Perspective on Use of Statistical Tools in Pharmaceutical Manufacturing - PowerPoint PPT Presentation


Title: Perspective on Use of Statistical Tools in Pharmaceutical Manufacturing


1
Perspective on Use of Statistical Tools in
Pharmaceutical Manufacturing
  • Karthik Iyer (CQE, CSSBB)
  • Senior Policy Advisor
  • CDER/OC/OMPQ
  • March 9th, 2012
  • AOAC Conference

This presentation reflects the views of the
author and should not be construed to represent
FDAs views or policies.
2
Agenda
  • Enforcement Action Examples
  • CGMP References
  • ASTM Standards
  • Conclusions

3
Recent warning letters and other compliance issues
  • Examples involving
  • Incorrect application of sampling plans
  • Equipment changes and process capability
  • Container closure - determining baseline defect
    rates
  • Recall example application of ASTM E2709

4
1. Warning Letter sampling plans
  • Firm using sampling plans incorrectly
  • Pooled X vials, used only 1 reportable value, but
    used nX in sampling plan.
  • .based your lot or batch acceptance/rejection
    criteria on a single reportable value averaged
    from a pooled sample.
  • For .., you are collecting 3 pooled samples
    (each pool 10 vials). This equates to a lot
    disposition action on 3 reportable values with
    corresponding AQL of X and LQ of X
    respectively. This is not equivalent to an X or X
    plan as claimed in your SOP.

5
1. Warning Letter sampling plans
  • Response to 483 indicated firm did not know how
    to use and interpret sampling plans correctly.
  • Firm did not understand concepts of Acceptable
    Quality Level (AQL) and Limiting Quality (LQ) and
    Operating Characteristic Curve (OC) of a specific
    sampling plan.

6
2. Warning Letter equipment comparability and
process capability
  • Four (4) tablet products, various strengths
  • Initial process qualification used a single-sided
    tablet press. During routine production,
    however, these products were also being
    manufactured using a double-sided tablet press.
  • Compression using the double sided press was not
    qualified.
  • Firms response to the FDA 483 attempted to show
    statistical equivalence between the single and
    double sides presses.

7
2. Warning Letter equipment comparability and
process capability
  • The firms written response referenced the Cpk
    values for processes using a double-sided tablet
    press and the single-sided tablet press.
  • FDA evaluation of the FDA 483 response
  • The Cpk value alone was not an appropriate metric
    to demonstrate statistical equivalence. Cpk
    analysis requires a normal underlying
    distribution and a demonstrated state of
    statistical process control. The firm did not
    address these issues in their response.
  • Statistical equivalence between the two presses
    could have been shown by using either parametric
    or non-parametric (based on distribution
    analysis) approaches and comparing means and
    variances. Neither of these approaches was
    employed. Firm did not use the proper analysis
    to support their conclusion that no significant
    differences existed between the two compression
    processes.

8
2. Warning Letter equipment comparability and
process capability
  • Issues
  • Data did not support proper statistical
    conclusions.
  • Firm did not understand underlying assumptions
    required to conduct Process Capability
    calculations.
  • Firm did not conduct proper statistical analysis
    to demonstrate equivalence between two
    operations.

9
3. Warning Letter container closure quality and
baseline defect rates
  • Product LVP in a dual chamber bag
  • Numerous complaints of leaks, bursts, and
    premature activation during 2 ½ year period.
  • Root cause - variability in the film thickness
    that influenced the sealing of the bags. Bags
    have two seals and their strength (or weakness)
    relative to each other led to different failure
    modes.
  • Critical defects compromising sterility and
    stability.
  • Poor history for the supplier of this container
    closure system.
  • Incoming acceptance activities, as well as
    in-process and finished product release
    activities, were found inadequate.

10
3. Warning Letter container closure quality and
baseline defect rates
  • Issues
  • In responding to the 483, the firm equated
    customer complaints to true manufacturing defect
    rate. They did not understand that market
    incident data may not track with the quality of
    the product prior to release.
  • The proposed sampling plans to identify these
    known potential defects were not based on
    appropriate statistics.
  • Firm did not understand sampling plan used for
    lot release.
  • Firm could not justify using a riskier sampling
    plan (higher probability of accepting bad
    product).

