Statistical Analysis for Educational Outcomes Measurement in CME

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Statistical Analysis for Educational Outcomes Measurement in CME

  1. 1. Statistical Analysis for Educational Outcomes Measurement in CME Jason J. Olivieri, MPH Director, Outcomes Services Imedex, LLC (404) 319-9782 j.olivieri@imedex.com www.assessCME.wordpress.com
  2. 2. Statistical Questions in CME 1. Was there an educational effect? 2. If so, how big was the effect? 3. How does this effect compare with other CME activities?
  3. 3. Was there an educational effect? Statistical tests of significance • Determine whether any observed differences (e.g., pre- vs. post) are due to random chance. • Observed differences not attributed to random chance are considered “statistically significant” and reflect a true difference between groups that could be associated with participation in this educational activity. • Statistical significance is reported as a “P value”. A P value ≤ .05 is considered statistically significant. Generally speaking, a P value ≤ .05 means that there is a 5% chance or less that the result of a particular comparison is due to random chance.
  4. 4. Statistical tests of significance: Choosing the right test • Define variable type (typically either categorical or ordinal) • Is the comparison group data (e.g., pre vs. post) paired or unpaired? • What is the sample size?
  5. 5. Statistical tests of significance • Categorical variables (e.g., knowledge “test” question) – Unpaired data (comparison groups not matched) • Chi square (samples ≥ 5) • Fisher’s exact test (samples < 5) – Paired data (matched comparison groups) • McNemar’s test
  6. 6. Knowledge Change Which of the following is NOT a complication associated with bisphosphonate therapy? 19% 10% 6% 17% 48% 8% 4% 2% 1% 85% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Arm pain Renal dysfunction Osteonecrosis of the jaw Atypical fx of the femur Peripheral neuropathy (correct) Pre-activity (n = 111) Post-activity (n = 157) 37% increase
  7. 7. Competency Change (case vignette) Frontline therapy for a former smoker with symptomatic advanced stage adenocarcinoma of the lung (EGFR+) 51% 12% 19% 12% 6% 75% 6% 3% 9% 6% 0% 10% 20% 30% 40% 50% 60% 70% 80% Erlotinib alone (evidence-based, preferred) Erlotinib + bevacizumab Carboplatin-paclitaxel & bevacizumab Carboplatin-pemetrexed & bevacizumab Full house: erlotinib + carboplatin-paclitaxel & bevacizumab Control (n = 65) Participants (n = 65) 24% increase
  8. 8. Statistical tests of significance • Ordinal variables (e.g., rating scale) – Unpaired data (comparison groups not matched) • Mann-Whitney U – Pair data (matched comparison groups) • Wilcoxon signed-rank test
  9. 9. 5.9 6 6.5 5.8 5.6 4.8 6.3 4.4 4.3 4.7 3.2 3.8 2.9 5.5 0 1 2 3 4 5 6 7 Achieving hemostatis with Coblation for adenotonsillectomy Using Coblation for adenoidectomy Using Coblation for complete tonsillectomy Using Coblation for partial tonsillectomy Using a microdebrider for adenoidectomy Using a microdebrider for intracapsular tonsillectomy Selecting an appropriate surgical technique for adenotonsillectomy for specific indications in patients Confidence in performing seven clinical tasks in adenotonsillectomy (1 = not at all confident, 7 = extremely confident) Pre (n = 57) Post (n = 49) Knowledge (self-efficacy) Change
  10. 10. Competency Change Using bevacizumab-based combo therapy for non-squamous NSCLC 32% 9% 41% 14% 5% 14% 18% 23% 32% 14% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 1 = Never Not very often Sometimes Often 5 = Always Current vs. planned use of bevacizumab-based combo therapy in nonsquamous NSCLC Current (mean = 2.5, n = 44) Planned (mean = 3.1, n = 39) Overall 26% increase in planned vs current use
  11. 11. Once you know which statistical test to use, where do you go? www.VassarStats.net
  12. 12. Statistical test of significance: Categorical variable example •Participants in a CME activity were administered a paper-based pre- and post-activity survey which contained five knowledge “test” questions based on the CME content •Survey participant responses were anonymous (i.e., pre/post not matched) •You want to determine if the proportion of respondents answering the question correctly pre- vs. post-activity is significantly different
