Japanese Journal of Biometrics

Original Article

Sensitivity Analysis in Alpha Factor Analysis

Zenaida F. Mateo, Yoshimasa Odaka, Yutaka Tanaka

1993 Volume 14 Issue 1.2 Pages 1-15
Published: September 30, 1993
Released on J-STAGE: February 23, 2012

DOIhttps://doi.org/10.5691/jjb.14.1

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There are several sensitivity analysis procedures which have been considered by Tanaka and Okada (1989 a, b, c) to investigate the phenomena of how a small change of data affects the outcome of factor analysis. Among them are those for principal factor analysis (PFA), maximum likelihood factor analysis (MLFA) and least square factor analysis (LSFA). This motivates us to show that a similar technique can also be utilized to develop the sensitivity analysis in alpha factor analysis (AFA). Some examples are explained to illustrate the present procedure and a comparison is made in particular with the cases of PFA and MLFA.

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Review

Interaction and Confounding in Clinical Trials

Hideki Origasa

1993 Volume 14 Issue 1.2 Pages 17-30
Published: September 30, 1993
Released on J-STAGE: February 23, 2012

DOIhttps://doi.org/10.5691/jjb.14.17

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This paper critically revicws two important concepts which should be considered even in the analysis of clinical trials. These are interaction and confounding. Both must often be a great burden for the interpretation. It may be more difficult to detect the existence of confounding than interaction. It is noticed that testing for the imbalance of baselinc factors is a part of the check for confounding. It illustrates a realistic situation in which both confounding and interaction are involved. Tests for interaction are shown as the basis of odds ratio, risk ratio and risk difference, with an empirical comparison. Finally, testing for qualitative interactions is discussed for a possiblc application.

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Preliminary Report

On Estimation of ED₅₀ using Generalized Linear models and Generalized Additive models

Etsuo Miyaoka

1993 Volume 14 Issue 1.2 Pages 31-45
Published: September 30, 1993
Released on J-STAGE: February 23, 2012

DOIhttps://doi.org/10.5691/jjb.14.31

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To estimate the median effective dose (ED₅₀), we models the dose-response curve by using generalized linear models and generalized additive models as their extension. The estimation of ED₅₀ in generalized linear models has been known but not so for generalized additive models. We compare the result from nonparametric fitting of a generalized additive model with those of generalized linear models.

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A Stochastic Model for Proliferation of Cancer Cclls and Growth of Tumor
—A Monte Carlo Simulation Study—

Megu Ohtaki

1993 Volume 14 Issue 1.2 Pages 47-54
Published: September 30, 1993
Released on J-STAGE: February 23, 2012

DOIhttps://doi.org/10.5691/jjb.14.47

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A Monte Carlo simulation study was performed, on the bases of a simple branching process model concerning proliferation of cancer cells, to investigate the relationship between the length of the tumor growth period and the characteristics of proliferation of cancer cells. This simulation suggested that even a slight change of the proliferation condition may cause a great difference in the length of the tumor growth and the morbidity of cancer as well.

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