Released: December 04, 2020
    Supplementary material

    Prediction models are usually developed through model-construction and validation. Especially for binary or time-to-event outcomes, the risk prediction models should be evaluated through several aspects of the accuracy of prediction. With unified algebraic notation, we present such evaluation measures for model validation from five statistical viewpoints that are frequently reported in medical literature: 1) Brier score for prediction error; 2) sensitivity, specificity, and C-index for discrimination; 3) calibration-in-the-large, calibration slope, and Hosmer-Lemeshow statistic for calibration; 4) net reclassification and integrated discrimination improvement indexes for reclassification; and 5) net benefit for clinical usefulness. Graphical representation such as a receiver operating characteristic curve, a calibration plot, or a decision curve helps researchers interpret these evaluation measures. The interrelationship between them is discussed, and their definitions and estimators are extended to time-to-event data suffering from outcome-censoring. We illustrate their calculation through example datasets with the SAS codes provided in the web appendix.

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  • Satoshi Teramukai
    2020 Volume 41 Issue 1 Pages 37-54
    Published: 2020
    Released: December 04, 2020

    Fisher’s randomization rule has been widely viewed as a revolutionary invention in experimental design. The three rationales of randomization in clinical trials are (i) randomization ensures that known and unknown confounders are asymptotically controlled, (ii) the use of randomization itself provides the basis of statistical inference, supposing patients in a clinical trial are a non-random sample of a population, and (iii) the act of randomization mitigates selection bias by providing unpredictability in treatment allocation. Randomized controlled trials have been the gold standard for more than five decades, while such trials may be costly, inconvenient and ethically challenging. Some Fisherian statisticians have emphasized the importance of design-based inference based on randomization test, however some statisticians does not agree with them. From the Bayesian point of view, the randomization sequence is ancillary for a parameter of interest, and randomization itself is not absolutely essential although it may sometimes be helpful. In this review, I provide an overview of the rationales of randomization and the related topics, and discuss the significance and limitations of randomization in clinical trials.

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  • Kentaro Sakamaki, Michio Kanekiyo, Shoichi Ohwada, Kentaro Matsuura, T ...
    2020 Volume 41 Issue 1 Pages 55-91
    Published: 2020
    Released: December 04, 2020
    It is common to use hypothesis testing to decide whether an investigational drug is ineffective and to determine sample size. However, it may not be good practice that only hypothesis testing is used for sample size determination, go/no-go decision making, and drug development decisions, especially in exploratory clinical trials. That is because important factors for decision making, such as treatment effects, drug development costs, and gains after launch, are not considered in hypothesis testing. The Bayesian decision theory is one of the approaches to consider such factors for decision making. The utility, which is defined by using important information such as cost, benefit, and disease severity, is used for decision making in the decision theory. In consideration of uncertainties of data and parameters, the expected value of the utility is used for decision making in the Bayesian decision theory. In this article, we explain basic concepts of the Bayesian decision theory, backward induction for calculation of expected value of utility in sequential decision-making, and introduce some approaches using the Bayesian decision theory in clinical trials. We summarize actions, utilities and sample size determination for applications of Bayesian decision theory in future clinical trials.
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