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Sept. 16, 20041 Harsha Ingle, Ph.D. GlaxoSmithKline Using Statistics in Optimization and Analysis of Bioprocessing -- 20 Tips for Success -- George Bernstein, Ph.D. Manufacturing Analysis Inc. Copyright 2004 Manufacturing Analysis Inc. Slide 2 Sept. 16, 20042 Topics Biases Organizational Data Collecting it Looking at it Analyzing it Statistical Analysis Copyright 2004 Manufacturing Analysis Inc. Slide 3 Sept. 16, 20043 Biases Copyright 2004 Manufacturing Analysis Inc. Slide 4 Sept. 16, 20044 1. Many people dont like or trust statistics Didnt like it in college Dont understand the limitations/power of statistics Have been burned in previous projects Copyright 2004 Manufacturing Analysis Inc. Slide 5 Sept. 16, 20045 To implement a successful statistical project, you must: Follow good project management principles Establish clear objectives Reasonable timelines Establish a multidisciplinary team, Set clear realistic goals Be able to communicate progress/results in plain English Copyright 2004 Manufacturing Analysis Inc. Slide 6 Sept. 16, 20046 2. Statistics dont hate you so dont hate them Copyright 2004 Manufacturing Analysis Inc. Slide 7 Sept. 16, 20047 Part of being a good scientist requires knowledge of statistical principles Statistics is a language that takes time and patience to understand Budget some time to talk with a statistician before you begin any large project Make a point to learn the strategic use of the tools Copyright 2004 Manufacturing Analysis Inc. Slide 8 Sept. 16, 20048 3. Dont ask, Dont tell? Copyright 2004 Manufacturing Analysis Inc. Slide 9 Sept. 16, 20049 Statistics makes you accountable: If you just spent $100K on a study, and your management team likes it, do you really want to find out if your results are statistically significant? If you cant afford a negative answer, dont ask the question. Copyright 2004 Manufacturing Analysis Inc. Slide 10 Sept. 16, 200410 Organizational Copyright 2004 Manufacturing Analysis Inc. Slide 11 Sept. 16, 200411 4. Bring your statistician in early Copyright 2004 Manufacturing Analysis Inc. Slide 12 Sept. 16, 200412 Unlike dropping your receipts on your tax accountants desk, you cant throw data at your statistician after the fact and expect him/her to make sense of it Copyright 2004 Manufacturing Analysis Inc. Slide 13 Sept. 16, 200413 5. Get everyone involved Copyright 2004 Manufacturing Analysis Inc. Slide 14 Sept. 16, 200414 Studies take time and are expensive Better to start well than to re-do the study You may not know all the factors that can affect the study. Get as many people that might influence the study involved in the planning: Brainstorming Buy-in Copyright 2004 Manufacturing Analysis Inc. Slide 15 Sept. 16, 200415 Data Copyright 2004 Manufacturing Analysis Inc. Slide 16 Sept. 16, 200416 6. The second most valuable resource a company has is its production data (the first is the people) Copyright 2004 Manufacturing Analysis Inc. Slide 17 Sept. 16, 200417 We love being data pack rats because it makes us feel secure that our processes are in control But more important than this reflex is to determine the right data and use it. To make use of your production data You must know what data are important Remember 21CFR Part 11 Copyright 2004 Manufacturing Analysis Inc. Slide 18 Sept. 16, 200418 Raw Materials Bioprocess Unit Process Unit Process Unit Process Different processes, different data Copyright 2004 Manufacturing Analysis Inc. Slide 19 Sept. 16, 200419 7. Do you have a good sampling plan ? Copyright 2004 Manufacturing Analysis Inc. Slide 20 Sept. 16, 200420 A large number of 486s are due to bad sampling plans. For example: Due to long QC testing schedules, samples are often taken at the beginning or start of the process Samples that are hard to obtain are ignored The result? Samples that are not representative of your process Wrong conclusions Copyright 2004 Manufacturing Analysis Inc. Slide 21 Sept. 16, 200421 8. Know your errors Copyright 2004 Manufacturing Analysis Inc. Slide 22 Sept. 16, 200422 Different error types Type I error (concluding that things are different when they arent) Type II error (concluding that things arent different when they are) Data entry errors (always double check!). Computational errors. Have methods to validate your data before you begin your analysis. Copyright 2004 Manufacturing Analysis Inc. Slide 23 Sept. 16, 200423 9. Collecting data is not even half the job Copyright 2004 Manufacturing Analysis Inc. Slide 24 Sept. 16, 200424 