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Quality Assurance in Epidemiologic Studies

Quality Assurance in Epidemiologic Studies. Manya DeLeon Miller EPID 712 22 March 2000. Topics to be Discussed. Brief history of quality assurance (QA) Applied aspects of QA for use in clinical trials and observational studies Some “how to” steps to establish/maintain QA systems.

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Quality Assurance in Epidemiologic Studies

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  1. Quality Assurance in Epidemiologic Studies Manya DeLeon Miller EPID 712 22 March 2000

  2. Topics to be Discussed • Brief history of quality assurance (QA) • Applied aspects of QA for use in clinical trials and observational studies • Some “how to” steps to establish/maintain QA systems

  3. History and Background • Quality Assurance (QA) is not a new idea: cunieform instructions for quality in work may be found from 2000 b.c. • Japan and industry initiated “modern” QA efforts/philosophy

  4. History and Background • Biggest efforts from pharmaceutical industry—clinical trials, FDA changes to require randomized controlled trials in 1960’s • Alphabet soup: QA, QC, TQI, QM, TQM, etc., etc… What does it all mean?

  5. An Applied Approach to QA • Data as a skeleton: A person and his reality interviewer/MD/nurse/etc.  case report forms (CRFs) data entry numbers

  6. An Applied Approach to QA • The person who used to look like this: • now looks like this: 1019988563222009

  7. An Applied Approach to QA • QA necessary to ensure that data are abstracted • HONESTLY • ACCURATELY So that the subject is adequately reflected by the data. (fraud vs. error…)

  8. An Applied Approach to QA • REMEMBER: the data should conform the person; the person doesn’t need to conform to the data codes, etc.!

  9. An Applied Approach to QA • Always consider the conclusions that will come from your study, and how inaccurate data will affect your conclusions.

  10. An Applied Approach to QA • Standardization is CRUCIAL • All techniques designed to ensure that data are collected in a fashion that is: • Standardized • Systematic • Reliable • Valid

  11. An Applied Approach to QA • Consider system and QA issues BEFORE setting up study… Not during or after!

  12. Steps in QA • Study set up/study design/protocol • Regulatory • Forms/source documents • QA • QC • Data entry • Monitors/audits

  13. Steps in QA • Study set up/study design/protocol • Starting with a solid protocol is essential. • Should articulate IN DETAIL all aspects of the study so that techniques (lab, data abstraction, evaluations, etc.) are the same irrespective of who/where study is done • NO AMBIGUITY! • See real protocols, book, appendix D, web

  14. Steps in QA • Regulatory • Regulatory aspects should be well maintained; part of assuring not only quality, but safety of subjects (and staff) • IRB • Simple files fine, just be sure that they are consistent and adherent with regulatory guidelines of institution, city, state, federal

  15. Steps in QA • Forms/source documents • Forms (often called Case Report Forms or CRFs) should be well considered and piloted before implementation • Want to standardize how questions are asked and be sure that answers address the study questions of interest • Often, bad formsbad study, even if all else is great • Training is essential!

  16. Steps in QA • QA • Define (second set of eyes…) • Check all data before submission for • Logical consistency • Adherence to protocol/study • Correct representation of subject experience • How to? • Specific training • Red pen sticky note or more elaborate systems

  17. Steps in QA • QA • Team spirit essential with QA

  18. Steps in QA • QC • If possible, great to have a third set of eyes for QC… obvious logical issues. May work together with data entry or be same person

  19. Steps in QA • Data entry • Again, training is key • Validity checks in data entry screens • Real time logical checks (later=harder) • Double data entry (pros/cons) • Random sample double data entry

  20. Steps in QA • Monitors/audits • In clinical trials, very common • Should also do for cohort/case-control/cross-sectional studies

  21. Examples • Pediatric AIDS Clinical Trials Unit • Partner Notification Study

  22. Ever heard the saying, • GARBAGE IN, GARBAGE OUT?? • Think about the effort you are giving your study, the conclusions you want to be able to make. Data need to be valid; QA techniques allow valid data to emerge from your study

  23. Questions??

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