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Trend Data

Trend Data. Lynn Torbeck Torbeck and Assoc. Evanston, IL. Overview. OOT vs. OOS Why trend? How to get started Types of trends with examples OOT is relative Graphical tools Tend limits. Why Trend Data?. Good business practice.

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Trend Data

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  1. Trend Data Lynn Torbeck Torbeck and Assoc. Evanston, IL

  2. Overview • OOT vs. OOS • Why trend? • How to get started • Types of trends with examples • OOT is relative • Graphical tools • Tend limits

  3. Why Trend Data? • Good business practice. • Early warning of possible Out Of Specification (OOS) results. • Gain process understanding. • Minimize risk of potential failures of product in the market. • Find the “gold in the hills” for process improvements.

  4. Regulatory Basis for Trending • No specific regulation requirement • 211.180(e) Annual Reviews • FDA Form 483 for observations • Establishment Inspection Reports • Warning letters • FDA presentations at conferences

  5. OOS Guidance Footnote • “Although the subject of this document is OOS results, much of the guidance may be useful for examining results that are out of trend (OOT).” • How is OOT different than OOS? • How is OOT the same as OOS?

  6. Out Of Specification - OOS • OOS is the comparison of one result versus a predetermined specification criteria. • OOS investigations focus on determining the truth about that one value. • Is the OOS result confirmed or not?

  7. Out Of Trend - OOT • OOT is the comparison of many historical data values versus time. • OOT investigations focus on understanding non-random changes. • Is the non-random change confirmed or not?

  8. OOS Guidance • Taking into account the differences between OOS and OOT, the guidance does provide a framework for OOT investigations: • Responsibilities • Philosophical basis • General principles of investigations

  9. 1. How to get started • Select the variable to be studied: • Potency • Yield • Impurities • Hardness • Bioburden

  10. 2. How to get started • Select a time period: • At least one year if possible. • More than two preferred. • Do not go past a major change in the process. Use process knowledge to advantage. • Use the reportable result, the value compared to the specifications.

  11. 3. How to get started • Enter the data into analysis software: • Excel • Minitab • Sigma Plot • JMP • StatGraphics • Northwest Analytical • SAS

  12. 4. How to get started • Plot the data vs. time or lot sequence. • Look for non-random changes over time. • Determine if they are of practical importance. • Statistical significance is insufficient. • Do an impact and risk assessment.

  13. What is Trending? • The several activities of: • Collecting data, • Recording it, • Documenting it, • Storing it, • Monitoring it, • Fitting models to it • Evaluating it, and • Reporting it.

  14. What is a trend? • Any non-random pattern. • Short and long term patterns in data over time that are of practical importance.

  15. Beneficial Trends • Desirable patterns in the data series. • Examples: • A move toward the target or center of the specification. • More consistent with less variation. • Less likelihood of an OOS value. • A benefit to SSQuIP.

  16. Beneficial Trend

  17. Easier to define what a trend is not. Random data Noise Stationary No ups, no downs No cycles No outliers No Trend

  18. Neutral or No Trend • Neither beneficial or adverse • Examples: • Results that are always the same. • Stability data with a slope of zero. • Data in a state of “statistical control” on a control chart.

  19. Process Control • Statistical Process Control, SPC • Normal random data over time • Due to common causes only • Engineering Process Control, EPC • Estimate departures from target • Feedback to control point • Physical changes to the process

  20. Adverse Trends • Undesirable patterns in the data series. • Examples: • A movement away from the target. • Increased variability. • Increased probability of OOS. • An unexplained change to a beneficial trend. • A challenge to SSQuIP.

  21. Out-of-Trend (OOT) • A change from an established pattern that has the potential of an adverse effect on SSQuIP or of becoming OOS. • Must be large enough to be of practical significance. • Statistical significance is insufficient to determine OOT.

  22. Long Term Change • Not stationary around a fixed value • Increasing or decreasing average. • Apparently will continue to get worse (or better) unless action is taken.

  23. The Aberrant Outlier • Stationary and random but with one very large value that could be a statistical outlier. • Generally assumed to be due to a “special cause.”

