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 Trust your clinical experience Common diseases Common diseases Clinical controversies Clinical controversies Standards of practice in human patients Standards of practice in human patients

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

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

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

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

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

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

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

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

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

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

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

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

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

16. RandomizationRandomization Random numbers Random numbers Evaluators should be blinded to scheme Evaluators should be blinded to scheme 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

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

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

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

20. Sample size and power Both prospective and retrospective designs Both prospective and retrospective designs Need enough cases to overcome variability within groups to show a difference between groups 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 I error: finding a difference when it is actually due to chance Type II error: missing a difference that is actually present Type II error: missing a difference that is actually present With too few cases, you can have either type With too few cases, you can have either type

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

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

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

25. Sample size Consider drop-out Consider drop-out

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

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

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

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

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

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

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

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

34. Questions or comments?