Presentation - Setting specifications, statistical considerations

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Transcript of Presentation - Setting specifications, statistical considerations

  • Setting SpecificationsStatistical considerations

    Enda Moran Senior Director, Development, PfizerMelvyn Perry Manager, Statistics, Pfizer

  • B i St ti tiBasic Statistics1 5Population distribution 1.5

    1.0

    True batch assay

    Distribution of

    p(usually unknown).Normal distribution described by and .

    0.5

    0.0

    possible values

    1009998

    We infer the population from samples by calculating and s.x

    1.5

    True batch assay

    1.5

    True batch assay

    1.5

    True batch assaySample 3

    1.0

    0.5

    y

    possible valuesDistribution of

    Sample 1 Average 98.6

    1.0

    0.5

    y

    possible valuesDistribution of

    Sample 2 Average 98.7 1.0

    0.5

    y

    possible valuesDistribution of

    Sample 3 Average 99.0

    210099980.0 10099980.0 10099980.0

  • I t lIntervals30

    28

    30

    24

    26

    22

    Populationaverage

    18

    20

    0 10 20 30 40 50 60 70 80 90 10016

    100 samples of size 5 taken from a population with an average of 23.0 and a standard deviation of 2.0.The highlighted intervals do not include the population average (there are 6 of them).For a 95% confidence level expect 5 in 100 intervals to NOT include the population average

    3For a 95% confidence level expect 5 in 100 intervals to NOT include the population average.

    Usually we calculate just one interval and then act as if the population mean falls within this interval.

  • I t lIntervalsPoint EstimationPoint Estimation

    The best estimate; eg MEAN

    Interval EstimationA range which contains the true population parameter or a future observation to a certain degree of confidence.

    Confidence Interval- The interval to estimate the true population parameter (e.g. the population

    mean)mean).

    Prediction IntervalPrediction Interval- The interval containing the next single response.

    Tolerance Interval- The interval which contains at least a given proportion of the population.

    4

  • F l f I t lFormulae for Intervals

    Intervals are defined as:

    A i l di t ib ti

    ksx

    Assuming a normal distribution Confidence (1-) interval

    50

    Prediction (1-) interval for m future observationsst

    nxCI

    n 1,2

    1

    5.01

    =

    Prediction (1 ) interval for m future observations

    stn

    xPIn

    m1,

    21

    5.011

    +=

    Tolerance interval for confidence (1-) that proportion ( ) i d

    m2

    (p) is covered( ) z

    nn p

    2)1(

    111

    +

    5s

    nxTI

    n2

    1,

    2

    =

  • P C bilitProcess Capability33 s3s3Process capability is a measure of the

    risk of failing specification. The spread of the data are compared with the width of th ifi ti

    The distance from the mean to the

    the specifications.

    The distance from the mean to the nearest specification relative to half the process width (3s).

    The index measures actual performance. Which may or may not be on target i.e., centred.

    xx LSLUSLUSLLSL x

    LSLx xUSL

    =

    sx

    sxPpk 3

    LSL,3

    USLmin

    6

  • P C bilit P k d C kProcess Capability Ppk and Cpk Ppk should be used as this is the actual risk of failing specification

    Random data of mean 10 and SD 1, thus natural span 7 to 13.Added shifts to simulate trends around common average

    Ppk should be used as this is the actual risk of failing specification. Cpk is the potential capability for the process when free of shifts and drifts.

    LSL USL

    Process Capability of Shifted

    151

    I Chart of Shifted

    Added shifts to simulate trends around common average.With specs at 7 and 13 process capability should be unity. When data is with trend Ppk

    less than Cpk due to method of calculation of std dev.

    LSL 7Target *USL 13Sample Mean 10.0917Sample N 30StDev (Within) 1.16561StDev (O v erall) 1.95585

    Process Data

    C p 0.86C PL 0.88C PU 0.83C pk 0.83

    Pp 0.51PPL 0.53PPU 0.50

    O v erall C apability

    Potential (Within) C apability

    WithinOverall14

    13

    12

    11

    10

    divi

    dual

    Val

    ue

    _X=10.092

    UCL=13.589

    1 calculation of std dev.

    Ppk uses sample SD. Ppk less than 1 at 0.5.

    C k i

    14121086

    Ppk 0.50C pm *

    PPM < LSL 0.00PPM > USL 100000.00PPM Total 100000.00

    O bserv ed PerformancePPM < LSL 3995.30PPM > USL 6296.59PPM Total 10291.88

    Exp. Within PerformancePPM < LSL 56966.34PPM > USL 68512.70PPM Total 125479.03

    Exp. O v erall Performance

    28252219161310741

    9

    8

    7

    6

    Observation

    Ind

    LCL=6.595

    Cpk uses average moving range SD (same as for control chart limits).Cpk is close to 1 at 0.83.

