Estimating Risk PDF

Estimating Risk Objectives of Epidemiology  To determine rates of disease by person, place, and time  Absolute Risk  Incidence, Prevalence  To identify the risk factors for the disease  Relative Risk, Odds Ratio  To develop approaches for disease prevention  Attributable Risk Absolute Risk Ab...

Estimating Risk Objectives of Epidemiology  To determine rates of disease by person, place, and time  Absolute Risk  Incidence, Prevalence  To identify the risk factors for the disease  Relative Risk, Odds Ratio  To develop approaches for disease prevention  Attributable Risk Absolute Risk Absolute Risk  The likelihood of an individual in a defined population developing a disease or adverse health problem  The incidence of a disease in a population  Can indicate the magnitude of risk in a group with a certain exposure  Your risk of developing a disease over a period of time Incidence: Measures the occurrence of new cases in a population over time Purpose: Indicator of the force of the disease or the conversion of health status Cumulative Incidence Incidence Density # new cases during follow-up # of people at the start of follow-up # new cases during follow-up Accumulated person-time Absolute Risk  The likelihood of an individual in a defined population developing a disease or adverse health problem  The incidence of a disease in a population  Can indicate the magnitude of risk in a group with a certain exposure  Your risk of developing a disease over a period of time  Does not indicate whether the exposure is associated with an increased risk of the disease, because there is no comparison Absolute Risk Examples  Congenital rubella appears in 80-100% of infants if maternal infection of rubella occurs during the first trimester1  Vertical transmission of parvovirus B19 is ~35%1  ~90% of infants perinatally or infected during the first year of life develop chronic hepatitis B virus infection1 * 1 Implicit comparison made, but to address whether an association exists – we need to make explicit comparisons Neu et al. (2015) Does an Association Exist?  Must determine whether there is an excess risk of disease among people who have been exposed to a certain agent  The stronger the association, the more likely the relationship is causal in absence of known biases  May be misleading due to confounding Measures of Associations  Cohort and case control studies are designed to address the association between exposure and disease  Association involves the comparison between two measures  Cohort Studies: Relative Risk  Case Control Studies: Odds Ratio Steps in Epidemiologic Research  Generate a research question of interest  Define the population of interest  Conceptualize and create measure of exposures and health outcomes  Take a sample of the population  Estimate measures of association between exposures and health outcomes  Rigorously evaluate whether the association observed suggests a causal association  Assess the evidence for causes working together  Assess the extent to which the result matters, is externally valid, to other populations Contingency Table: 2 by 2 Table  Important tool in evaluating the association between exposure and disease  Cross-classifies exposure status and disease status  Represents the association between exposure and disease status  Columns represent disease or outcome status (yes/no)  Rows represent exposure status (yes/no) Exposure Status 2 by 2 Table Exposure Present No Exposure Disease Status People with Disease People without Disease A B Exposure & Disease Present Exposure Present, No Disease C D No Exposure, Disease Present No Disease, No Exposure A+C B+D Total of Diseased Total of Nondiseased A+B Total Exposed C+D Total Unexposed N Sample Total Relative Risk Relative Risk  Probability of an event occurring in exposed people compared to the probability of the event in non-exposed people  Measures the size of the effect on the disease rates of the risk factor and the strength of the association in epidemiology  Cannot be calculated from case control studies  No incidence data in the exposed or non-exposed  Odds ratio can provide an acceptable estimate of relative risk  Ratio of two incidence rates Relative Risk Cumulative Incidence in Exposed Cumulative Incidence in Unexposed Exposure Status Measure of Association in Cohort Studies: Disease Status RR A B C D A / (A+B) C / (C+D) Relative Risk Interpretation RR=1 No evidence of association Risk exposed = Risk unexposed RR>1 Positive association Risk exposed > risk unexposed RR Coffee consumption is associated with 5 times higher risk of anxiety Statistically Significant?  