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CLINICAL TRIAL

CLINICAL TRIAL

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CLINICAL TRIAL

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  1. CLINICAL TRIAL

  2. 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

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

  4. Randomised Controlled Trial (RCT)

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

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

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

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

  9. 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.

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

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

  12. 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

  13. 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

  14. 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

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

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

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

  18. Quality of RCT

  19. 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)

  20. limitations • Loss to follow up • Contamination • Drop out • Drop in

  21. Effect

  22. Randomized Clinical Trials Cure Total Yes No 100 A 13 87 Treatment 75 100 25 B 38 162 200

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

  24. 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)

  25. NNTs from Controlled Trials

  26. Cross over studies

  27. Cross over studies • Types: • planned • Washout period • Sequence of treatment • Unplanned

  28. 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

  29. 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

  30. Main effects • Main effect of time • Main effect of setting • Main effects on both

  31. 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 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney

  32. 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. ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney

  33. Main effects • Main effect of time • Main effect of setting • Main effects on both ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney

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

  35. Interaction effect • Interaction as a difference in magnitude of response • Interaction as a difference in direction of response ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney

  36. Before after study