In the problem-solving mode, a retrospective change-point analysis is conducted on selected attributes to see if the timeframe
of a change in one of the attributes corresponds to the occurrence of a manufacturing problem. As a hypothetical example,
let's assume that there was an increase in out-of-range results for sodium concentration in a processing buffer. A change-point
analysis should be run on the sodium concentration of buffer lots over time. This allows us to determine if the out-of-range
batches are isolated instances or if there has been a shift in the sodium concentration of the buffer that is causing more
batches to be out-of-range. The analysis should encompass enough lots before the increase in out-of-range results to provide
a good representation of the variation in the data. If the change-point analysis showed that a change in the sodium concentration
occurred in the same timeframe as the onset of the out-of-range results, the investigation will focus on the cause of the
As part of the investigation, many potential causes may be identified, including changes in:
- mixing speed during buffer preparation
- mixing time
- operators preparing the buffer
- raw material from a vendor
- assay procedures or equipment
- assay reagents or standards.
Change-point analysis can be conducted on any of the time-ordered data such as mixing speed for each lot, mixing time for
each lot, or assay control values. If any of the results from a change-point analysis indicate a shift in the data that corresponds
to the sodium concentration shift, one can narrow the scope of the investigation. Alternatively, root causes can be eliminated
if a measured parameter did not change over the timeframe examined.
The hypothetical example described above shows how change-point analysis can be used to provide a retrospective analysis of
a data set to see if shifts in the data correspond to the onset of a manufacturing issue. In this context, change-point analysis
serves as a powerful problem-solving tool.
Control charts are commonly used to evaluate data on a lot-by-lot basis or sample-by-sample basis, and can indicate when a
particular lot or sample is not part of the same population of data as the data that was used to generate the control limits
(special cause variation). Furthermore, trend analysis can be done with control charts if the data is normally distributed.
Likewise, change-point analysis also can be used on a real-time basis to look for trends or shifts in a process. Change-point
analysis provides some advantages over control charts when looking for shifts in data.2 These advantages are indicated below.
1. Change-point analysis can detect more subtle shifts than control charts. Because biomanufacturing processes can have a
lot of variation, subtle changes may not always be meaningful. However, this determination must be made separately from the
determination of significant changes identified by change-point analysis.
2. Any type of data distribution can be analyzed. With control charts, a trend analysis requires a normally distributed data
3. Any type of data, including attributes, can be analyzed with the same change-point analysis tool. In contrast, a different
type of control chart is needed for each data type.
4. Change-point analysis can lead to fewer false positives than control charts.
5. Multiple changes in the mean and variation can be detected by a single change-point analysis using Change-Point Analyzer.
Because change-point analysis does not assume a normal distribution, it works particularly well with non-normal data such
as particle counts or bioburden data.
It is preferable to use change-point analysis in conjunction with control charts. One recommendation is to use a control chart
to determine whether there is special cause variation for each data point as it is generated. A change-point analysis is then
used on a less frequent basis to look for shifts in the data. The reason to use change-point analysis on a less frequent basis
is that changes are not always detected immediately after a shift in the data. It may take a few lots or data points after
a shift in the mean or variation before a change is recognized. The recommended frequency of running a change-point analysis
depends on how fast data points are generated. In the biologics or recombinant protein industry, where only a few lots may
be produced each week, a weekly or monthly change-point analysis is sufficient.