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Frequency and measures of association

Frequency and measures of association. Center for Clinical Epidemiology and Evidence-Based Medicine (CEEBM) Faculty of Medicine, University of Indonesia – Cipto Mangunkusumo Hospital. Frequency measures. Two types: Someone has the disease already: prevalence

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Frequency and measures of association

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  1. Frequency and measures of association Center for Clinical Epidemiology and Evidence-Based Medicine (CEEBM) Faculty of Medicine, University of Indonesia – Cipto Mangunkusumo Hospital

  2. Frequency measures • Two types: • Someone has the disease already: prevalence = measure population disease status • Someone gets the disease in the future: incidence =measure frequency of disease onset

  3. Measure of disease occurrence (example) • Incidence: the rain arriving • Prevalence: the water in the puddle, new and old • Period prevalence: the water in the puddle, during a period • Point prevalence: at one point of time The water draining away into the soil or into drains reduce the puddle (i.e. the prevalence) just as recovery or death reduce the number of patients with a problem

  4. Prevalence Number of cases of disease at a specific time Population exposed at that time • Proportion of population affected by the disease at a given point in time • Expressed as a percentage: (number of diseased)/(population) * 100

  5. Frequency measures: prevalence • Cross-sectional studies • Determinant and disease measured at the same time • Used in diagnostic research • Prevalence • Number of persons with the disease at a certain moment • Prevalence (%) • Number of persons with the disease / total population

  6. Frequency measures: prevalence • Examples • 50% of the persons with a suspicion of lung cancer had a lesion on the thorax X-ray • In a general practice population of 2500 persons, 50 had asthma • 30% of the Indonesian people smoke

  7. Frequency measures: prevalence • Interpretation / relevance • Quantification amount of disease: a priori probability • public health planning • Issues • non-response • prevalence of MI • prevalence of dementia • selective mortality

  8. New events… • Incidence • Incidence rate • Incidence density • Attack rate • Cumulative incidence • Risk • ……

  9. Frequency measures: Incidence • Incidence • number of new cases • in the population at risk • Two types of incidence • Cumulative incidence • Incidence density (incidence rate)

  10. Frequency measures: Incidence • Used in prognostic research • Incidence density • The number of new disease cases in the population divided by the observation time • Cumulative incidence • new cases in a certain time period in the population at risk (free of the disease at the start) • proportion / probability • varies between 0 and 1 • within certain time period

  11. Frequency measures: Incidence • Cumulative incidence: examples • 5-year risk of a second MI • 10-year survival for women with breast cancer • 1-year risk of a fracture for osteoporotic women

  12. Exercise 1

  13. Exercise 1 Ad question 1: tonsillitis • Dutch population • 1 year • incidence • 19/1000 or 1.9%

  14. Exercise 1 Ad question 2: asthma • Children in the general practice • Certain moment (look into practice data at a certain moment) • (point) prevalence

  15. Exercise 1 Ad question 3: breast cancer • Women • Life • Incidence

  16. Exercise 1 Ad question 4: vertebral collapse • 9% • 55-59 year-old men and women • Certain moment • (point) prevalence

  17. Exercise 1 Ad question 5: fractures • Post-menopausal women • Follow-up duration of the study • Incidence

  18. Frequency measures: Incidence • How do we calculate an incidence?

  19. Frequency measures: Incidence • Cohort approach • Group of persons with the same characteristics • All participants have the same starting point (start cohort) • However, baseline can differ in time • All participants are followed during a certain time period

  20. Cumulative incidence • Cumulative incidence excludes prevalence at baseline • Example: Population 350.000 New cases 1.250 Cumulative incidence 3.6/1000 per year Number of NEW cases of disease during a period Population exposed during the period

  21. Frequency measuresIncidence density • # new patients / person-years of the population at risk • 10 per 1000 person-years • between 0 and infinity Number of new/incident cases Amount of at-risk experience time

  22. Frequency measures:Incidence: cohort • 5 persons followed during a year • (N at risk = 5) • A------------------------------ • B------------------------------ • C-------------breast cancer • D------------------------------ • E------------------------------ • 1-year risk of breast cancer = CI = 1/5=20% per year • ID = 1/4.5 person-years = 222/ 1000 person-years

  23. Frequency measures: example cohort • 13 persons followed for 5 years for mortality • A-----------------------------x--Moves away t=2.5 • B-----------------------------x-------------Death t=3.0 • C-------breast cancer/death t=1.0 • D-----------------------------x------------------------------------------- alive t=5.0 • E-----------------------------x--------lost to follow-up t=3.0 • F-----------------------------x--------------------------------------------alive t=5.0 • G-----------------------------x---------------------------breast cancer/death t=4.0 • H-----------------------------x-Myocardial infarction/death t=2.5 • I--------death t=1.0 • J------------------------------x-------------------------------------------alive t=5.0 • K-------------lost to follow-up t=1.5 • L-----------------------------x----------------moves from the area t=3.5 • M--------1---------------2--x----------3---------------4-------------------alive t=5.0 • Total amount time at-risk = 42 years

  24. Frequency measures: example cohort • CI = 5/13 = 38% • ID = 5/42 x 1000 = 199/1000 person-years

  25. Measures of association • Epidemiology • Disease = f (determinants) • Is the determinant associated with the disease? • Is the probability of disease different for exposed and non-exposed?

