1. Control Charts for CPV – A Pharma Perspective
Maneesha Altekar, Principal Statistician, AstraZeneca
MBSW Conference, May 22-24, 2017, Muncie, IN

2. Overview Why use Control Charts? Type of Data vs Type of Control Chart
Calculating Limits
Responding to Signals - Western Electric Rules
Challenges

3. Overview Why use Control Charts? Type of Data vs Type of Control Chart
Calculating Limits
Responding to Signals - Western Electric Rules
Challenges

4. Background 2011 FDA Guidance on Process Validation, EU Annex 15
Demonstrate that the validated process continues to remain in a validated state – Continued Process verification (CPV)
Emphasis on the use of statistical methods

5. Control Charts Statistical analysis methodology, used for CPV
Visually monitor process over time against established limits
Ensure that it is stable and in statistical control
Exhibits only common cause variability
React to real changes in the process
Not over-react to minor changes that are part of routine variation

6. Control Charts Control Chart displaying only Common Cause Variation
Control Chart displaying Special Cause Variation

7. Requires adjustments to how we implement control charts
CPV Implementation
Put control of monitoring process in the hands of process owners, not statisticians – this is key!
Make it simple to execute and interpret
Facilitate decision making
Requires adjustments to how we implement control charts
So in the context of CPV implementation, Our goal is to . . .

8. Control Charts – assumptions
Process stable
Data are normally distributed (for X, X-bar, etc)
Data are identically and independently distributed
Monitored in real time
Not often met in pharmaceutical data!
And hence, the need to look at control charts with a different lens. Let’s look at various aspects of control charts and how we might treat them differently

9. Overview Why use Control Charts? Type of Data vs Type of Control Chart
Calculating Limits
Responding to Signals - Western Electric Rules
Challenges

10. Data Type vs Control Charts
Data can be continuous or discrete
Continuous – assay, dissolution, tablet weight
Discrete – number of defects, proportion defective
Not all control charts apply to all types of data
But sometimes, we may be able to get away with an “incorrect” chart
What kind of data do we see in the pharmaceutical industry?

11. Control Charts – Continuous Data
X-Bar and R charts (Normal dist)
Multiple measurements (reported values) per batch
Batch means, range,
Example, tablet weights, dissolution, CU
X and MR chart (Normal dist)
Single measurement (reported value) per batch
Example, water content, pH, assay

12. Control Charts – Discrete Data
P chart
Proportion of defective units (Binomial dist)
NP chart
Number of defective units (Binomial dist)
C chart
Number of defects (Poisson dist)
U chart
Number of defects per unit (Poisson dist)

13. Control Charts Month Defects Jan 12 1 May 13 2 Feb 12 June 13 Mar 12
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14. Control Charts
Correct chart
Common mistake

15. Normality can sometimes be approximated
I Chart
Good Enough

16. Overview Why use Control Charts? Type of Data vs Type of Control Chart
Calculating Limits
Responding to Signals - Western Electric Rules
Challenges

17. Creating Control Charts Calculating Limits
Legacy products
Process history, historical data
Use most recent data – batches
Capture short term and long term variability
Trend data
Is it stable? If not, is there a root cause?
Should any data be excluded?
Some special cause variation is expected
Are there outliers? Should we exclude them?
We know there is nothing magical about 30, it could be 29 or even 25. But handing off this process to practitioners, it is helpful to provide a number, with the understanding that there can be some flexibility

18. Creating Control Charts Calculating Limits
Look at histogram
Are data normally distributed?
Are data approximately normally distributed?
Calculate limits based on historical mean and SD
Let’s look at some examples

19. Control Charts

20. Control Charts

21. Control Charts

22. Control Charts
Let’s now look at how we would view the process differently for a new product

23. Control Chart – Assumption of Normality
X-bar, Individual charts – assume normality
Can test for normality but . . .
Test is sensitive to number of samples
Too small => everything will pass normality test
Too large => even known normal data will fail normality if there is an outlier or two
Practical approach – assume normality for tests that are known to be normal, e.g., assay, CU, tablet weight
Look at histograms to check for extreme/unusual values
How about normality?

24. Control Chart – Assumption of Normality
If data truly non-normal
Consider transformation
Interpretation can be difficult
Use chart appropriate for underlying distribution, if known
Simply trend and track visually
For example, degradant products
Keep specification in mind

25. Creating Control Charts Calculating Limits
New products
No process history, limited data
Trend and track only – no limits
Monitor data visually
Calculate limits once 30 batches are available
Preliminary limits
Similar considerations as before but less rigorous
Update when additional data available

26. Control Charts – Calculating Limits
New products
Common mistake - monitoring current data with limits calculated based on current data!

27. Overview Why use Control Charts? Type of Data vs Type of Control Chart
Calculating Limits
Responding to Signals - Western Electric Rules
Challenges

28. Interpreting Control Charts Responding to Signals
Western Electric rules - Decision rules for detecting an out-of-control process
Look for patterns in data
A few key ones
A single result outside the +/- 3σ limits
2 out of 3 consecutive results outside the 2σ limits, on the same side of the mean
8 consecutive points on the same side of the mean
6 consecutive results increasing (or decreasing)
Use only 2 or 3 that are most meaningful to the process; using too many increases the risk of false positive signals.

29. Processes are rarely truly stable
Batch results are rarely independently and identically distributed
Many charts assume underlying normal distribution; data are not always normal

30. Limited ability to react in real time!
Batches are often not tested for days after manufacture
Many more batches produced in the interim
Batches tested in order different from manufacturing
Signals often observed only during periodic review
Limited ability to react in real time!
So we need to be smart about how we interpret signals. Let’s look at some examples.

31. React to this signal? Is this rule useful? React to this signal?
Typically include 1or 2 previous review periods during the current review. The answer is “it depends”.

32. Control Chart – Assumption of Independence
Batches often manufactured in campaigns
Example, based on lots of raw material
Testing often done in groups
Example, multiple batches may be simultaneously tested for assay in the HPLC
May see patterns in data related to above rather than true lack-of control

33. Control Chart – Assumption of Independence
By manufacturing date
By testing date

34. Control Chart – Assumption of Normality
X-bar, Individual charts – assume normality
Can test for normality but . . .
Test is sensitive to number of samples
Too small => everything will pass normality test
Too large => even known normal data will fail normality if there is an outlier or two
Practical approach – assume normality for tests that are known to be normal, e.g., assay, CU, tablet weight
Look at histograms to check for extreme/unusual values
We have already talked about normality

35. Control Charts – Response to Signals
Risk based approach
Signals should trigger a response
But, response should be commensurate with risk – to patient, process
So, now that we have established control charts with limits, have selected the rules we want to follow, how do we now respond to signals?

36. Control Charts

37. Control Charts – Response to Signals
All signals need not be classified as “deviations”
All signals should not lead to full scale investigations
Mindset change for QA organizations

38. Overview Why use Control Charts? Type of Data vs Type of Control Chart
Calculating Limits
Responding to Signals - Western Electric Rules
Challenges
There are challenges to implementing control charts in the pharma industry. But there are also challenges on a “people” level

39. Challenges Training provided to staff implementing CPV
Difficult to proceduralize a trained statistician’s thought process and insights
“Number of batches needed” taken too literally
Failure to appreciate limitation of reported (rounded) test results for analysis
Continued coaching / mentoring needed until proficient

40. Summary Control charts are an important tool for CPV and help us
monitor processes
understand variability
demonstrate that process remains stable and in statistical control
Control chart may need to be looked at differently for pharmaceutical manufacturing
Risk based assessment
Easing of assumptions
Flexibility in implementation