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Chris and Abbie’s Vital Statistics

Chris and Abbie’s Vital Statistics. Arghhh Statistics. It’s actually quite dull!!! But......It is quite easy if you think it through. Arghhh Statistics. Some Definitions…. Arghhh Statistics. The probability that something will occur…In a particular group. Risk:. Arghhh Statistics.

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Chris and Abbie’s Vital Statistics

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  1. Chris and Abbie’s Vital Statistics

  2. Arghhh Statistics... • It’s actually quite dull!!! • But......It is quite easy if you think it through.

  3. Arghhh Statistics... • Some Definitions…

  4. Arghhh Statistics... The probability that something will occur…In a particular group • Risk:

  5. Arghhh Statistics... The probability that something will occur in one group compared to another group. • Risk Ratio:

  6. Arghhh Statistics... The chance of something occurring compared to it not occurring… • Odds:

  7. Arghhh Statistics... The ratio of the odds of something occurring in one group compared to odds of it occurring in a different group • Odds Ratio…

  8. Arghhh Statistics... Number of people needed to treat to have one given outcome.... = 1 ÷ Absolute Risk Reduction Number needed to Treat…

  9. Arghhh Statistics... • An example…. Sausage Addicts! • A study over one year.

  10. Risk • What is the risk of having a heart attack in the sausage eating group?

  11. Risk in sausage group • Risk = Number of heart attack/total population = 25/1700 = 0.0147 (1.47%)

  12. Risk in non sausage group • Risk = Number of heart attack/total population = 12/1800 = 0.00667 (0.67%)

  13. Relative Risk • Relative Risk in one group/risk in the other group. = 0.147/0.0067 = 2.19

  14. Absolute Risk = (25/1700) - (12/1800) = 0.0080 (0.8%) Absolute risk = (Risk in sausage eaters) – (Risk in non sausage eaters)

  15. Number needed to harm • Number needed to harm = 1 ÷ Absolute risk reduction = 1/0.008 = 125 i.e. if 100 people ate sausages for 1 year an additional 0.8 would have a heart attack. If 1000 people ate sausages for 1 year an additional 8 would have a heart attack. 1000/8 = 125....So 125 people are needed to eat sausages for 1 year to get one additional heart attack.

  16. Odds of heart attack in sausage group = 25/1675 = 0.0149. i.e. For every person who does not have a heart attack 0.0149 of a person does have a heart attack. Heart attack ÷ no heart attack

  17. Odds of heart attack in non sausage group Heart attack ÷ no heart attack 12/1788 = 0.00671. i.e. For every person who does not have a heart attack 0.00671 of a person does have a heart attack. (its less)

  18. Odd Ratio • Odds of Heart attack in sausage eaters: 0.0149 • Odds of Heart attack in none sausage eaters: 0.00671 Odds ratio 0.00671/0.0149 = 2.22 So eating sausages doubles your odds of having a heart attack in one year...

  19. Arghhh more stats….! • And Now Abbie…..!

  20. Sensitivity and Specificity • Why Bother? • If disease is present a truly accurate, test will always give a positive result • If disease is not present, the test will always give a negative result.

  21. Sensitivity… • … • the proportion of people with disease who have a positive test result = true positive (true positive + false negative)

  22. Specificity • … • the proportion of people without disease who have a negative test result = true negative (true negative + false positive)

  23. An example False positive True positive False negative True negative

  24. Work it out… 35/36 = 0.97 (97%) • Sensitivity = • Specificity = 40/45 = 0.89 (89%)

  25. Clinically… PSA for prostate cancer Sensitivity is 46% Specificity is 91% Ca125 for ovarian cancer Sensitivity is 72% Specificity is 78%

  26. Predictions… PPV : proportion of positive test results that are true positives = number of true positives No. of true positives + no. of false positives

  27. Continued… NPV : proportion of negative test results that are true negatives = number of true negatives No. of true negatives + no. of false negatives

  28. An example False positive True positive False negative True negative

  29. Worked example… • PPV = • NPV = 35/40 = 0.875 (88%) 40/41 = 0.975 (98%)

  30. Papers… • Meta analysis • Systematic review • Can anyone tell me the difference? “Science is cumulative, with new ideas being based on previous knowledge and observation, and new advances in science should help us make sense of what we already know and have observed. But if we don't collect previous knowledge and observation in a systematic way, we are unlikely to make progress as quickly as we could.” The Cochrane Collaboration open learning material http://www.cochrane-net.org/openlearning/html/mod1-2.htm

  31. Meta-analysis • Calculating the results of each study identified by the reviewer, and then to calculate an average of those results in a meta-analysis. • Systematic reviews do not have to have a meta-analysis - there are times when it is not appropriate or possible and vice-versa • We tend to use forest plots to present the results of a meta-analysis…

  32. Forest Plots • Forest plots show the information at a glance from the individual studies that went into the meta-analysis. • It provides a simple visual representation of the amount of variation between the results of the studies, as well as an estimate of the overall result of all the studies together.

  33. Can someone talk me through it?

  34. Give papers out…

  35. Paper to work on… • Can you decipher the forest plot?

  36. And again…

  37. And now for some fun… • CRITICAL APPRAISAL!

  38. Critical Appraisal

  39. Critical Appraisal

  40. Critical Appraisal

  41. Critical Appraisal

  42. Critical Appraisal Critical Appraisal Skills Programme (CASP)

  43. The Answers…

  44. The Answers…

  45. The Answers…

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