Interval Estimation of the 50% Effective Time in Small Sample Assay Data
Takashi Sozu, Ayako Shiraishi, Yohei Hyodo, Chikuma Hamada, Isao Yoshimura
Abstract
Takashi Sozu, Ayako Shiraishi, Yohei Hyodo, Chikuma Hamada, Isao Yoshimura
Abstract
The time score for 50% cell viability (50% effective time; ET50) is used as the index of skin irritation evaluated by a three-dimensional human skin model, such as TESTSKIN and Vitrolife-Skin. ET50 is conventionally estimated by linear interpolation of measurements at two time points, which yields cell viabilities above and below 50%. This simple method is problematic in that biased estimates are occasionally obtained and confidence intervals cannot be appropriately constructed. We compared four estimation methods including a logistic regression method, a log-time regression method, a linear regression method and a newly proposed two-stage method through a Monte-Carlo simulation study in small sample sizes due to the experimental restrictions. The logistic regression method provides almost unbiased estimates, although the confidence interval for ET50 is occasionally not obtained. The log-time regression method and the linear regression method provide positive biased estimates, although the confidence interval for ET50 is obtained in any case. The two-stage method is reasonable, in which the log-time regression method is adopted only if the logistic regression method cannot construct a confidence interval for ET50.
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The time score for 50% cell viability (50% effective time; ET50) is used as the index of skin irritation evaluated by a three-dimensional human skin model, such as TESTSKIN and Vitrolife-Skin. ET50 is conventionally estimated by linear interpolation of measurements at two time points, which yields cell viabilities above and below 50%. This simple method is problematic in that biased estimates are occasionally obtained and confidence intervals cannot be appropriately constructed. We compared four estimation methods including a logistic regression method, a log-time regression method, a linear regression method and a newly proposed two-stage method through a Monte-Carlo simulation study in small sample sizes due to the experimental restrictions. The logistic regression method provides almost unbiased estimates, although the confidence interval for ET50 is occasionally not obtained. The log-time regression method and the linear regression method provide positive biased estimates, although the confidence interval for ET50 is obtained in any case. The two-stage method is reasonable, in which the log-time regression method is adopted only if the logistic regression method cannot construct a confidence interval for ET50.
Key concepts: Statistics, Confidence interval, Logistic regression, Mathematics, Segmented regression, Proper linear model, Polynomial regression, Linear regression