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Making evidence more accessible using pictures

Making evidence more accessible using pictures. Rod Jackson Oxford 8/9/09. What is Evidence Based Practice?. What is Evidence Based Practice?. The 6 steps of Evidence Based Practice. ASK a focussed question ACCESS - search for epidemiological evidence to help answer question

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Making evidence more accessible using pictures

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  1. Making evidence more accessible using pictures Rod Jackson Oxford 8/9/09

  2. What is Evidence Based Practice?

  3. What is Evidence Based Practice?

  4. The 6 steps of Evidence Based Practice ASK a focussed question ACCESS - search for epidemiological evidence to help answer question APPRAISE the evidence for its validity, effect size, precision) AGGREGATE the evidence with patient/community, clinical/hlth & policy issues & make an evidence-based decision APPLY your decision [AUDIT your practice (i.e. check your actual practice against evidence-based practice on a regular basis)].

  5. © The GATE frame Graphic Approach To Epidemiology

  6. PECOT: the 5 parts of every epidemiological study P Participants Exposure Group Comparison Group E C Outcomes O Time T All epidemiological studies can be hung on the GATE frame

  7. EBP Step 1: ASK - turn your question into 5 parts (PECOT) Participants (patient(s) you want to treat) Exposure (an intervention if about therapy) Comparison (there is always an alternative! - another therapy, nothing … Outcome (usually a disease or condition you want to prevent or manage) Time frame (over which you expect a result)

  8. EBP Step 2: ACCESS - search for the best evidence to answer your questions Use the PECOT components to choose search terms

  9. EBP Step 3:Appraise the evidenceusing PECOT & RAMBO on the GATE frame P • P • E • C • O • T • Recruitment • Allocation • Maintenance • Blind or • Objective measurements & processes E C O T

  10. EBP Step 4: AGGREGATE the relevant information & make an evidence-based decision:’ the X-factor ©

  11. X-factor: making evidence-based decisions Epidemiologic evidence Clinical / population health considerations Patient / community preferences Policy issues expertise: ‘putting it all together’ the art of practice

  12. Step 5 APPLY Implementation!

  13. Step 6: AUDIT - evaluate & improve performance 1. Determine ‘best’ practice (EBP Steps 1-4) 2. Assess current practice: survey 3. Compare with best practice - is there a gap? 4. Consider reasons for gap, identify processes to reduce gap & implement 5. Re-survey: is there any improvement? = quality improvement / audit

  14. GATEGraphic Approach To Epidemiology Graphic Appraisal Tool for Epidemiology Graphic Architectural Tool for Epidemiology www.epiq.co.nz

  15. The GATE frame © the shape of every epidemiological study

  16. GATE study design (PECOT) P E C O T

  17. a b c d GATE study analyses (EGO & CGO) EG CG

  18. GATE study appraisal (RAMBO) P Recruitment Allocation E C Maintenance Blind or Objective measurements & processes O T

  19. GATE study design (PECOT) P Participants Exposure Group Comparison Group E C Outcomes O Time T

  20. Participants Study Setting Eligible Participants P Participants

  21. Exposure & Comparison Groups Exposure or Intervention Group (EG) Comparison or Control Group (CG) CG EG

  22. a b yes ‘Dis-ease’ c d no Outcomes (O) O Outcomes (O)

  23. Time (T) incidence T prevalence

  24. GATE study analyses

  25. D Denominator (Participants) N Numerator (Outcomes) All epidemiological studies involve measuring the OCCURRENCE of disease Occurrence = Numerator ÷ Denominator O = N÷D

  26. a b c d GATE study analyses Overall Denominator P Denominator 1: Exposure Group EG Denominator 2: Comparison Group CG EG CG Numerator 1: a Numerator 2: b O

  27. a b c d Occurrence = N ÷ D P Denominator 1: Exposure Group EG Denominator 2: Comparison Group CG EG CG Numerator 1: a Numerator 2: b O Exposure Group Occurrence: EGO = a ÷ EG Comparison Group Occurrence: CGO = b ÷ CG