11
Application of ASTM E2709 - Standard Practice for
Demonstrating Capability to Comply with an
Acceptance Procedure. Tablets, Q value
70 Background Firm was having recall issues
due to dissolution failures on stability.
Dissolution data was analyzed using ASTM 2709.
Sample data below shown for 2 lots (Each row is a
different lot). If evaluated correctly, these
lots would have been flagged as high risk for
failure.
Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10 Unit 11 Unit 12 Mean SD RSD USP - PASS or FAIL ASTM E2709 Probability _at_ 95 confidence
96 72 82 74 102 70 97 63 71 78 74 60 78 14 17 Pass 0.14
77 73 90 95 92 59 73 94 60 72 62 85 78 13 17 Pass 0.14
12
CGMP References
  • 211.110(a) (b)
  • 211.165(d)
  • 211.180(e)
  • Preamble for 21 CFR 210, 211

13
Key elements in these requirements
  • Control procedures
  • Monitor the output
  • Performance
  • Variability in the characteristics of in-process
    material and the drug product
  • .derived from previous acceptable process
    average and process variability estimates (where
    possible)
  • .determined by...suitable statistical procedures
    (where appropriate)

14
Statistics
  • Can sample tablets at any stage of process and
    analyze for
  • Weight.
  • Content Uniformity.
  • Dissolution.
  • Other critical quality attributes and or
    parameters of interest.
  • Can make decisions at any stage of process with
    respect to
  • Ability for a lot to pass USP UDU and or
    Dissolution tests in the future. (ASTM E2709)
  • Confidence in sampling. (ASTM E2334 ASTM E122)
  • Capability and Performance analysis. (ASTM E2281)
  • Statistical Process Control Charts. (Monitor
    Variation, ASTM E2587)
  • Following tools illustrate making inferences
    about untested units on a particular attribute,
    variable and or parameter with respect to sample
    size and an associated confidence.

15
Voluntary Consensus StandardsUS Government
Agencies
  • OMB Circular A119
  • Federal Participation in the Development and Use
    of Voluntary Consensus Standards and in
    Conformity Assessment Activities (Rev. Feb 10,
    1998)
  • directs agencies to use voluntary consensus
    standards in lieu of government-unique standards
    except where inconsistent with law or otherwise
    impractical
  • intended to reduce to a minimum the reliance by
    agencies on government-unique standards.
  • http//www.whitehouse.gov/omb/circulars/a119/a119.
    html

16
ASTM E2709Standard Practice for Demonstrating
Capability to Comply with an Acceptance
ProcedureOne tool to analyzeUniformity of
DosageUnits
17
ASTM E2709 ExplanationStandard Practice for
Demonstrating Capabilityto Comply with an
Acceptance Procedure
  • Slide shows the relationship between sample size
    and tolerance for variability. As sample size
    increases, so does the tolerance for variability.
  • The analysis was performed using ASTM E2709-10.
    The RSD limits on the y-axis represent the
    maximum variability a lot can possess to ensure
    with 95 or 99 confidence that there is at least
    a 95 or 99 probability a lot will comply with
    the USP Uniformity of Dosage Units test based
    upon a given sample size, confidence level, and
    sample mean.
  • For example If you sampled 30 units and had a
    sample mean of 95, then the maximum RSD value
    for those 30 units would be 3.0 to be 95
    confident that there is at least a 95
    probability a future sample from the lot would
    pass the USP UDU test.