Sample, by chance, may not represent exact disease and exposure experience of the population  Confidence intervals can help us understand the variability in the study estimates due to chance in sampling process  How confident are you of that your estimated measure of association reflects the true measure of association?  95% Confidence Interval  Range of values in which you can be 95% certain contains the true estimate  There is 95% chance that the confidence interval calculated contains the true measure of association  If the 95% CI contains the null value of 1.00, then the results are NOT statistically significant and should be interpreted as no association between exposure and outcome.  RR = 2.1, 95% CI (0.7-2.4)  Not statistically significant! Interpret as there is no association between exposure and disease 95% Confidence Interval for RR 1. Take the natural log of RR ln(RR) 2. Estimate standard error (SE) 1 1 1 1 A (A + B) C (C + D) 3. Estimate upper and lower bounds on log scale ln(RR) - 1.96(SE) 4. Exponentiate upper and lower bounds eLowerBound eUpperBound ln(RR) + 1.96(SE) Let’s revisit Relative Risk Example 2 to determine whether associations are statistically significant! Research Objective: To examine whether history of sexual abuse is associated with suicide attempts in adolescents Relative Risk Example 2 RR Deykin & Buka (1994) History of Sexual Abuse Suicide Attempt 14 49 9 149 2.46 1 1 1 1 A (A + B) C (C + D) 1 1 1 1 14 (23) 49 (198) SE 0.21 60.8% 24.7% LB ln(RR) - 1.96(SE) ln(2.46) - 1.96(0.21) 0.49 UB ln(RR) + 1.96(SE) ln(2.46) + 1.96(0.21) 1.31 LB UB e(LB) e(0.49) 1.63 e(UB) e(1.31) 3.71 RR=2.46, 95% CI 1.63-3.71 Considerations  Central Limit Theory  Validity of confidence interval dependent on the central limit theory  Assumptions  Large sample size  Each cell must have at least 5 participants Attributable Risk Attributable Risk  Clinical and Public Health Impact  How much of the disease that occurs can be attributed to a certain exposure?  Amount of disease incidence (risk) that can be attributed to a specific exposure  How much of the risk of disease can we prevent if we eliminate the exposure Attributable Risk  Attributable Risk (AR) is the amount of the incidence of disease in the exposed that is due to the exposure  It is the incidence of a disease in the exposed that would be eliminated if exposure was removed  Difference in incidence of disease between exposed and unexposed individuals  Incidence in unexposed = background risk AR Ie Iu Incidence of Exposed Incidence of Unexposed Attributable Risk Percent  AR% is the percent of the incidence of a disease in the exposed that is due to the exposure  It is the percentage or proportion of the incidence of a disease in the exposed that would be eliminated if exposure was eliminated AR% AR Iu Ie Ie 100 Population Attributable Risk (PAR)  Represents the amount of the outcome (in a specified time) in the whole population that may be prevented if a cause of the outcome was totally eliminated Attributable Risk 35% 30% Type II Diabetes 25% 23% 20% 15% 10% 5% 7% 0% Overweight/Obese 7% Normal Weight Attributable Risk Example 1  Attributable Risk = Risk of type II diabetes attributed to being overweight/obese  AR = Type II diabetes among overweight/obese individuals – Type II diabetes among normal weight individuals  AR = 30 per 100 overweight/obese individuals – 7 per 100 normal weight individuals  AR = 23 of the 30/100 instances of type II diabetes among individuals who are overweight/obese are attributed to the fact that they are overweight/obese Attributable Risk 35% 30% Type II Diabetes 25% 23% 20% Due to being overweight/obese Background rate 15% 10% 5% 7% 0% Overweight/Obese 7% Normal Weight Attributable Risk Example 1  Attributable Risk = Risk of type II diabetes attributed to being overweight/obese  AR = Type II