Example of Predict for Stability Study with a fixed batch factor

A quality engineer for a pharmaceutical company wants to determine the shelf life for pills that contain a new drug. The concentration of the drug in the pills decreases over time. The engineer wants to determine when the pills get to 90% of the intended concentration. Because this is a new drug, the company has only 5 pilot batches to use to estimate the shelf life. The engineer tests one pill from each batch at nine different times.

In the example of a stability study with a fixed batch factor, the engineer determined that the shelf life for the pills was 54.79 months. For this analysis, the shelf life is the time when the 95% confidence limit for the mean concentration crosses the lower specification limit. The engineer wants to predict the mean concentration for the best batch and the worst batch at 54.79 months.

  1. Open the sample data, ShelfLife_model.MTW.
  2. Choose Stat > Regression > Stability Study > Predict.
  3. In Response, select Drug%.
  4. In the second drop-down list, select Enter individual values.
  5. In the variables table, enter the setting for each variable.
    Month Batch
    54.79 1
    54.79 2
  6. Click OK.

Interpret the results

The predicted concentration for Batch 1 is 94.87%. The predicted concentration for Batch 2 is 91.36%. The XX next to each row indicates that the original data do not include the variable setting that you want to predict. The oldest samples in the stability study are 48 months old. Only further testing with older samples can confirm that the shelf life estimate is accurate.

Prediction for Drug%

Regression Equation Batch 1 Drug% = 99.853 - 0.090918 Month 2 Drug% = 100.15 - 0.16047 Month
Settings Variable Setting Month 54.79 Batch 1 Prediction Fit SE Fit 95% CI 95% PI 94.8716 0.801867 (93.2340, 96.5092) (92.8324, 96.9108) XX XX denotes an extremely unusual point relative to predictor levels used to fit the model.
Settings Variable Setting Month 54.79 Batch 2 Prediction Fit SE Fit 95% CI 95% PI 91.3609 0.801867 (89.7233, 92.9986) (89.3217, 93.4001) XX XX denotes an extremely unusual point relative to predictor levels used to fit the model.
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