  13. 13. Knowledge Change Which of the following is NOT a complication associated with bisphosphonate therapy? 19% 10% 6% 17% 48% 8% 4% 2% 1% 85% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Arm pain Renal dysfunction Osteonecrosis of the jaw Atypical fx of the femur Peripheral neuropathy (correct) Pre-activity (n = 111) Post-activity (n = 157) 37% increase
  14. 14. Statistical tests of significance • Categorical variables (e.g., knowledge “test” question) – Unpaired data (comparison groups not matched) • Chi square (samples ≥ 5) • Fisher’s exact test (samples < 5) – Paired data (matched comparison groups) • McNemar’s test
  15. 15. Calculating significance for a categorical variable • Determine # of correct / incorrect answers for each group (e.g., pre- and post-activity) – Pre-activity: .48*111 = 53 correct / 58 incorrect – Post-activity: .85*157 = 133 correct / 25 incorrect • Plug these values into online calculator (www.vassarstats.net) – Click “frequency data” – Click “Chi-Square, Cramer’s V and Lambda”
  16. 16. Knowledge Change Which of the following is NOT a complication associated with bisphosphonate therapy? 19% 10% 6% 17% 48% 8% 4% 2% 1% 85% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Arm pain Renal dysfunction Osteonecrosis of the jaw Atypical fx of the femur Peripheral neuropathy (correct) Pre-activity (n = 111) Post-activity (n = 157) 37% increase The proportion of respondents answering this question correctly pre- vs. post activity was not likely due to random chance, P < . 0001
  17. 17. Statistical test of significance: Ordinal variable example •Participants in a CME activity were asked via ARS to rate their pre- and post-activity use of five clinical practice strategies tied to the CME content •Survey participant responses were anonymous (i.e., pre/post not matched) •You want to determine whether the difference in rating pre- vs. post- activity is significantly different
  18. 18. Competency Change Using bevacizumab-based combo therapy for non-squamous NSCLC 32% 9% 41% 14% 5% 14% 18% 23% 32% 14% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 1 = Never Not very often Sometimes Often 5 = Always Current vs. planned use of bevacizumab-based combo therapy in nonsquamous NSCLC Current (mean = 2.5, n = 44) Planned (mean = 3.1, n = 39) Overall 26% increase in planned vs current use
  19. 19. Statistical tests of significance • Ordinal variables (e.g., rating scale) – Unpaired data (comparison groups not matched) • Mann-Whitney U – Pair data (matched comparison groups) • Wilcoxon signed-rank test
  20. 20. Calculating significance for an ordinal variable • Go to www.vassarstats.net – Click “ordinal data” – Click “Mann-Whitney U test” • Enter in the total number of pre-activity (“sample A”) and post- activity (“sample B”) respondents • Copy and paste pre- and post-activity responses into “sample A” and “sample B” boxes • Click “Import data to data cells” • Click “Calculate from Raw Data”
  21. 21. Competency Change Using bevacizumab-based combo therapy for non-squamous NSCLC 32% 9% 41% 14% 5% 14% 18% 23% 32% 14% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 1 = Never Not very often Sometimes Often 5 = Always Current vs. planned use of bevacizumab-based combo therapy in nonsquamous NSCLC Current (mean = 2.5, n = 44) Planned (mean = 3.1, n = 39) Overall 26% increase in planned vs current use The difference between pre- and post-activity rating was statistically significant, P = . 02
  22. 22. That’s it! You’re now able to answer whether there was an educational effect. Now onto quantifying the magnitude of such effect…
  23. 23. CME activity EOM plan Pre- vs. post-activity assessment via ARS or paper survey Data 8 case vignette or clinical practice strategy questions Didactic presentation followed by case-based discussion
  24. 24. Example paper survey question (clinical practice strategy) Using bevacizumab-based combo therapy for non-squamous NSCLC
  25. 25. CME activity EOM plan Pre- vs. post-activity assessment via ARS or paper survey Data 8 case vignette questions or clinical practice strategies Didactic presentation followed by case-based discussion How do we summarize this data? And how do we then compare this result to results of other activities?
