ten deadly statistical traps in pharmaceutical quality control l.
Skip this Video
Loading SlideShow in 5 Seconds..
Ten Deadly Statistical Traps in Pharmaceutical Quality Control PowerPoint Presentation
Download Presentation
Ten Deadly Statistical Traps in Pharmaceutical Quality Control

Loading in 2 Seconds...

play fullscreen
1 / 70

Ten Deadly Statistical Traps in Pharmaceutical Quality Control - PowerPoint PPT Presentation

  • Uploaded on

Ten Deadly Statistical Traps in Pharmaceutical Quality Control. Lynn Torbeck Pharmaceutical Technology 29 March 2007. Your Morning Mantra. “In theory there is no difference between theory and practice, but in practice there is.” Yogi Berria. The Ten Deadly Sins. Graphs Normal Distribution

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

Ten Deadly Statistical Traps in Pharmaceutical Quality Control

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
ten deadly statistical traps in pharmaceutical quality control

Ten Deadly Statistical Traps in Pharmaceutical Quality Control

Lynn Torbeck

Pharmaceutical Technology

29 March 2007

your morning mantra
Your Morning Mantra

“In theory there is no difference between theory and practice, but in practice there is.”

Yogi Berria

the ten deadly sins
The Ten Deadly Sins
  • Graphs
  • Normal Distribution
  • Statistical Significance
  • Xbar 3S
  • %RSD
the ten deadly sins4
The Ten Deadly Sins
  • Control Charts
  • Setting Specifications
  • Cause and Effect
  • Variability
  • Sampling Plans
graph what graph
Graph? What &%$# Graph?
  • Q#1 “Have you graphed the data?”
  • I have solved many statistical problems by simply graphing the data.
  • Always, always, always plot your data.
  • No ink on the page that isn’t needed.
  • Cause and effect on the same page.
  • Make the answer appear obvious.
  • Read Edward Tufte’s books
anscombe s astounding graphs7
Anscombe’s Astounding Graphs
  • N=11
  • Average of X’s = 9.0
  • Average of the Y’s = 7.5
  • Regression Line Y=3+0.5X
  • R2 = 0.67
  • Std Error of the Slope = 0.118
  • Residual Sums of Squares = 13.75
prolonged acting pro stuff
Prolonged Acting Pro-Stuff
  • An ulcer drug from the late 1960’s.
  • In 1980 a change in a raw material resulted in more rejects.
  • In-process control using a UV assay
  • Composite of 5 tablets assayed
prolonged acting pro stuff13
Prolonged Acting Pro-Stuff
  • Sample from the top of each can
  • Specs were 95% to 105%
  • If value in spec, accept the can
  • If value out of spec, reject the can
  • Accepting and rejecting specific cans
  • About 50% of the cans were rejected
prolonged acting pro stuff17
Prolonged Acting Pro-Stuff
  • No good cans or bad cans.
  • Some “good” cans when retested are now out of specifications.
  • The cans accepted are just as bad or good as the cans rejected.
  • 45% of the values are OOS
  • The product was taken off the market.
  • A personal story
a little normal history
A Little Normal History
  • The concept of the Normal is basic.
  • Also called Gaussian or Bell Curve.
  • First published in November 12, 1733.
  • First set of tables in 1799 !
  • Used by the astronomer Laplace for errors.
  • First called the Normal in 1893 by the statistician Karl Pearson.
they were blown away
They Were Blown Away
  • “I know of scare anything so apt to impress the imagination as the wonderful form of cosmic order expressed by the ‘Law of Frequency of Error.’”

Francis Galton in Natural Inherence, 1888

hunting the elusive normal
Hunting the Elusive Normal
  • I have never met a real Normal distribution. Gotten close a couple of times.
  • There are no real Normal distributions
  • It’s a theoretical fiction that is useful part of the time.
  • We must separate reality from theory.
normal distribution
“Normal Distribution”