  24. Shift in the Average • Here the mean has increased from 100 to 104 at sample 51. • No other changes were made. • Variability is the same.

  25. Variation Change • This is stationary around a fixed mean of 100%. • But, the standard deviation increased from 1.0 to 4.0.

  26. Cycles • A reoccurring cycle. • Stationary about a fixed mean. • The data are not independent.

  27. Autocorrelated • Data are correlated with the previous data. • Not stationary. • Check different time lags, 1,2, ….

  28. OOT is Relative

  29. OOT is Relative • The importance of a trend is its size relative to the specification criteria. • A state of Statistical Control is desired but not necessary. • A state of Engineering Control is necessary to meet specifications. • Success is a marriage of the two.

  30. A Little Humor (Very Little) • Lottery: A tax on the statistically-challenged. • If you want three opinions, just ask two statisticians. • Statistics means never having to say you're certain. • http://www.keypress.com/x2815.xml

  31. Trend Fitting • “The general process of representing the trend component of a time series.” • A Dictionary of Statistical Terms. Marriott • Depends very much on the type of data and the subject matter being studied. • Need to adapt the tools and techniques to our specific data and issues.

  32. Tools of Trending • Summary statistics • Averages, Medians • Ranges, Standard Deviations, %RSD • Graphical plots • Distribution analysis - Histograms • Outlier determination • Regression analysis

  33. Graphic Tools • Line Plots vs. time. • Shewhart Control Charts. • Histograms. • Sector chart

  34. Line Plots vs. Time • Response on the vertical axis. • Time or batch # on the horizontal axis. • Usually connect the data points with a line, but optional.

  35. Control Chart • Add ‘natural process limits’ to the line plot. •  ± 3  • A chart for the response. • A chart for the variability.

  36. Control Chart Family • Individuals • Averages • Medians • Standard deviations • Ranges • Number of defectives • Fraction defectives • Defects per units • Number of defects

  37. Variation Change • A control chart will detect change in the variation.

  38. The Outlier • A control chart finds values outside the natural limits of the data. • The value is larger than would be expected by chance alone.

  39. “Western Electric” Rules • One value outside 3 S limits. • Nine values in a row on one side of the average. • Six values in a row all increasing or decreasing. • 14 values in a row alternating up and down.

  40. “Western Electric” Rules • Two of three values greater than 2 S from the average. • Four of five values greater than 1 S from the average. • 15 values in a row within 1 S of the average. • Eight values in a row greater than 1 S.

  41. Histogram • Show the ‘shape’ of the distribution of data. • In this case it is Normally distributed.

  42. The Outlier • The outlier is clearly seen in the histogram.

  43. Outlier Determination • Reference: • USP 30 NF 25 • Chapter <1010> • “Analytical Data – Interpretation and Treatment” • Page 392 “Outlying Results” • Appendix C: Examples of Outlier Tests for Analytical Data.

  44. Regression Analysis

  45. Trend Limits • Numeric (or non-numeric) criteria, that if exceeded, indicates that an out-of-trend change has occurred. • Usually the ‘natural process’ variation • AKA “Alert limits” • Use Statistical Tolerance Limits • See USP <1010> Appendix E

  46. Here, Trend This

  47. A New Engineering Chart • Brings together for the first time: • Comparison to the specification limits in place of the probability limits • Divides the specification range into equal zones in place of 1, 2, & 3 sigma areas • Uses cumulative scores • Pharmaceutical Technology, April 2005

  48. The New “Sector Chart”

  49. The New “Sector Chart” Rules • The first batch tally takes the weight of the sector it is in. • Subsequent batches have a cumulative tally of the previous tally plus the current sector weight. • If the tally reaches a value of, say, 10, an alert is given. • If the batch enters the A or B sectors, the tally is reset to zero.

  50. The New “Sector Chart” Rules • Sectors A and B cover the center 50% of the specification range. • Sector F is outside the current specification. • Other weights can be set to fit the process and the degree of sensitivity needed.

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