18
ASTM E2334Setting an Upper Confidence Bound For
a Fraction orNumber of Non-Conforming items, or
a Rate of Occurrencefor Non-conformities, Using
Attribute Data, When There is aZero Response in
the Sample
19
ASTM E2334 ExplanationSetting an Upper
Confidence Bound For a Fraction orNumber of
Non-Conforming items, or a Rate of Occurrencefor
Non-conformities, Using Attribute Data, When
There is aZero Response in the Sample
  • Slide shows the relationship between Confidence
    and Sample Size. As sample size increases, so
    does confidence demonstrated.
  • The analysis was performed using ASTM E2334-09.
    Keeping the maximum percent defective constant
    (1, 0.5, and 0.065) a line was generated to show
    how sample size effects the confidence
    demonstrated in having no more than the maximum
    percent defective. A zero response was assumed
    (that is zero defects in the sample) and a
    binomial distribution was used.
  • For example If you desire a percent defective
    of no more than 0.5 and sample 30 units, then
    you are only 15 confident that your lot has no
    more than 0.5 defects.

20
ASTM E2334Setting an Upper Confidence Bound For
a Fraction orNumber of Non-Conforming items, or
a Rate of Occurrencefor Non-conformities, Using
Attribute Data, When There is aZero Response in
the Sample
21
ASTM E2334 Explanation Setting an Upper
Confidence Bound For a Fraction orNumber of
Non-Conforming items, or a Rate of Occurrencefor
Non-conformities, Using Attribute Data, When
There is aZero Response in the Sample
  • Slide shows the relationship between the upper
    confidence bound on percent defects and sample
    size. As sample size increases the upper
    confidence bound on percent defects decreases.
  • The analysis was performed using ASTM E2334-09.
    Keeping the confidence level constant (95 and
    99) a line was generated to show how sample size
    effects the upper confidence bound percent
    defects. A zero response was assumed (that is
    zero defects in the sample) and a binomial
    distribution was used.
  • For example If you want to be 99 confident
    that there is no more than 1 defective units in
    your lot, then you must sample 460 units with a
    zero response.

22
ASTM E122Standard Practice forCalculating
Sample Size to Estimate, With SpecifiedPrecision,
the Average for a Characteristic of a Lot
orProcess
23
ASTM E122 explanation Standard Practice
forCalculating Sample Size to Estimate, With
SpecifiedPrecision, the Average for a
Characteristic of a Lot orProcess
  • Slide shows the relationship between sample size
    and precision. As sample size increases, so does
    your estimate precision.
  • The analysis was done using ASTM E122-09. Lines
    were generated using different sample sizes to
    show the effect it has on your estimate
    precision.
  • For example If you sampled 30 units and your
    sigma value was 6, then your sample average is
    /-3.5 of your true population average.

24
ASTM E2281Standard Practice forProcess and
Measurement Capability Indices
25
ASTM E2281 ExplanationStandard Practice
forProcess and Measurement Capability Indices
  • Slide shows the relationship between a reported
    Process Capability Index (Cpk (3.14)) and sample
    size. As sample size increases, so does the
    reported Cpk.
  • When reporting a Cpk, a lower 95 or 99
    confidence bound should always be the value
    reported. As this value accounts for the sample
    size in which the Cpk was estimated.
  • For example If you sampled 30 units and
    estimated a Cpk of 3.14, then the value reported
    should be 2.5 (that is I am 99 confident that
    the Cpk for my process is at least 2.5). The
    analysis was done using ASTM E2281-08.

26
ASTM E2281Standard Practice forProcess and
Measurement Capability Indices
27
ASTM E2281 ExplanationStandard Practice
forProcess and Measurement Capability Indices
  • Slide shows the relationship between a reported
    Process Performance Index (Ppk (2.79)) and sample
    size. As sample size increases, so does the
    reported Ppk.
  • When reporting a Ppk, a lower 95 or 99
    confidence bound should always be the value
    reported. As this value accounts for the sample
    size in which the Ppk was estimated.
  • For example If you sampled 30 units and
    estimated a Ppk of 2.79, then the value reported
    should be 2.2 (that is I am 99 confident that
    the Ppk for my process is at least 2.2). The
    analysis was done using ASTM E2281-08.

28
ASTM E2587Standard Practice for Use of Control
Charts in Statistical Process Control
  • SPC (Statistical Process Control) Charts are a
    collection of very effective statistical-graphical
    tools which can be used to
  • Understand and diagnose your data.
  • Track performance to identify problems, or shifts
    in performance (good or bad).
  • Control or adjust the process to maintain desired
    performance.
  • Can be applied for data based on Incoming,
    In-process, or Lot release samples.