  26. 26. What is effect size? •Quantifies the magnitude of effect (maximum expected range: -3 to +3) •Difference in means (e.g., pre-test and post-test) divided by the square root of the pooled-group variances (Cohen’s d) •Enables the comparison of CME effects across activities on a common dimensionless scale •Calculated from comparison data (e.g., pre/post, post/control) linked directly to CME content −Knowledge questions −Case vignettes −Self-reported frequency of use of key clinical practice strategies
  27. 27. How is it calculated?
  28. 28. Calculating effect size • Can be done using only MS Excel® and free, online resources • Approach dependent upon variable type: – ordinal (e.g., clinical practice strategy) – categorical (e.g., case vignette)
  29. 29. Example paper survey question (clinical practice strategy) Using bevacizumab-based combo therapy for non-squamous NSCLC
  30. 30. Calculating effect size for an ordinal variable (e.g., clinical practice strategy) • Calculate average and standard deviation for each group (e.g., pre- and post-activity) – Pre-activity: mean (SD) = 2.5 (1.3) – Post-activity: mean (SD) = 3.1 (1.2) • Plug these values into an online calculator (http://www.uccs.edu/~lbecker/)
  31. 31. Clinical practice strategy (CPS) use rating (1= never, 5 = always) Pre-test Post-test Effect size CPS #1 Standard deviation CPS #1 Standard deviation Cohen’s d 2.5 1.3 3.1 1.2 .48 *d = .2 (small effect), d = .5 (medium effect), d = .8 (large effect) How is effect size interpreted? Ordinal variable example How big was the educational effect? Expressed in standard deviation units: The average score of a post-test respondent was .48 standard deviations above the average score of a pre-test respondent Effect sizes are proportional (.48 is twice as much effect as .24) How does the effect compare to other activities? Cohen (1988): .2 = small, .5 = medium, .8 = large Wolf (1986): .25 = educationally significant, .50 = clinically significant
  32. 32. Example ARS question (case vignette) Frontline therapy for a former smoker with symptomatic advanced stage adenocarcinoma of the lung (EGFR+)
  33. 33. Calculating effect size for a categorical variable (e.g., case vignette) • Determine # of correct / incorrect answers for each group (e.g., pre- and post-activity) – Pre-activity: .51*65 = 33 correct / 32 incorrect – Post-activity: .75*65 = 49 correct / 26 incorrect • Plug these values into online calculator (www.vassarstats.net) – Click “frequency data” – Click “Chi-Square, Cramer’s V and Lambda”
  34. 34. Calculating effect size for a categorical variable, continued • Visit www.lyonsmorris.com/ma1/index.cfm • Select “Correlation coefficient (r) to Effect Size” • Enter Cramer’s V (.1474) • Enter total number of pre-activity and post-activity respondents
  35. 35. How effective was your live CME in 2013? 10 live CME activities 8 ARS questions per activity 80 slides 10 effect sizes One summary effect size for live CME
  36. 36. Overall EIS (by format) June 2010 – Sept 2013 n=9 1. Cohen. J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates. 2. Mansouri & Lockyer. J Contin Educ Health Prof 2007;27:6-15. 3. Drexel et al. Int J Chron Obstruct Pulmon Dis 2011; 6: 297–307. 4. Casebeer et al. BMC Med Educ 2010;10: 42. 4Casebeer et al 2010. Knowledge effect size (eLearning) = .82 3Drexel et al 2011. Competence effect size = .85 2Mansouri & Lockyer 2007. Knowledge effect size = .6 1Cohen J 1988. Small effect = .2, Medium effect = .5, Large effect = .8
  37. 37. Questions? Resource: www.assessCME.wordpress.com

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