Methods for Identifying Out-of-Trend Results in Ongoing Stability Data - Pharmaceutical Technology

Latest Issue

Latest Issue
PharmTech Europe

Methods for Identifying Out-of-Trend Results in Ongoing Stability Data
The authors discuss three methods for identification of out-of-trend (OOT) results and further compare the z-score method and the tolerance interval in OOT analysis for stability studies.

Pharmaceutical Technology
Volume 37, Issue 6, pp. 46-51,59

Figure 1: Least-square line method for the time period of 0–9 months. (ALL FIGURES COURTESY OF AUTHORS)
Regression-control-chart-method. The regression-control-chart method is used to compare the results within the batch and detect present OOT results. For the purpose of this method, the tenth batch was examined. Several least-square regression lines were fit to the suitable data (5). The first regression line was constructed from the three results for assay at the first three time points (0, 3, and 6 months). With extrapolation of that regression line, the expected values for Y and the Y residuals were calculated (see Figure 1) (6). The procedure was then repeated by gradually adding all the other consecutive time points.

Table I: Regression-control chart for the tenth batch.
The next step was to calculate the mean and standard deviation (σ) of the Y residuals of the regression line. As a result, a sum of five means and standard deviations corresponding to the time periods (0–6, 0–9, 0–12, 0–18, and 0–24 months) were constructed. To identify the present OOT result, the z-score test was used to calculate the z-value for each Y residual at each time point. The z-value is based on the means and standard deviations of the defined time periods (see Table I). The z-value was limited to -3 < z <+3, where 99.73% of the future results are expected to enter the interval within these limits.

Table II: The regression-control chart method limits from the tolerance interval (TI) and z-values for the corresponding TI values.
Testing the precision of the TI in comparison to the z-score test, the TI for the same five time periods was calculated according to the suitable equation with defined certainty (α=0.027) and confidence (γ=0.95) (7). To compare the two methods for each TI value, a corresponding z-value was calculated (see Table II).

Figure 2: Representation of the historical data with the use of the by-time-point method.
By-time-point method. The by-time-point method is used to determine whether a result is within expectations on the basis of experiences from other batches measured at the same stability time point. To minimize the α and β error (6), batches that comprise the historical data (Batches 1 to 9) were individually tested for present OOT results. In addition, calculations for the mean and standard deviation of the values for the tested attribute were made from the historical data for each time point individually (see Figure 2).

Table III: Mean value and standard deviation of the historical data and z-value for Batch 10.
To identify the present OOT result, the z-score test was used to calculate the z-value for each value of Y (the assay result) of the tenth batch. Analysis was made at each time point using the mean and the standard deviation from the historical data corresponding to the tested time point (see Table III). The value of z was again limited to -3 < z < +3.


blog comments powered by Disqus
LCGC E-mail Newsletters

Subscribe: Click to learn more about the newsletter
| Weekly
| Monthly
| Weekly

FDASIA was signed into law two years ago. Where has the most progress been made in implementation?
Reducing drug shortages
Breakthrough designations
Protecting the supply chain
Expedited reviews of drug submissions
More stakeholder involvement
Reducing drug shortages
Breakthrough designations
Protecting the supply chain
Expedited reviews of drug submissions
More stakeholder involvement
View Results
Eric Langerr Outsourcing Outlook Eric LangerRelationship-building at Top of Mind for Clients
Cynthia Challener, PhD Ingredients Insider Cynthia ChallenerRisk Reduction Top Driver for Biopharmaceutical Raw Material Development
Jill Wechsler Regulatory Watch Jill Wechsler Changes and Challenges for Generic Drugs
Faiz Kermaini Industry Insider Faiz KermainiNo Signs of a Slowdown in Mergers
Ebola Outbreak Raises Ethical Issues
Better Comms Means a Fitter Future for Pharma, Part 2: Realizing the Benefits of Unified Communications
Better Comms Means a Fitter Future for Pharma, Part 1: Challenges and Changes
Sandoz Wins Biosimilar Filing Race
NIH Translational Research Partnership Yields Promising Therapy
Source: Pharmaceutical Technology,
Click here