diabetes among overweight/obese individuals – Type II diabetes among normal weight individuals  AR = 30 per 100 overweight/obese individuals – 7 per 100 normal weight individuals  AR = 23 of the 30/100 instances of type II diabetes among individuals who are overweight/obese are attributed to the fact that they are overweight/obese  Attributable Risk Percent = 23 30 100 77%  77% of type II diabetes among overweight/obese group may be attributed to being overweight/obese and could presumably be prevented by decreasing adiposity to that of the normal weight category Attributable Risk Example 2 Stroke Incidence Fish Consumption Totals Almost daily RR 1 82 1549 1631 Never * Sauvaget et al (2003) 23 779 Incidence in non-fish eaters Incidence in fish eaters 802 5.03 2.87 82 1631 5.03 per 100 23 802 2.87 per 100 Incidence in fish eaters 105 2433 4.32 per 100 Incidence in population 1.75 Incidence in nonfish eaters Attributable Risk Example 2 5.03 per 100 Incidence in nonfish eaters AR = Inofish – Ifish AR% = 2.87 per 100 4.32 per 100 Incidence in fish eaters Incidence in population = 5.03 – 2.87 = 2.16 per 100 = AR Inofish(exp) 2.16 5.03 100 43% PAR = Ipop – Iunexp = 4.32 – 2.87 = 1.45 per 100 PAR% = PAR Ipop = 1.45 4.32 100 33.6% Interpretations  RR = 1.75  Individuals who never eat fish have 1.75 times higher risk of stroke as those who eat fish almost daily  AR = 2.16 per 100  If those who do not eat fish change their eating habits and begin to eat fish almost daily, then their incidence of strokes will decrease by 2.16 per 100 individuals  AR% = 43%  There would be a 43% reduction in stroke incidence if exposure, no fish consumption, were eliminated among nonfish eaters  This represents a 43% reduction in stroke incidence among the exposed individuals (nonfish eaters) Interpretations  PAR = 1.45 per 100  A reduction of 1.45 new cases of stroke per 100 population is expected if everybody eats fish almost daily  PAR% = 33.6%  There would a reduction of 33.6% in the incidence of stroke in the population if everyone eats fish almost daily Attributable Risk for Lung Cancer in Springfield Lung Cancer Location of Residence Totals Springfield 20 80 100 AR = ISpringfield – IShelbyville = 20 – 10 = 10 AR% = Shelbyville 10 90 100 AR ISpringfield = 10 20 100 50%  50% of lung cancer cases can be attributed to living in Springfield  If we removed Springfield (the exposure) then we could reduce the risk of lung cancer by 50% Attributable vs. Relative Risk  Relative Risk  Measure of the strength of the association, which is an important consideration in deriving a causal inference  Valuable in etiologic studies of disease, whereas the attributable risk has major applications in clinical practice and public health  Attributable Risk  Measure of how much the disease risk is attributable to a certain exposure Attributable vs. Relative Risk Age adjusted death rates per 100,000 Lung cancer CHD Smokers Nonsmokers RR AR AR% 140 10 14.0 130 92.9 669 413 1.6 256 38.3  256 deaths per 100,000 from CHD would be prevented if smokers would no longer smoke  130 deaths per 100,000 from lung cancer would be prevented if smokers would no longer smoke ? Why are there fewer deaths prevented for lung cancer if smoking was eliminated when the proportion of deaths attributed to smoking is higher among lung cancer patients (92.9%) compared to CHD (38.3%) Attributable vs. Relative Risk  Prevented: 256 deaths per 100,000 from CHD  Prevented: 130 deaths per 100,000 from lung cancer ? Why are there fewer deaths prevented for lung cancer if smoking was eliminated when the proportion of deaths attributed to smoking is higher among lung cancer patients (92.9%) compared to CHD (38.3%)  Mortality level (number of people) in smokers is much higher for CHD (669) than for lung cancer (140)  Attributable risk (the difference between total risk in smokers and the background risk) is much greater for CHD than for lung cancer Odds Ratio Odds Ratio  Can be calculated in case control and cohort studies  Appropriate measure of association for studies in which individuals with or without the disease are recruited and then there is a retrospective assessment of exposure status  Case Control Study  The proportion of cases who were