normal facts
Normal Facts
  • In theory, the tails of the distribution stretch from minus infinity to plus infinity, but there are real physical limits.
  • It is unique in that it is fully described by just its mean, mu, , and its standard deviations, sigma, , which are almost never actually known for certain.
  • Probabilities are represented by areas.
what s normally normal
What’s Normally Normal?
  • Tablet and capsule weights
  • Most manufactured parts
  • Student test scores, the ‘bell curve’ again
  • Things that grow in nature:
    • Apples
    • Bird eggs
    • Flowers
    • Peoples heights
ain t never gonna be normal
Ain’t Never Gonna be Normal
  • Particle sizes
  • LAL, EU/mL
  • Bioburden, cfu/mL
  • Failures of most anything
  • Telephone calls per unit of time
  • Church contributions
  • Floods
watch out
Watch Out!
  • The tails are the most volatile and unstable
  • But, that is often the area of most interest!
  • Difficult to tell if data are normally distributed by looking at a small sample.
  • Crude rule is that we need at least 100 representative data values to determine if it is even approximately normal.
statistical significance who cares
Statistical Significance:Who Cares ?
  • The role of statistical analysis is as an additional tool to assist the scientist in making scientific interpretations and conclusions and not an end in itself.
  • A scientific analysis often takes the form of looking for significant differences.
  • Is drug A different from drug B?
  • Is the increase in yield significantly better with the new centrifuge?
  • A difference can be significant in two ways, practical and statistical.
practical significance
Practical Significance
  • Practical significance comes form comparing a difference to an absolute reference or absolute truth.
  • How big a difference can you accept for:
    • Number of seconds of tooth pain?
    • Number of phone rings before hanging up?
    • How long will you wait for a bus?
    • How big your next raise is?
statistical significance
Statistical Significance
  • Statistical significance testing is one of the great tools of statistics and science.
  • Statistical significance comes from comparing a difference, a signal, to a relative reference of random variability or the best estimate of noise in the data.
practical vs statistical
Practical vs.Statistical
  • Practical Significance always wins and takes precedence over statistical significance!
  • In most applications, statistical significance should not be tested until practical significance is found.
are the analysts different





Spec= 90.0 to 110.0






Two Sided t, P=0.04

Are The Analysts Different?
signal to noise
Signal to Noise
  • All statistical significance testing is only a comparison of the signal to the noise.
  • If the signal can be shown to be larger than the noise, than we would expect by chance variation alone, we say it is significant.
  • Bigger signal more significant.
  • Smaller noise more significant.
why do it to it
Why Do It To It?
  • The primary purpose of statistical tests of significance is to prevent a us from accepting an apparent result as real when it could be just due to random chance.
  • Statistical significance without practical significance could in some circumstances be a lead to finding new relationships.
  • What if the spec was changed to 98.0 to 102.0?
  • We may want to find out why different
the biggest lie in statistics
The Biggest Lie in Statistics?
  • Your statistics professor mislead or lied.
  • Is Xbar±3S ever Correct?
  • For ever complex problem there is a solution that is quick, simple, understandable and absolutely wrong!
  • More grief has been perpetuated by this formula than any in statistics.
the biggest lie in statistics38
The Biggest Lie in Statistics?
  • What is true is that   3  will bracket 99.73% of the area under the normal cures.
  • Note that this assumes we know the true values for the mean mu, , and standard deviation, sigma, , which we never do of course. We have to estimate them with the small samples we take.
  • Thus, there is uncertainty in the estimates.
side line
Side Line
  • Did you hear about the statistician’s wife who said her husband was just average?
  • She was being mean.
so what do i do now
So, What Do I Do Now?
  • Don’t use Xbar±3S as generalized monkey wrench and apply it to all of your statistical questions. Use the right tool for the job.
  • Use Confidence Intervals to bracket the unknown mean.
  • Use Tolerance Intervals to bracket a given percentage of the individual data values.
rsd friend or foe
%RSD: Friend or Foe?
  • S= SQRT[(X-Xbar)2/(n-1)]
  • %RSD = (100 * S) / Xbar
  • They are two different summary statistics
  • They measure two different concepts
  • They are not substitutes for each other
  • We need to report both.
control charts
Control Charts
  • Having just told you not to use Xbar±3S, I now have to tell you that is how control charts define the control limits.
  • This is an artifact of history.
  • Control charts were developed by Dr. Walter Shewhart in 1924 while working at Western Electric in Cicero Ill.
control chart
Control Chart
  • Add Xbar 3S limits to a line plot.
  • A chart for the response.
  • A chart for the moving range to estimate variability.
do you trust your control chart
Do You Trust YourControl Chart?
  • Control charts are crude tools and not exact probability statements.
  • They don’t take into account the number of samples in the data set for the limits.
  • They are intended as early warning devices and not accept/reject decision tools.
  • Don’t use for large $$ decisions.
oh wow i don t believe it
Oh Wow, I Don’t Believe It !

You did what to set the specification criteria for your million dollar product?