10. Expect 90% of your efforts in data cleansing Copyright 2004 Manufacturing Analysis Inc. Slide 25 Sept. 16, 200425 Once you collect it, you have to: organize it analyze it correctly act on it Technology has made it easy to collect volumes of data. Probably 95% of all data that is collected is archived without being used. This is expensive. Copyright 2004 Manufacturing Analysis Inc. Slide 26 Sept. 16, 200426 11. Use graphical techniques to Analyze your process Look for patterns, trends Copyright 2004 Manufacturing Analysis Inc. Slide 27 Sept. 16, 200427Copyright 2004 Manufacturing Analysis Inc. Slide 28 Sept. 16, 200428 Raw material Seasonal variation? Effect on process? Copyright 2004 Manufacturing Analysis Inc. Slide 29 Sept. 16, 200429 12. Run charts are an invaluable way to track/analyze your process Copyright 2004 Manufacturing Analysis Inc. Slide 30 Sept. 16, 200430 Control charts are a way to: Look at a process over time Separate random variations from a trend Identify a causative reason for a process shift Track/trend your process so you can take action before a problem occurs/recurs. Copyright 2004 Manufacturing Analysis Inc. Slide 31 Sept. 16, 200431Copyright 2004 Manufacturing Analysis Inc. Slide 32 Sept. 16, 200432 13. No matter what, statistics will not disprove the laws of nature Copyright 2004 Manufacturing Analysis Inc. Slide 33 Sept. 16, 200433 Be able to explain what the statistics say This is the purpose of a multifunctional team Beware of data artifacts Test your conclusions in the lab Copyright 2004 Manufacturing Analysis Inc. Slide 34 Sept. 16, 200434 We wish that most data Would look like this Weight By Height 100 120 140 160 180 200 60.062.565.067.570.072.5 Height Linear Fit Weight = -237.03 + 5.73945 Height Copyright 2004 Manufacturing Analysis Inc. Slide 35 Sept. 16, 200435 Unfortunately, is often looks like this Copyright 2004 Manufacturing Analysis Inc. Slide 36 Sept. 16, 200436 0 5 10 15 20 :0:00:00:0:05:33:0:11:06:0:16:40 Elapsed Time of Settle What conclusions can you draw? Copyright 2004 Manufacturing Analysis Inc. Slide 37 Sept. 16, 200437 Statistical Analysis Copyright 2004 Manufacturing Analysis Inc. Slide 38 Sept. 16, 200438 14. Purchasing a statistics package does not guarantee that you will be doing good statistics no matter how good the software Copyright 2004 Manufacturing Analysis Inc. Slide 39 Sept. 16, 200439 Lots of statistical packages available: Excel (Analyze-it) SAS (ECO, JMP, Systat SPSS S-Plus Minitab Copyright 2004 Manufacturing Analysis Inc. Slide 40 Sept. 16, 200440 15. Know your statistics package Copyright 2004 Manufacturing Analysis Inc. Slide 41 Sept. 16, 200441 Do you know what assumptions are being made by the software? Just because you got an answer doesnt mean its the right one. Does the test expect the data to be normally distributed? If not, is using non-parametric methods more suitable? Copyright 2004 Manufacturing Analysis Inc. Slide 42 Sept. 16, 200442 16. One statistics tool doesnt fit all studies Copyright 2004 Manufacturing Analysis Inc. Slide 43 Sept. 16, 200443 Example: A manager is familiar with t-tests and wants to use it for comparing means of two groups for all studies. That may not always be appropriate. If there are more than 2 groups, an ANOVA may be more appropriate or ANCOVA, if there is an influencing variable that can be used as a covariate. Copyright 2004 Manufacturing Analysis Inc. Slide 44 Sept. 16, 200444 17. Data Modeling keep it simple Copyright 2004 Manufacturing Analysis Inc. Slide 45 Sept. 16, 200445 Given enough variables, you can fit anything Keep to the critical process parameters Consider SOME interaction terms Occams Razor Make sure variables chosen are controllable Copyright 2004 Manufacturing Analysis Inc. Slide 46 Sept. 16, 200446 18. Optimization involves experimentation Copyright 2004 Manufacturing Analysis Inc. Slide 47 Sept. 16, 200447 Use Design of Experiments (DOE) to Maximize the information Minimize the number of experiments Copyright 2004 Manufacturing Analysis Inc. Slide 48 Sept. 16, 200448 19. You cannot predict a response outside of your solution space Copyright 2004 Manufacturing Analysis Inc. Slide 49 Sept. 16, 200449 You may have found a local maximum but it may not Be the optimal solution Scale up Represent stable conditions Which means more experiments Copyright 2004 Manufacturing Analysis Inc. Slide 50 Sept. 16, 200450 20. Document all work including data, study results, and conclusions to ensure compliance with GMPs Copyright 2004 Manufacturing Analysis Inc.