  28. Relative Effect or Risk = EGO ÷ CGO e.g. relative risk (RR), risk ratio, prevalence ratio, incidence ratio Absolute Effect or Risk Difference = EGO - CGO e.g. risk difference (RD), absolute risk Number Needed To Treat (NNT) = 1 ÷ RD Estimating effects & associations involves comparing occurrences

  29. Analyses it’s all about EGO & CGO

  30. a b c d Exposure Group Occurrence: EGO = a ÷ (EG x T) Comparison Group Occurrence: CGO = b ÷ (CG x T) Occurrence = N÷D per unit of time P Denominator 1: Exposure Group EG x T Denominator 2: Comparison Group CG x T EG CG ‘person-time exposure’ Numerator 1= a Numerator 2 = b O

  31. GATE study appraisal (RAMBO) P Recruitment Allocation Blind or Objective measurements & processes E C Maintenance O T

  32. Study appraisal How well was the study done? Was it ok ( or +) or not ok ()? or unclear (?) or not applicable (n/a) ‘no study is perfect!’

  33. RAMBO appropriate Recruitment? participants representative of target population P Study setting & eligibility criteria well described? Recruit random sample OR Recruit consecutive eligibles E C O T ‘appropriateness’ depends on study question

  34. RAMBO P appropriate Allocation process? were EG & CG comparable Allocation process well described? If allocated by investigators: Allocated randomly (e.g drugs) AND Concealed allocation OR If allocated by measurement (e.g. smoking): Adjusted for differences between EG & CG (e.g. statistical or matching) Allocate EG CG O T

  35. P P RCT: Allocate randomly by investigators (e.g drugs) Cohort: Allocate by measurement (e.g. smoking) EG CG EG CG O O T T

  36. RAMBO P good Maintenance? did participants remain in allocated groups (EG & CG) EG CG Participants &/or investigators blind to exposure (and comparison exposure)? Compliance high & similar in EG & CG Contamination low & similar in EG & CG Co-interventions low & similar in EG & CG Completeness of follow-up high & similar in EG & CG O T

  37. RAMBO P Blind or Objective? measurements & processes A Allocation concealed (blind) if randomised EG & CG measurements well described Outcome measurements well described Allocation/Measurement process similar for all participants If measurement not objective (eg. automated or definitive) were assessors blind to exposure (and comparison exposure) EG CG O T

  38. The GATE approach: every epidemiological study hangs on the GATE frame • There is only one basic study design: • Cohort (& case-control) studies - aetiology / prognosis / intervention • RCT (a randomised cohort study)- interventions • Cross-sectional studies - diagnosis

  39. Cohort (follow-up) study: archetypal epidemiological approach P Participants Allocated by measurement (not by randomisation) Exposure Group Comparison Group E C Outcomes O Time T Best design for investigating aetiology (risk), prognosis

  40. Randomised controlled trial - cohort study where exposure allocated by randomisation process P Participants Allocated by randomisation Exposure Group Comparison Group E C Outcomes O Time T Best design for investigating treatments

  41. Case series is a Cohort study with no comparison group P Participants Allocated by measurement Exposure Group E C Outcomes O Time T

  42. Before-after study P Participants Allocated by timing of intervention C Comparison Group E Exposure Group Outcomes O Time T

  43. Cross-over trial P Participants Allocated by randomisation E1 C2 Exposure Group 1 Comparison Group 2 Exposure Group 2 E2 C1 Comparison Group 1 Outcomes O Time T

  44. real-life time Cross-sectional study P Participants Allocated by measurement Exposure Group Comparison Group E C Outcomes O Time T best design for prevalence and diagnostic test accuracy

  45. a b c d Diagnostic test accuracy study P Disease +ve Disease -ve EG CG + O Test - Likelihood +ve test if D+ve: EGO = a ÷ EG Likelihood +ve test if D -ve: CGO = a ÷ CG

  46. a b c d Diagnostic test accuracy study P Disease +ve Disease -ve EG CG + Test O - Likelihood -ve test if D+ve: EGO = c ÷ EG Likelihood -ve test if D -ve: CGO = d ÷ CG

  47. a b c d Diagnostic test for disease prediction P Test +ve Test -ve EG CG + Disease O - Likelihood of D if test +ve: EGO = a ÷ EG Likelihood of no D if test -ve CGO = d ÷ CG Positive predictive value Negative predictive value

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