29
ASTM E2587 Standard Practice for Use of Control
Charts in Statistical Process ControlVariable
X-bar-R chart
30
ASTM E2587Standard Practice for Use of Control
Charts in Statistical Process Control
  • Chart is used to detect special causes of
    variation during manufacturing.
  • Control is determined against standard 8 rules
    established by Dr. Walter Shewhart.
  • Preceding chart is called X-bar-Range with
    Subgroup size of 5 tablets (each point is an
    average of 5 individual results).
  • Control limits reveal true variability of the
    process.

31
ASTM E2587 Standard Practice for Use of Control
Charts in Statistical Process ControlAttribute
nP chart
32
Conclusions
  • What have we covered?
  • Enforcement action examples and CGMP references.
  • Use of statistics to quantify relationship
    between confidence associated with attribute,
    variable and or parameter of interest with
    respect to the sample size collected.
  • To make inferences on untested units.
  • Can be applied to In-coming, In-process, or
    Finished samples.
  • Can be used for real time manufacturing and or
    annual/periodic product reviews.

33
Conclusions
  • Other Statistical Tools
  • Sampling plans
  • Do they describe Consumers Risk?
  • How are true defect rates calculated to use a
    particular sampling plan?
  • Confidence, Prediction, and Tolerance Intervals
  • Is the correct statistical tool being applied for
    the right analysis?
  • Do the tools help answer questions about product
    quality and process performance?

34
Conclusions
  • The specific statistical tools and analysis
    depends on what variables, attributes, parameters
    are being used to monitor process performance and
    product quality.
  • The preceding example is just one set of
    statistical methods available to monitor process
    performance and product quality.
  • Statistics is a tool to elicit information to
    confirm that a specific manufacturing process is
    producing quality product.

35
Acknowledgements
  • Alex Viehmann CDER/OPS
  • Grace McNally CDER/OC
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Perspective on Use of Statistical Tools in Pharmaceutical Manufacturing

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Title: Perspective on Use of Statistical Tools in Pharmaceutical Manufacturing


1
Perspective on Use of Statistical Tools in
Pharmaceutical Manufacturing
  • Karthik Iyer (CQE, CSSBB)
  • Senior Policy Advisor
  • CDER/OC/OMPQ
  • March 9th, 2012
  • AOAC Conference

This presentation reflects the views of the
author and should not be construed to represent
FDAs views or policies.
2
Agenda
  • Enforcement Action Examples
  • CGMP References
  • ASTM Standards
  • Conclusions

3
Recent warning letters and other compliance issues
  • Examples involving
  • Incorrect application of sampling plans
  • Equipment changes and process capability
  • Container closure - determining baseline defect
    rates
  • Recall example application of ASTM E2709

4
1. Warning Letter sampling plans
  • Firm using sampling plans incorrectly
  • Pooled X vials, used only 1 reportable value, but
    used nX in sampling plan.
  • .based your lot or batch acceptance/rejection
    criteria on a single reportable value averaged
    from a pooled sample.
  • For .., you are collecting 3 pooled samples
    (each pool 10 vials). This equates to a lot
    disposition action on 3 reportable values with
    corresponding AQL of X and LQ of X
    respectively. This is not equivalent to an X or X
    plan as claimed in your SOP.

5
1. Warning Letter sampling plans
  • Response to 483 indicated firm did not know how
    to use and interpret sampling plans correctly.
  • Firm did not understand concepts of Acceptable
    Quality Level (AQL) and Limiting Quality (LQ) and
    Operating Characteristic Curve (OC) of a specific
    sampling plan.

6
2. Warning Letter equipment comparability and
process capability
  • Four (4) tablet products, various strengths
  • Initial process qualification used a single-sided
    tablet press. During routine production,
    however, these products were also being
    manufactured using a double-sided tablet press.
  • Compression using the double sided press was not
    qualified.
  • Firms response to the FDA 483 attempted to show
    statistical equivalence between the single and
    double sides presses.

