Installation & Upgrade: stab for R is one of R packages. Thus, users have to download and install R first, and then run it. Under R Console, users can click "Packages" from the menu then ->"Install package(s)..." --> select a CRAN mirror site near you and you will see the list of currently available R packages. Just select "stab" from the list and click "OK" to start installation. Then it's done! If you want to upgrade from
previous version of stab for R when a new version is released, please go the menu and click "Packages", and then select "Update packages...". then select a CRAN mirror site near you and click "OK". Done! Pretty easy to upgrade. If you're running Linux PC or Mac OS X, you may not see the menu but the R Console. You can read this R Installation and Administration (pdf ) for detailed information. You can also type help("INSTALL") or help("install.packages") in R Console for information on how to install packages from this directory. Don't worry about this. You only need to do this once. After these installation, you now can run stab by simply typing "library(stab) (enter)" under R Console.
Remove stab for R: Simply go to the directory of R where you installed and then go to the sub-directory of /library, delete stab.
Methods:: This package includes two steps. In the first step, Decision Tree for Data Evaluation follows "Appendix A" of ICH guideline "Q1E Evaluation for Stability Data" that assists users evaluating stability data and guide users to consider doing an extrapolation for a proposed retest period or shelf life.
In the second step, Statistical Approaches to Stability Data Analysis is conducted for two different situations. First one is for a single batch. This approach estimates the retest period or shelf life for a single batch of drug product. The relationship between residuals and time is assumed to be linear. Two-sided 95 % confidence intervals of the regression line for residuals (% relative to the original amount) of a drug product intersect with upper and lower acceptance criteria of label claimed. Then, the shortest one is the shelf life. When there are multiple batches (e.g. 3 batched) available, analysis of covariance (ANCOVA) is first employed to test the difference in slopes and intercepts of the regression lines with different factors (packages, dosage forms, etc.). Then, based on the statistical results, there can be three possibilities. (1). slope (P>=0.25) and intercept (P>=0.25): the tests for equality of slopes and equality of intercepts are all no differences. The data from all batches then should be combined. Then, a single retest period or shelf life is estimated from the combined data. (2). slope (P>=0.25) and intercept (P<0.25): the test rejects the hypothesis of equality of intercepts but fails to reject the hypothesis with that all slopes are equal. The data should be combined to estimate the common slope. The retest periods or shelf lives for individual batches can be estimated. Then, the shortest estimate among batches should be chosen as the shelf life for all batches. (3). [slope (P<0.25) and intercept (P>=0.25)] or [slope (P<0.25) and intercept (P<0.25)]: the result in this scenario shows that the test rejects the hypothesis of equality of all slopes. It is not appropriate to combine the data from all batches in this situation. The retest periods or shelf lives for individual batches is estimated. Then, the shortest estimate among batches should be chosen as the shelf life for all batches. Please note the "batch" should be numerical ONLY (e.g., 1, 2, 3...) as the following example; otherwise, it may cause error.
Cross Validation with FDA provided SAS Stability programs: Please check with this .pdf file and use bookmarks to browse this pdf file. Please note that in SAS output, the lines for the upper and lower ranges were drawn between 90% and 110% (set as default). We did not change that with its SAS code (should be the one called stabgraf.sas), though we used different ranges; however, they would not affect the final results. They were just like marks over there.
