1. CLINICAL TRIAL

3. Clinical Trials Strengths: – Best measure of causal relationship – Best design for controlling bias – Can measure multiple outcomes Weaknesses: – High cost – Ethical issues may be a problem – Compliance

4. Intuition and Logic in Research Dominant Mental Activity Intuition Feeling Judgement Experience Analysis Experiment Control over variance Hi Potential for Misinterpretation Qualitative Research Case Report Case Series Cross-sectional Study Case-control Study Cohort Study Clinical trials Lo Hi

5. Randomised Controlled Trial (RCT)

6. Strength of evidence Anecdote Observational Prospective Retrospective Experimental Case series Cohort study Case-control study RCT Systematic Review

7. Randomised Controlled Trial (RCT) RCT is a trial in which subjects are randomly assigned to two groups: -the experimental group -the comparison group or Controls Source: Cochrane Collaboration Glossary

8. CASP Randomised controlled trial population Outcome group 1 group 2 new treatment control treatment inclusion/ exclusion

9.  Study population (participant) treatment / control  Investigators  Assessors  Clinical intervention (medical, surgical,hygiene)  Outcome

10. Who is in control? Every experiment should have a “control group.” People in control group are treated exactly the same way as the other people in the experiment, except they do not get the “active treatment.” A “placebo group” is a special kind of control group.

11. RANDOMIZATION Definition advantage Pseudo randomization( quasi –R) disadvantage

12. راههای تقسیم تصادفی افراد بین گروهها coin toss envelope Random number table Computer assisted

13. Randomization simpleclusterstratified

14. Blinding: Open Single-blind Double blind :with placebo or active control(double dummy) neither the researcher nor the individuals know who received what Triple blind

15. Potential benefits accruing dependent on those individuals successfully blinded Individuals psychological More likely to comply with trial regimens Less likely to seek additional interventions Less likely to leave trial Trial investigators Less likely to transfer their inclinations or attitudes to participants Less likely to differentially administer co-interventions Less likely to differentially adjust dose Less likely to differentially withdraw participants Less likely to differentially encourage or discourage participants to continue trial Assessors Less likely to have biases affect their outcome assessments, especially with subjective outcomes

16. Ascertainment selectionBIAS publication Inappropriate handling of withdrawals

17. SELECTION BIAS Inclusion & exclusion Intervention New drug on MS and depression Randomization Allocation concealment – if both patients and investigators could not predict the next assignment of treatment

19.  Double blinding prevents ascertainment bias and protects randomization after allocation and during study  Allocation concealment prevents selection bias and protects randomization during selection

20. Assessment of quality To exclude Selection bias Concealment of allocation Performance bias Blinding of both patients and care providers Attrition bias Intention-to-treat analysis and > 80% of the patients with a complete follow-up Detection bias Blinding of outcome assessors to the treatment assignment

21. Trial design Study execution Reporting Publication

22. برای چه مواردی مناسب نیست؟ RCT Testing the etiology of disease rare outcome Low participation Exclusion Low cost

23. RCT IS NOT suitable for: * ETIOLOGY AND CLINICAL COURSE smoking and cancer * RARE & PROLONGED OUTCOME

24. ethics Phase 1 – 20-80 – Toxic and pharmacologic effects Phase 2 – 100-200 – Efficacy – immunity Phase 3 – RCT – Multicenter Phase 4 – After release

25. Quality of RCT

26. RCTs - a checklist Good randomisation procedures patients blind to treatment clinicians blind to treatment all participants followed up all participants analysed in the groups to which they were randomised (intention to treat)

27. limitations Loss to follow up Contamination – Drop out – Drop in

28. Effect

29. 75 25 8713 YesNo Cure A B Treatment 100 16238200 Total Randomized Clinical Trials

30. ARR(absolute risk reduction) RR OR RRR:Efficacy= (risk in treatment-risk in control)/risk in control NNT(Number needed to treat)=1/ARR

31. Definition Number Needed to Treat (NNT): – Number of persons who would have to receive an intervention for 1 to benefit. – 100/ARR (where ARR is %) – 1/ARR (where ARR is proportion)

32. NNTs from Controlled Trials CER%EER%ARR%NNT Population: hypertensive 60-year-olds Therapy: oral diuretics Outcome: stroke over 5 years 2.91.91100 Population: myocardial infarction Therapy: ß-blockers Outcome: death over 2 years 9.87.32.540 Population: acute myocardial infarction Therapy: streptokinase (thrombolytic) Outcome: death over 5 weeks 129.22.836

33. Cross over studies

34. Types: – planned Washout period Sequence of treatment – Unplanned

37. 37 Factorial designs Two or more independent variables are manipulated in a single experiment They are referred to as factors The major purpose of the research is to explore their effects jointly Factorial design produce efficient experiments, each observation supplies information about all of the factors

38. 38 A simple example Investigate an education program with a variety of variations to find out the best combination – Amount of time receiving instruction 1 hour per week vs. 4 hour per week – Settings In-class vs. pull out 2 X 2 factorial design – Number of numbers tells how many factors – Number values tell how many levels – The result of multiplying tells how many treatment groups that we have in a factorial design

39. 39 Null outcome None of the treatment has any effect Main effect – is an outcome that is a consistent difference between levels of a factor. Interaction effect – An interaction effect exists when differences on one factor depend on the level you are on another factor.

40. 40 Main effects Main effect of time Main effect of setting Main effects on both

41. 41 Interaction effect An interaction effect exists when differences on one factor depend on the level of another factor How do we know if there is an interaction in a factorial design? – Statistical analysis will report all main effects and interactions. – If you can not talk about effect on one factor without mentioning the other factor – Spot an interaction in the graphs – whenever there are lines that are not parallel there is an interaction present!

42. 42 Interaction effect Interaction as a difference in magnitude of response Interaction as a difference in direction of response

43. 43 Factorial design variations A 2 X 3 example study the effect of different treatment combinations for cocaine abuse. – Factor 1: treatment psychotherapy behavior modification – Factor 2: inpatient day treatment outpatient – Dependent variable severity of illness rating

44. Friday, May 14, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 44 A simple example Investigate an education program with a variety of variations to find out the best combination – Amount of time receiving instruction 1 hour per week vs. 4 hour per week – Settings In-class vs. pull out 2 X 2 factorial design – Number of numbers tells how many factors – Number values tell how many levels – The result of multiplying tells how many treatment groups that we have in a factorial design

45. Friday, May 14, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 45 Null outcome None of the treatment has any effect Main effect – is an outcome that is a consistent difference between levels of a factor. Interaction effect – An interaction effect exists when differences on one factor depend on the level you are on another factor.

46. Friday, May 14, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 46 Main effects Main effect of time Main effect of setting Main effects on both

47. Friday, May 14, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 47 Interaction effect An interaction effect exists when differences on one factor depend on the level of another factor

48. Friday, May 14, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 48 Interaction effect Interaction as a difference in magnitude of response Interaction as a difference in direction of response

49. Friday, May 14, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 49 Factorial design variations A 2 X 3 example study the effect of different treatment combinations for cocaine abuse. – Factor 1: treatment psychotherapy behavior modification – Factor 2: inpatient day treatment outpatient – Dependent variable severity of illness rating

50. Before after study