7
2. Warning Letter equipment comparability and
process capability
  • The firms written response referenced the Cpk
    values for processes using a double-sided tablet
    press and the single-sided tablet press.
  • FDA evaluation of the FDA 483 response
  • The Cpk value alone was not an appropriate metric
    to demonstrate statistical equivalence. Cpk
    analysis requires a normal underlying
    distribution and a demonstrated state of
    statistical process control. The firm did not
    address these issues in their response.
  • Statistical equivalence between the two presses
    could have been shown by using either parametric
    or non-parametric (based on distribution
    analysis) approaches and comparing means and
    variances. Neither of these approaches was
    employed. Firm did not use the proper analysis
    to support their conclusion that no significant
    differences existed between the two compression
    processes.

8
2. Warning Letter equipment comparability and
process capability
  • Issues
  • Data did not support proper statistical
    conclusions.
  • Firm did not understand underlying assumptions
    required to conduct Process Capability
    calculations.
  • Firm did not conduct proper statistical analysis
    to demonstrate equivalence between two
    operations.

9
3. Warning Letter container closure quality and
baseline defect rates
  • Product LVP in a dual chamber bag
  • Numerous complaints of leaks, bursts, and
    premature activation during 2 ½ year period.
  • Root cause - variability in the film thickness
    that influenced the sealing of the bags. Bags
    have two seals and their strength (or weakness)
    relative to each other led to different failure
    modes.
  • Critical defects compromising sterility and
    stability.
  • Poor history for the supplier of this container
    closure system.
  • Incoming acceptance activities, as well as
    in-process and finished product release
    activities, were found inadequate.

10
3. Warning Letter container closure quality and
baseline defect rates
  • Issues
  • In responding to the 483, the firm equated
    customer complaints to true manufacturing defect
    rate. They did not understand that market
    incident data may not track with the quality of
    the product prior to release.
  • The proposed sampling plans to identify these
    known potential defects were not based on
    appropriate statistics.
  • Firm did not understand sampling plan used for
    lot release.
  • Firm could not justify using a riskier sampling
    plan (higher probability of accepting bad
    product).

11
Application of ASTM E2709 - Standard Practice for
Demonstrating Capability to Comply with an
Acceptance Procedure. Tablets, Q value
70 Background Firm was having recall issues
due to dissolution failures on stability.
Dissolution data was analyzed using ASTM 2709.
Sample data below shown for 2 lots (Each row is a
different lot). If evaluated correctly, these
lots would have been flagged as high risk for
failure.
Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Unit 7 Unit 8 Unit 9 Unit 10 Unit 11 Unit 12 Mean SD RSD USP - PASS or FAIL ASTM E2709 Probability _at_ 95 confidence
96 72 82 74 102 70 97 63 71 78 74 60 78 14 17 Pass 0.14
77 73 90 95 92 59 73 94 60 72 62 85 78 13 17 Pass 0.14
12
CGMP References
  • 211.110(a) (b)
  • 211.165(d)
  • 211.180(e)
  • Preamble for 21 CFR 210, 211

13
Key elements in these requirements
  • Control procedures
  • Monitor the output
  • Performance
  • Variability in the characteristics of in-process
    material and the drug product
  • .derived from previous acceptable process
    average and process variability estimates (where
    possible)
  • .determined by...suitable statistical procedures
    (where appropriate)

14
Statistics
  • Can sample tablets at any stage of process and
    analyze for
  • Weight.
  • Content Uniformity.
  • Dissolution.
  • Other critical quality attributes and or
    parameters of interest.
  • Can make decisions at any stage of process with
    respect to
  • Ability for a lot to pass USP UDU and or
    Dissolution tests in the future. (ASTM E2709)
  • Confidence in sampling. (ASTM E2334 ASTM E122)
  • Capability and Performance analysis. (ASTM E2281)
  • Statistical Process Control Charts. (Monitor
    Variation, ASTM E2587)
  • Following tools illustrate making inferences
    about untested units on a particular attribute,
    variable and or parameter with respect to sample
    size and an associated confidence.