  26. Measures of association • Research question? Is smoking associated with lung cancer? • Cohort approach • divide the cohort in smokers and non-smokers • estimate the incidence density (or CI) in each group • prior: ID smokers > ID not smokers

  27. Measures of association Disease Yes No Yes a - PY1 Determinant No c - PY0 ID1 a/py1 ID0 c/py0 RR = =

  28. Measures of association • Smoking and lung cancer Disease Yes No Yes 440 - 22.008 py Determinant No 212 - 21.235 py RR = (440/22.008) / (212/21.235) = 2.0

  29. Measures of association • Risk difference between exposed and non-exposed • CI or ID • public health impact • Risk difference smoking and lung cancer • 20/1000 py - 10/1000 py = 10 / 1000 personyears

  30. Measures of association • Research question: Does smoking increase the risk of lung cancer ? • Case-control study • select cases and controls • Estimate the frequency of smoking among cases and controls • prior: % smokers among cases > % smokers among controls

  31. Measures of association Disease Yes No Yes a b Determinant No c d • RR? • Odds ratio = (a/c) / (b/d) = ad / bc • Odds= the chance of something happening/the chance of it not happening • Odds Ratio - a ratio of two odds

  32. Measures of association • Smoking and lung cancer (controls = 10% random sampling from cohort) Disease Yes No Yes 440 300 740 Determinant No 212 350 562 • Odds ratio (440/212) / (300/350) = 2.42

  33. Measures of association • Smoking and lung cancer Disease Yes No Yes 440 300 740 Determinant No 212 350 562 • RR = (440/740) / (212/562) = 1.57 (shouldn’t be calculated) • Odds ratio (440/212) / (300/350) = 2.42

  34. Measures of association • Smoking and lung cancer Disease Yes No Yes 440 3000 3440 Determinant No 212 3500 3712 • Now entire cohort as control • RR = (440/3440) / (212/3712) = 2.23 • Odds ratio =(440/212) / (3000/3500) = 2.42 • RR (a/(a+b)) / (c/(c+d)) ~ (a/c) / (b/d)

  35. Frequency measures:Therapeutic research • Suppose: you see a patient with an increased blood pressure who you want to treat with blood pressure decreasing drugs. He asks about the effect of this treatment on the prognosis • Research question: Does treatment decrease the probability of CVD?

  36. Frequency measures:Incidence • Intervention study (RCT) • Estimate incidence density (or CI) for each group • prior: ID treated < ID not treated

  37. Exercises 2 and 3

  38. Exercise 2 • People of age 55 years and older • 5 years • Incidence (probably cumulative) • Relative risk and risk difference

  39. Exercise 2 Risksmokers = 41/1736 = 0.024 Risknon-smokers = 107/5949 = 0.018 - RR = 0.024/0.018 = 1.3 Smokers have a 1.3 x higher probability of CVD than non-smokers • RD = 0.024 - 0.018 = 0.006 Smokers have a 5-year risk of CVD that is 0.6% higher than that of non-smokers

  40. Exercise 3 • Case-control study • Severe head injury • Population • Alzheimer’s disease • Odds ratio

  41. Exercise 3 Severe head injury in the past Alzheimer Yes No Severe Yes 33 31 Head injury No 165 167 OR = (33x167)/(31x165)=1.1

  42. SummaryFrequency and measures of association • Frequency • Prevalence • Incidence • cumulative • density • Association • - Relative risk • - Rate ratio • - Risk ratio • - Odds ratio • - Risk difference

  43. Outcome measures • Diagnostics? • Prognostics? • Etiology? • Intervention?

  44. Outcome measures • Diagnostics • Prevalence (abs. risk), posterior probability, Se, Sp, PV+, PV-, OR, AUC • Prognostics • Incidence (abs. risk), OR, AUC • Etiology • Incidence (abs. risk), RR, OR • Intervention • Incidence (abs. risk), RR, RD, mean difference, NNT

  45. Effect estimate • Does a single effect estimate, e.g. RR=1.5 or RR=1.0 give sufficient information?

  46. Effect estimate • No, because it does not tell anything about precision

  47. P-values versus confidence intervals • P-value: The probability that the found association (or more extreme) occurs given the nullhypothesis is true (often with arbitrary cut-off of 5%) • Confidence interval: Range of possible effect estimates that you would find if you would repeat the research (infinitely) often

  48. P-values • Statistical significance (is not the same as clinical relevance) • Dependent on • Size of the effect • Size of the study population

  49. Example • American study on losing weight in obese people • Intervention: • Half an hour per day sports+ diet advice • only half an hour sports • Numbers: 2 x 10.000 people

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