Biomedical subjects

Wen-Jen Chen

Publications and source records attributed to Wen-Jen Chen.

3 recordsLinked to original sources

PubMed2003

Significance levels for stability pooling test: a simulation study.

Shelf life of a drug product is defined as the length of time under specific conditions of storage that the product will remain within specifications established to ensure its identity, strength, quality, and purity. The objective of an new drug application (NDA) stability study is to collect and evaluate the evidence in support of the sponsor-proposed shelf life. The proposed shelf life is supported when each batch of the drug products in the study has shelf life no shorter than the proposed shelf life. When the value of the batch mean changes linearly, sometimes batches of the same product may share the same slope or regression line. In practice, batches are pooled to have a common estimate of slope or regression line when there is no significant difference in slope or regression line. Such practice is often applied in pooling across levels of a design factor such as package or strength in a stability study designed with multiple factors. However, falsely pooling different slopes or intercepts may increase false positive rates on the decision of approval for the proposed shelf life. The proposed algorithm used with the simulation technique tries to reduce false pooling rates for testing slope or intercept differences in order to bring down the false positive rates on the decision of approval for the proposed shelf life to the prespecified Type-I error rate of 5 or 10%.

Algorithms↗

PubMed2003

ANCOVA approach for shelf life analysis of stability study of multiple factor designs.

For a traditional multiple batch stability design with no other factor, the conventional analysis is analysis of covariance (ANCOVA) modeling using F-tests based on type I sum of squares to determine whether the batches may be pooled for a common estimate of the linear regression line(s). In the last decade, many multiple factor designs were proposed in stability studies. With the objective of model selection, the generalization of the conventional ANCOVA model using type I sum of squares to designs with multiple factors requires a prespecified hierarchical pooling test ordering to determine whether any of the factors may be eliminated. Different shelf life estimates may be derived using different hierarchical pooling test orderings. On the other hand, setting the hierarchical ordering can be subjective and controversial. The stepwise modeling based on F-tests using type III sum of squares for model determination and factor elimination is proposed to eliminate such difficulties.

Drug Stability↗

PubMed2003

Shelf life determination based on equivalence assessment.

In a regular analysis of covariance (ANCOVA) approach to stability analysis, the decision for pooling data from different batches plays a key role in the determination of the shelf life of the drug product. Conventionally, the decision to pool data for the estimate of slope and intercept of common or individual regression lines is made by "no evidence to reject the null hypothesis of no difference." With typically limited observations, a significance level of much higher than 0.05 was recommended for the pooling tests in order to avoid inflation of type-I error rate of the shelf life testing. This logic of the pooling test decision making discouraged the use of replicates to improve power of testing and precision of estimation. The concept of pooling by equivalence test was originally proposed by Ruberg and Hsu in their 1990 article "Multiple comparison procedures for pooling batches in stability studies" Such a concept has evolved to pooling batches based on the shelf life equivalence test by Yoshioka et al. in their 1996 article "Power of analysis of variance for assessing batch-variation of stability data of pharmaceuticals." In this article, an approximation test of shelf life equivalence and a test of chemical value equivalence for the data pooling decision are proposed as an alternative to the conventional ANCOVA approach.

Confidence Intervals↗