15
Voluntary Consensus StandardsUS Government
Agencies
  • OMB Circular A119
  • Federal Participation in the Development and Use
    of Voluntary Consensus Standards and in
    Conformity Assessment Activities (Rev. Feb 10,
    1998)
  • directs agencies to use voluntary consensus
    standards in lieu of government-unique standards
    except where inconsistent with law or otherwise
    impractical
  • intended to reduce to a minimum the reliance by
    agencies on government-unique standards.
  • http//www.whitehouse.gov/omb/circulars/a119/a119.
    html

16
ASTM E2709Standard Practice for Demonstrating
Capability to Comply with an Acceptance
ProcedureOne tool to analyzeUniformity of
DosageUnits
17
ASTM E2709 ExplanationStandard Practice for
Demonstrating Capabilityto Comply with an
Acceptance Procedure
  • Slide shows the relationship between sample size
    and tolerance for variability. As sample size
    increases, so does the tolerance for variability.
  • The analysis was performed using ASTM E2709-10.
    The RSD limits on the y-axis represent the
    maximum variability a lot can possess to ensure
    with 95 or 99 confidence that there is at least
    a 95 or 99 probability a lot will comply with
    the USP Uniformity of Dosage Units test based
    upon a given sample size, confidence level, and
    sample mean.
  • For example If you sampled 30 units and had a
    sample mean of 95, then the maximum RSD value
    for those 30 units would be 3.0 to be 95
    confident that there is at least a 95
    probability a future sample from the lot would
    pass the USP UDU test.

18
ASTM E2334Setting an Upper Confidence Bound For
a Fraction orNumber of Non-Conforming items, or
a Rate of Occurrencefor Non-conformities, Using
Attribute Data, When There is aZero Response in
the Sample
19
ASTM E2334 ExplanationSetting an Upper
Confidence Bound For a Fraction orNumber of
Non-Conforming items, or a Rate of Occurrencefor
Non-conformities, Using Attribute Data, When
There is aZero Response in the Sample
  • Slide shows the relationship between Confidence
    and Sample Size. As sample size increases, so
    does confidence demonstrated.
  • The analysis was performed using ASTM E2334-09.
    Keeping the maximum percent defective constant
    (1, 0.5, and 0.065) a line was generated to show
    how sample size effects the confidence
    demonstrated in having no more than the maximum
    percent defective. A zero response was assumed
    (that is zero defects in the sample) and a
    binomial distribution was used.
  • For example If you desire a percent defective
    of no more than 0.5 and sample 30 units, then
    you are only 15 confident that your lot has no
    more than 0.5 defects.

20
ASTM E2334Setting an Upper Confidence Bound For
a Fraction orNumber of Non-Conforming items, or
a Rate of Occurrencefor Non-conformities, Using
Attribute Data, When There is aZero Response in
the Sample
21
ASTM E2334 Explanation Setting an Upper
Confidence Bound For a Fraction orNumber of
Non-Conforming items, or a Rate of Occurrencefor
Non-conformities, Using Attribute Data, When
There is aZero Response in the Sample
  • Slide shows the relationship between the upper
    confidence bound on percent defects and sample
    size. As sample size increases the upper
    confidence bound on percent defects decreases.
  • The analysis was performed using ASTM E2334-09.
    Keeping the confidence level constant (95 and
    99) a line was generated to show how sample size
    effects the upper confidence bound percent
    defects. A zero response was assumed (that is
    zero defects in the sample) and a binomial
    distribution was used.
  • For example If you want to be 99 confident
    that there is no more than 1 defective units in
    your lot, then you must sample 460 units with a
    zero response.

22
ASTM E122Standard Practice forCalculating
Sample Size to Estimate, With SpecifiedPrecision,
the Average for a Characteristic of a Lot
orProcess
23
ASTM E122 explanation Standard Practice
forCalculating Sample Size to Estimate, With
SpecifiedPrecision, the Average for a
Characteristic of a Lot orProcess
  • Slide shows the relationship between sample size
    and precision. As sample size increases, so does
    your estimate precision.
  • The analysis was done using ASTM E122-09. Lines
    were generated using different sample sizes to
    show the effect it has on your estimate
    precision.
  • For example If you sampled 30 units and your
    sigma value was 6, then your sample average is
    /-3.5 of your true population average.

