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Risk Assessment

Risk Assessment. FMED 1531 Lecture 9 Spring 2007 Kim Cooper, Ph.D. Exam 1. Lecture Outline. Introduction Comparison of Risk between Groups Other Measures of Risk Impact. Introduction. What do we mean by risk? How is risk assessed? Risk quantification. What do we mean by risk?.

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Risk Assessment

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  1. RiskAssessment FMED 1531 Lecture 9 Spring 2007 Kim Cooper, Ph.D.

  2. Exam 1

  3. Lecture Outline • Introduction • Comparison of Risk between Groups • Other Measures of Risk Impact

  4. Introduction • What do we mean by risk? • How is risk assessed? • Risk quantification

  5. What do we mean by risk? • Risk assessment identifies two types of risks • Risk factors for disease • A factor that correlates with presence of a disease • Often assumed to be contributory to the disease • Risk of treatment side effects • No treatment is absolutely safe

  6. How is risk assessed? • The two most commonly used study types for assessing risk • Cohort studies • Case-control studies • What’s measured in these studies? • Independent variable (cause): risk • May have different “strengths” of exposure • May have different durations of exposure • Dependent variable (effect): outcome (disease or side effect) • May have different severities of outcome

  7. How is risk assessed? • Simplest case is that of a dichotomous risk variable and a dichotomous outcome variable • Data from study clearly summarized in a standard 2 x 2 contingency table

  8. Risk Quantification • In order to compare the risks between groups, the risks and disease must be quantified • Done via incidences calculated from contingency table • Cohort studies • Compare disease incidence in risk exposed group vs. unexposed group • Case-control studies • Compare exposure incidence in diseased group vs. well group

  9. Risk Quantification

  10. Risk Quantification Example • The One Million Woman Study • Conducted in the UK • 1,084,100 women aged 50-64 were recruited between 1996 and 2001 • Subsequently followed for breast cancer and other outcomes

  11. Risk Quantification Example • One of the main risk factors considered was HRT therapy, so compared • 392,757 women who had never used HRT • 285,987 women who were current HRT users • During the follow-up period • 3,202 of the HRT users developed breast cancer • 2,894 of the non-HRT users developed breast cancer

  12. Risk Quantification Example • What is the risk factor? • HRT • What is the outcome? • Death • What type of study is this? • Cohort

  13. Risk Quantification Example

  14. Comparison of Risk between Groups • The disease and exposure incidences can be compared either absolutely or relatively • Absolute risk • Absolute differences in risk • Relative differences in risk • Relative risk (risk ratio) • Odds ratio • Number needed to harm

  15. Absolute Risk • Simplest measure of risk is disease incidence • If disease incidence is (100 new cases/year)/100,000 people • Absolute risk is 1 in 1000 of contracting the disease this year • Example from third lecture: • Breast cancer incidence = 121 new cases/year/100,000 women • Divide top and bottom by 121 • Absolute risk is 1 case for every 826.4 women per year • Can’t use in case-control studies because disease incidence cannot be assessed • Investigator chooses number of disease cases and controls • Usually chosen to be equal so would get a disease incidence of 50% in every case

  16. Absolute Differences in Risk • The simplest comparison between groups in a cohort study is the absolute difference in disease incidence (DI) AR = DIexposed – DIunexposed AR = [a/(a + b)] – [c/(c + d)]

  17. Absolute Difference Example • For the HRT study AR = DIexposed – DIunexposed AR = [a/(a + b)] – [c/(c + d)] AR = [1132/100,000] – [742/100,000] = 390/100,000

  18. Relative Risk (RR) • Compares risk by forming ratio of disease incidence (DI) in the exposed group to that in unexposed group, applies only to cohort studies RR = DIexposed /DIunexposed RR = [a/(a + b)] / [c/(c + d)] • Interpretation: • RR = 1, no evidence that you have identified a risk factor • RR > 1, evidence for a risk factor • RR < 1, evidence for a protective factor

  19. Relative Risk Example • For the HRT study RR = DIexposed /DIunexposed RR = [a/(a + b)] / [c/(c + d)] RR = [1132/100,000)] / [742/100,000] = 1132/742 = 1.53 • Interpretation: • RR > 1, HRT is a risk factor

  20. Odds Ratio (OR) • Remember, above measures can’t be used in case-control studies • Require disease incidence • Disease incidence can’t be calculated in a case-control study • Sampled population by disease state not by exposure state • What can be done? • Another measure of risk, called odds ratio (OR) • Works for both cohort and case-control studies

  21. What are odds? • Odds are just a different measure of probability commonly used in gambling! • Odds of an event: probability of event occurring divided by probability of event not occurring • Example: odds of throwing a six on a die • Probability of throwing a 6 = 1/6 • Probability of not throwing a 6 = 1 – (1/6) = 5/6 • Odds of throwing a six: (1/6)/(5/6) = 1/5 or 1:5 or one-to-five • So you are five times more likely to throw something other than a six than you are to throw a six

  22. Odds Ratio • Definition: OR = ad/bc

  23. Odds Ratio vs. Relative Risk RR = [a/(a + b)] / [c/(c + d)] • If disease is low incidence • a + b ~ b • c + d ~ d • Thus RR = [a/b)] / [c/d)] = ad/bc = OR

  24. OR in a Case-control Study • More correctly called exposure-odds ratio ORE = odds of exposure if you have the disease odds of exposure if you’re disease free • RR with disease incidence replaced with odds of exposure • Odds of having risk factor given you have the disease ORE = (a/c)/(b/d) = ad/bc

  25. Case-control Study Example • Hormone replacement and breast cancer study JAMA 2003 • 975 women with breast cancer • 1007 matched cases without breast cancer • These are the results • 360 of the breast cancer cases used HRT • 284 of the breast cancer cases had never used HRT • 397 of the controls used HRT • 339 of the controls never used HRT

  26. OR in a Case-control Study • Let’s construct a contingency table for the data 360 397 284 339 ORE = (360)(339)/(284)(397) = 1.08

  27. OR in a Cohort Study • More accurately called disease-odds ratio ORD = odds of having the disease if you were exposed d odds of having the disease given you weren’t exposed • Same as definition of relative risk, but with incidence replaced with odds ORD = ad/bc • Same answer as cohort definition, but notice difference in interpretation • This distinction is not always noted

  28. OR in a Cohort Study Example • For the HRT study ORD = ad/bc ORD = (3,202)(389,863)/(2,894)(282,785) (1.11) (1.38) = 1.53 • Note this is essentially the same as the RR, because there is such a low incidence of breast cancer in both groups

  29. Number Needed to Harm • New way of expressing harm developed in EBM • More intuitive and concrete • Can be computed from AR or OR • In terms of AR NNH = 1/AR • In terms of OR, the formulae are more complicated

  30. Number Needed to Harm • HRT example • From the HRT data we found that the AR of breast cancer from HRT was AR = 390/100,000 NNH = 1/AR = 256.4! • Interpretation: 256 women would have to be treated with HRT before there would be one additional case of breast cancer

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