setting specifications
Setting Specifications
  • A specification is a document that contains methods and accept/reject criteria
  • Criteria can be determined several ways
    • Wishful thinking
    • Clinical results
    • Compendial standards
    • Historical data and statistics
million decisions
Million $$ Decisions?
  • Regulatory Limits - External
  • Release: accept/reject - Internal
  • Action limits
  • Alert
    • Warning limits
    • Trend limits
    • Validation limits
calculating criteria
Calculating Criteria
  • Don’t use Confidence Intervals, they shrink toward zero with large sample sizes.
  • Don’t use X bar ± 3 S. They are too narrow for small sample sizes
  • Use Tolerance Intervals, preferably 99%/99%. This will take into consideration the sample size and uncertainty of the average and the standard deviation.
setting specification criteria
Setting Specification Criteria
  • For action limits, expect the average to vary and widen the Tolerance Limits
  • For accept/reject limits, add a further allowance for stability.
  • Consider the clinical results when possible as part of the justification for limits.
drunken teachers
Drunken Teachers
  • Did you know that there is a positive correlation between alcohol consumption and High School teacher’s salaries?
  • That there is a negative correlation between average student’s test scores for a state and the distance of the state capital from the Canadian boarder?
cow magnets cure gout
Cow Magnets Cure Gout
  • What’s a cow magnet?
  • What is gout?
  • How do we test a cause and effect relationship to see if this works?
  • Should we just ask people what they think?
  • “No causation without manipulation.”
  • Gold Standard is double blind clinical trial.
variability is the enemy
Variability is the Enemy
  • How many OOS values were documented in the lab last year?
  • How many manufacturing deviations were investigated last year?
  • How many lots were rejected last year?
  • How many of your quality problems would go away if there were no variation?
misconceptions of variability
Misconceptions of variability
  • We have variability because the equipment needs to be replaced with new technology.
  • We do too many tests.
  • Variability exists because some idiot didn’t do their job correctly.
  • Variability is an inherent fact of life and there isn’t a darn thing we can do about it except to live with it. It’s cost of business.
variability is the enemy55
Variability is the Enemy
  • “Special Cause” variation is the result of a single source. Use CAPA to solve it.
  • “Common Cause” variation is the result of multiple small sources all contributing to the sum total.
  • CAPA will not work for common cause
  • We need a culture change to address common cause variation
sources of variation
Sources of Variation:
  • Common cause variation:
    • People
    • Materials
    • Methods
    • Measurement
    • Machines
    • Environment
common vs special causes
Common vs. Special Causes
  • A plot of the data with X bar ± 3 S illustrates common cause variation.
  • A value that is larger than would be expected by chance alone is assumed to be due to a special cause.
deming s message
Deming’s Message
  • Dr. W. Edwards Deming was the very famous statistician that taught statistical quality control to the Japanese in the 50’s.
  • “If I had to reduce my message for management to just a few words, I’d say it all had to do with reducing variation.”
deming s message59
Deming’s Message
  • If you reduce variability, you will reduce scrap, rejects and rework. You can then make a better product at less cost. You will capture a larger market share. Your people will be employed and you will prosper.
      • Paraphrase of Deming’s message
confronting the enemy
Confronting the Enemy
  • Operational Definitions
  • Achieve the Target
  • Flexible Consistency
  • Hold Constant Controllable Factors
  • Mistake Proofing
  • New Technology
  • Continuous and forever improvement
the black hole of quality
The Black Hole of Quality
  • Like a black hole with light, sampling plans just suck the common sense right out of people’s brains.
  • Normal, logical and rational people suddenly become willfully and terminally stupid.
  • Many myths and misconceptions about what sampling plans can and can not do.
black hole facts
Black Hole Facts
  • A sample is only a small part of the whole
  • Each sample is going to be different
  • Some samples will have many defects
  • Some samples will have few defects
  • Bigger sample, better estimate.
  • On average, the defect percent can only be estimated and not known perfectly.
black hole facts63
Black Hole Facts
  • There is a small but real probability that a good lot of product will be rejected.
  • Called the “Producer’s Risk, usually 5%.
  • There is a small but real probability that a bad lot will be accepted.
  • “Consumer’s Risk, usually 5% or 10%
  • Most common plan is ANSI/ASQ Z1.4.
black hole facts64
Black Hole Facts
  • “The AQL is the quality level that is the worst tolerable process average … .”
  • “The acceptance of a lot is not intended to provide information about lot quality.”
  • “The standard is not intended as a procedure for estimating lot quality or for segregating lots.”
black hole facts65
Black Hole Facts
  • “The purpose of this standard is, through the economic and psychological pressure of lot non-acceptance, to induce a supplier to maintain a process average at least as good as the specified AQL while at the same time providing an upper limit on the consideration of the consumer’s risk of accepting occasional poor lots.”
  • Double and multiple sampling plans are not testing into compliance.
  • It is not possible to have an AQL=0.0
  • Accept on zero, reject on one is not always the best plan for critical defects.
  • If the lot size is ten times or more than the sample size, then the lot size doesn’t matter.
  • “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.”

H. G. Wells

  • NIST online statistics textbook
    • http://www.itl.nist.gov/div898/handbook/index.htm
  • Edward Tufte’s website
    • http://www.edwardtufte.com/tufte/
  • W. Edwards Deming’s book
    • Out of the Crisis
  • Torbeck, Lynn.,Using Statistics to Measure and Improve Quality, DHI Publishing 2004.
  • De Muth, James (1999). Basic Statistics and Pharmaceutical Statistical Applications, Marcel Dekker.
that s all folks
“That’s All Folks”
  • Thank you !
  • Questions ?