Turning a clinical question into a testable hypothesis

1. Turning a clinical question into a testable hypothesis Lauren A. Trepanier, DVM, PhD Diplomate ACVIM, Diplomate ACVCP Department of Medical Sciences School of Veterinary Medicine University of Wisconsin-Madison ???

2. Clinical questions • Trust your clinical experience • Common diseases • Clinical controversies • Standards of practice in human patients

3. Clinical questions • New diagnostic tests • Better treatment options • Characterization of outcomes • Prognostic indicators • Underlying etiology

4. Getting ideas • Journal club papers • Logical follow-ups • Specialty proceedings • Knowledge gaps • Discussions with senior faculty

5. Define the state of knowledge • Literature search • Multiple search terms • Reference lists from papers • Read full papers!! • Beware abstracts that never made it to peer reviewed pubs

6. Define the knowledge gap • Major conclusions from each paper • Organize as a logical story • Why it is important • What is known in humans • What is known in veterinary species of interest

7. Refining the clinical question • What remains to be answered? • Does your question need revising? • What do you think you will find (your hypothesis)?

8. Framing your research approach • Research objectives, or aims, to specifically test your hypothesis • To compare • To determine • To evaluate • To characterize

9. PICOT approach • Population • Intervention • Comparators • Outcomes • Time frame

10. Population smallanimal.vethospital.ufl.edu • Inclusion criteria • Gold standard for diagnosis • Validated surrogate marker

11. Population • Inclusion criteria • Specific breed(s) • Stage of disease • Severity of illness • Heterogeneity vs. homogeneity

12. Population • Exclusion criteria • Prior treatments allowed? • Washout • Patient size vs. blood drawn • Exclude fractious animals? • Owner consent

13. Intervention • Drug treatment • Surgical procedure • Diagnostic assay • What other care is allowed? • Avoid “clinician discretion” without guidelines

14. Intervention • Blinded vs. double blinded • Applies to all evaluators • Owners • Managing clinicians • Techs administering questionnaires • Radiologists • Pathologists

15. Comparators • Clinically relevant • Normal or suspected of disease? • Placebo or standard of care? • Concurrent • Randomized

16. Randomization Random Numbers 00531 41784 44584 62742 81710 71692 28303 58470 94527 33239 70219 59279 38984 99868 17217 18285 15081 24694 95854 82373 96259 54602 79573 78101 09076 16149 21490 05468 53534 82778 68487 37916 03072 07604 47125 02004 10808 37512 57402 97732 23626 99059 72760 25098 68083 65688 19758 84105 17622 90514 98395 48193 98800 20421 08672 43920 38175 81969 24030 71287 56074 48597 71028 03736 32171 73424 49666 67824 13349 03331 59942 63551 26167 64879 75301 90918 70624 31507 48857 49925 46720 56333 00936 14013 27898 86241 11213 09740 40716 47788 53129 37107 85173 14417 00127 69556 34712 39243 • Random numbers • Evaluators should be blinded to scheme

17. Outcomes • Define a primary outcome • Objective • Easily measured • Clinically available • Validated for your species • Relevant to clinical response Dr. Noel Moens, Guelph

18. Outcomes • Subjective primary outcomes • Validated scoring system • Complement with objective outcomes whenever possible • Blinded evaluators!! Dr. Duncan Lascelles, NCState

19. Outcomes • Secondary outcomes • Less important? • May be harder to prove • Can generate further hypotheses • Add depth

20. Sample size and power • Both prospective and retrospective designs • Need enough cases to overcome variability within groups to show a difference between groups P = 0.0004 Viviano et al. J Vet Intern Med. 2009

21. Sample size calculation • Type I error: finding a difference when it is actually due to chance • Type II error: missing a difference that is actually present • With too few cases, you can have either type

22. Power • Type I error: P = 0.05 • Type II error: often set at 10-20% • Power = 100 -Type II error • Power = ability to detect a true difference • Power often set at 80-90%

23. Sample size (or power) calculation • Two approaches: • Start with known sample size and calculate the power to find a difference • Set a minimum power and calculate needed sample size

24. Sample size (or power) calculation • Choose your stats test based on type of data • Define the variability in your control population (SD) • Define the difference you need to detect http://www.stat.uiowa.edu/~rlenth/Power/

25. Sample size • Consider drop-out

26. Time frame • Recruitment period • Timing of intervention • Duration of intervention • Time points for evaluation

27. Time frame • Consider seasonal variables • Follow-up • Complicated? • Prolonged?

28. Finalized PICOT research plan • Still addresses the hypothesis • Still relevant • Feasible! • Clinical expertise • Caseload • Support staff • Funds • Career time frame

29. Finalized PICOT research plan • Question is of interest to pet owners • Intervention is low risk • Follow-up is convenient • Incentives are considered

30. Common roadblocks • Disease is uncommon • Studied outcome is rare • Data collection too labor intensive

31. Common roadblocks • Samples banked without validated assays (!) • Case identification out of your control • Collaborators unmotivated

32. Key points • Study what you know • Choose straight-forward aims using available assays/procedures • Define the approach using PICOT

33. Key points • Make sure you would volunteer your own pet to participate • Results should be publishable no matter what the outcome

34. Questions or comments?