24
ASTM E2281Standard Practice forProcess and
Measurement Capability Indices
25
ASTM E2281 ExplanationStandard Practice
forProcess and Measurement Capability Indices
  • Slide shows the relationship between a reported
    Process Capability Index (Cpk (3.14)) and sample
    size. As sample size increases, so does the
    reported Cpk.
  • When reporting a Cpk, a lower 95 or 99
    confidence bound should always be the value
    reported. As this value accounts for the sample
    size in which the Cpk was estimated.
  • For example If you sampled 30 units and
    estimated a Cpk of 3.14, then the value reported
    should be 2.5 (that is I am 99 confident that
    the Cpk for my process is at least 2.5). The
    analysis was done using ASTM E2281-08.

26
ASTM E2281Standard Practice forProcess and
Measurement Capability Indices
27
ASTM E2281 ExplanationStandard Practice
forProcess and Measurement Capability Indices
  • Slide shows the relationship between a reported
    Process Performance Index (Ppk (2.79)) and sample
    size. As sample size increases, so does the
    reported Ppk.
  • When reporting a Ppk, a lower 95 or 99
    confidence bound should always be the value
    reported. As this value accounts for the sample
    size in which the Ppk was estimated.
  • For example If you sampled 30 units and
    estimated a Ppk of 2.79, then the value reported
    should be 2.2 (that is I am 99 confident that
    the Ppk for my process is at least 2.2). The
    analysis was done using ASTM E2281-08.

28
ASTM E2587Standard Practice for Use of Control
Charts in Statistical Process Control
  • SPC (Statistical Process Control) Charts are a
    collection of very effective statistical-graphical
    tools which can be used to
  • Understand and diagnose your data.
  • Track performance to identify problems, or shifts
    in performance (good or bad).
  • Control or adjust the process to maintain desired
    performance.
  • Can be applied for data based on Incoming,
    In-process, or Lot release samples.

29
ASTM E2587 Standard Practice for Use of Control
Charts in Statistical Process ControlVariable
X-bar-R chart
30
ASTM E2587Standard Practice for Use of Control
Charts in Statistical Process Control
  • Chart is used to detect special causes of
    variation during manufacturing.
  • Control is determined against standard 8 rules
    established by Dr. Walter Shewhart.
  • Preceding chart is called X-bar-Range with
    Subgroup size of 5 tablets (each point is an
    average of 5 individual results).
  • Control limits reveal true variability of the
    process.

31
ASTM E2587 Standard Practice for Use of Control
Charts in Statistical Process ControlAttribute
nP chart
32
Conclusions
  • What have we covered?
  • Enforcement action examples and CGMP references.
  • Use of statistics to quantify relationship
    between confidence associated with attribute,
    variable and or parameter of interest with
    respect to the sample size collected.
  • To make inferences on untested units.
  • Can be applied to In-coming, In-process, or
    Finished samples.
  • Can be used for real time manufacturing and or
    annual/periodic product reviews.

33
Conclusions
  • Other Statistical Tools
  • Sampling plans
  • Do they describe Consumers Risk?
  • How are true defect rates calculated to use a
    particular sampling plan?
  • Confidence, Prediction, and Tolerance Intervals
  • Is the correct statistical tool being applied for
    the right analysis?
  • Do the tools help answer questions about product
    quality and process performance?

34
Conclusions
  • The specific statistical tools and analysis
    depends on what variables, attributes, parameters
    are being used to monitor process performance and
    product quality.
  • The preceding example is just one set of
    statistical methods available to monitor process
    performance and product quality.
  • Statistics is a tool to elicit information to
    confirm that a specific manufacturing process is
    producing quality product.

35
Acknowledgements
  • Alex Viehmann CDER/OPS
  • Grace McNally CDER/OC
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