These tests make a comparison between two models, simple (reduced) vs. These values can be obtained by regression analysis using dummy dependent variables containing ones and zeros.ĪNCOVA calculations for comparing the intercepts and slopes of stability batches were calculated as described (4) and are shown in Table I. This model indicates that different batches have common slope and separate intercept. Separate intercept common slope (SICS) model. The expected value is calculated using slope and intercept from individual batches. Residual sum of squares is obtained by adding squared differences between observed and expected value. These values can be obtained by regression analysis from individual batches separately. This model indicates that different batches have different slopes and different intercepts. Separate intercept separate slope (SISS) model. The values for common slope and intercept for dataset 1 are obtained by using regression analysis in Excel. This model indicates that all the batches have a common intercept and slope. The other models used to describe stability studies are listed below:Ĭommon intercept common slope (CICS) model. The common intercept separate slope (CISS) model is used only in special circumstances (5). For multi-batch stability data, Figure 2 shows the possible scenarios for drug product degradation. The procedure involved regression analysis of stability data using in-built Excel functions and then creating an ANCOVA table using these values. Two stability data datasets each involving six batches from Ruberg and Stegeman’s research (1) were tested separately using the aforementioned approach. Two reported datasets each containing data from six batches will be used to demonstrate this approach. The goal of this paper is to test for equality of slope and intercepts using Microsoft Excel, when the number of test batches is greater than three. In a previous paper (4), the use of Microsoft Excel to test the equality of slopes and intercepts using ANCOVA for three batches was described. A significance level of 0.25 is used as the criterion for pooling (3). According to ICH’s guideline (2), analysis of covariance (ANCOVA) is used to test statistical difference between both slopes and intercepts of regression lines generated from these multiple batches, using time as a covariate. When multiple batches are manufactured, data from these batches can be pooled for shelf-life calculation. The objective of statistical analysis outlined in the guideline is to determine variability among these batches and then estimate the shelf life accordingly (2). The International Council for Harmonisation (ICH) Tripartite Guideline, Q1E Evaluation for Stability Data, describes the procedure for shelf-life estimation.

The shelf-life estimation for a hypothetical single batch is shown in Figure 1. The shelf life is the point in time where the lower 95% confidence interval from the linear fitted regression line crosses the 90% labeled amount (1). By using data from these studies, degradation rate as a function of time is calculated. These studies are conducted by taking random samples from a batch that is stored under specified conditions. Stability studies in the pharmaceutical industry are intended to measure the degradation of drug product over time. The spreadsheet is designed to meet the poolability criteria as per International Council for Harmonisation Harmonised Tripartite guideline for evaluation of stability data, Q1E. The goal of this paper is to create an Excel spreadsheet, which can be used for statistical testing of more than three stability batches for poolability. One of the main purposes of stability testing is to establish shelf life for these drug products. Stability studies are carried out to study the impact on the quality of drug substance or drug product over a period of time under the combined influence of environmental factors like temperature, humidity, and exposure to light.

Excel data analysis regression slope