    13 UCL=13.038

    I Chart of Raw

    LSL USLProcess Data Within

    Process Capability of Raw

    p

    When data is without trend12

    11

    10

    9Indi

    vidu

    al V

    alue

    _X=10.092

    LSL 7Target *USL 13Sample Mean 10.0917Sample N 30StDev (Within) 0.982187StDev (O v erall) 1.14225

    C p 1.02C PL 1.05C PU 0.99C pk 0.99

    Pp 0.88PPL 0.90PPU 0.85Ppk 0.85C *

    O v erall C apability

    Potential (Within) C apability

    OverallWhen data is without trend Ppk is same as Cpk. Only small differences are seen.

    Cpk effectively 1 at 0.99.

    7282522191613107419

    8

    7

    Observation

    I

    LCL=7.14513121110987

    C pm *

    PPM < LSL 0.00PPM > USL 0.00PPM Total 0.00

    O bserv ed PerformancePPM < LSL 822.51PPM > USL 1533.13PPM Total 2355.65

    Exp. Within PerformancePPM < LSL 3397.73PPM > USL 5446.84PPM Total 8844.57

    Exp. O v erall Performance

    Cpk effectively 1 at 0.99.

    Ppk close to 1 at 0.85.

  • M t U t i tMeasurement Uncertainty

    Goodt

    LSL USL

    parts almost always passed

    measurement

    pBad parts

    almost always

    j t d

    Bad parts almost always

    total

    rejected always rejected

    3measurement 3measurement

    The grey areas highlighted represent those parts of the curve with the

    8potential for wrong decisions, or mis-classification.

  • Misclassification with less i blvariable process

    LSL USL

    measurement

    total

    measurement

    3measurement 3measurement3measurement measurement

    9

  • M t U t i tMeasurement Uncertainty

    Precision to Tolerance Ratio:

    ToleranceTP MS= 6/

    How much of the tolerance is taken up bymeasurement error.This estimate may be appropriate for evaluating LimitSpecUpper =

    =USL

    LSLUSLTolerance

    This estimate may be appropriate for evaluatinghow well the measurement system can performwith respect to specifications. Sys.t Measuremen of Dev. Std.

    Limit SpecLower ppp

    ==

    MSLSL

    Gage R&R (or GRR%) 100&% =Total

    MSRR

    What percent of the total variation is taken up bymeasurement error (as SD and thus not additive).

    Total

    Use Measurement Systems Analysis to assess if the assay method is fit for purpose. It is unwise to have a method where the specification interval is consumed by the

    10measurement variation alone.

  • Specification example T l I t lTolerance Interval

    16.5Data from three sites used to set specifications

    15.0

    15.5

    16.0

    16.5Data from three sites used to set specifications.

    Tolerance interval found from pooled data of 253 batches.

    13.5

    14.0

    14.5Tolerance interval chosen as 95% probability that mean 3 standard deviations are contained.

    13.01 2 3

    Code

    Sample size: n=253

    Mean 3s Toleranceinterval

    ( % / N k multiplier5 6.6010 4.4415 3 89

    Table of values for 95% probability of interval containing 99% of population values

    Mean = 14.77

    s=0.58

    (95% / 99.7%)

    R 13 03 16 51 12 89 16 65 15 3.8930 3.35 2.58

    population values.

    Note at value is 2.58 which is the z value for

    Range 13.03 - 16.51 12.89 - 16.65

    99% coverage of a normal populationIf sample size was smaller, difference between

    these calculations increases.

    11As the sample size approaches infinity the TI approaches mean 3s.

  • Specification Example -St bilitStability

    Batch historyTotal SD (process and (pmeasurement)

    Stability batch with SD (measurement)

    Shelf life set from 95% CI on slope from three clinical batches.

    Need to find release criteria for high probability of production batches meeting shelf life based on individual results being less thanmeeting shelf life based on individual results being less than specification.

    R i f b t h hi t ill l d t bilit t t t12

    Review of batch history will lead to a process capability statement against release limit.

  • Specification Example -St bilitStability

    Release limit is calculated from the rate of

    7

    8Release limit is calculated from the rate of change and includes the uncertainty in the slope estimate (rate of change of parameter) and the measurement variation).

    5

    6

    ++=

    nssTtTm am

    222 -Spec RL

    3

    4

    n

    Specification = 8Shelf life (T) = 24

    1

    2( )

    Parameter slope (m) = 0.04726Variation in slope (sm) = 0.00722Variation around line (sa) = 0.86812t on 126 df approx = 2 0

    0 6 12 18 24 30 36MONTHS

    t on 126 df approx. = 2N= 1 for single determination

    Release limit = 5.1

    A similar approach can be taken in more complex situations; e.g,, after reconstitution of lyophilised product Product might slowly change during cold

    13