exposed compared to the proportion of controls who were exposed  Cohort Study  Proportion of exposed with disease development compared to the proportion of unexposed with disease development Cohort studies can also calculate an OR, because an OR does not require the establishment of temporality. However, a case control (unless it is a nested case control or a case-cohort) cannot calculate a RR, because it lacks the ability to establish temporality. Although cohort studies can calculate both measures of association, when you think of a cohort study, think of a RR !! Probability and Odds Probability Measures the number of times the outcome of interest occurs relative to the number of observations (sample size) Odds Measures the number of times the outcome occurs relative to the number of times it does not Odds Ratio Compares the odds of those exposed to the risk factor and the odds of those unexposed Risk vs. Odds  The chance of something happening can be expressed as a risk and/or as an odds Risk Odds Chances of something happening Chances of ALL things happening Chances of something happening Chances of it NOT happening If I choose a student randomly from a class size of 6, how likely is it that I will choose you? Risk Odds Chances of something happening 1 Chances of ALL things happening 6 Chances of something happening 1 Chances of it NOT happening 5 0.16 0.20 Odds Ratio in Case Control Studies  Ratio of the odds that a case was exposed compared with the odds that a control was exposed Odds Ratio Odds of Exposure in Diseased Odds of Exposure in Nondiseased Calculating Odds Ratio for a Case Control Disease What are the odds that a case was exposed Exposure Odds by disease group A B Cases Controls Number of times exposure occurs among cases C ? A C D Number of times exposure occurs among controls B D Calculating Odds Ratio for a Case Control Odds ratio Disease Exposure in diseased group versus nondiseased group Exposure Number of times exposure occurs among cases A B A C Number of times exposure occurs among controls B D C D A C AD B BC D Odds Ratio Sample Calculation of an Odds Ratio Gastric Cancer Population-based case control study in Mexico City of the relationship between chili pepper consumption and gastric cancer risk Chili Pepper Consumption A 1 204 552 Lopez-Carillo et al (1994) C AD B BC D 9 145 204×145 552×9 Odds Ratio 5.95 Interpretation of the Odds Ratio OR=1 No evidence of association Odds of exposure among cases = Odds of exposure among controls OR>1 Positive association Odds of exposure in cases > Odds of exposure in controls OR 204×145 552×9 Odds Ratio 5.95 Gastric cancer individuals have a 6-fold higher odds of chili pepper consumption compared to controls Are the findings statistically significant? 95% Confidence Interval for Odds Ratio 1. Take the natural log of OR ln(OR) 2. Estimate standard error (SE) 1 1 1 1 A B C D 3. Estimate upper and lower bounds on log scale ln(OR) - 1.96(SE) 4. Exponentiate upper and lower bounds eLowerBound eUpperBound ln(OR) + 1.96(SE) Relative Risk Example 2 Chili Pepper Consumption Gastric Cancer SE 204 552 9 145 0.35 Lopez-Carillo et al (1994) OR 552×9 1 1 1 1 A B C D 1 1 1 1 204 552 9 145 LB ln(OR) - 1.96(SE) ln(5.95) - 1.96(0.35) 1.10 UB ln(OR) + 1.96(SE) ln(5.95) + 1.96(0.35) 2.47 LB UB 1 204×145 e(LB) e(1.10) 3.00 e(UB) e(2.47) 11.82 5.95 OR=5.95, 95% CI 3.00, 11.82 Are the findings statistically significant? Yes! The 95% CI does not include the null value of 1.00. We would interpret this finding as, “Gastric cancer is associated with ~6-times higher odds of chili pepper consumption.” Odds Ratio Question Disease Exposure  Investigators want to determine whether vitamin D deficiency was associated with birth defects. By reviewing the birth certificates during a single year, the researcher located 189 infants born with NTDs. A total of 600 other births were selected at random from the certificates. Mothers were given a dietary questionnaire. Among mothers who gave birth to an infant with an NTD, 84 reported no use of supplementary vitamins; a total of 137 control mothers did not use a vitamin supplement. Construct the appropriate 2 by 2 table and calculate the OR between vitamin use and NTDs. Are the results statistically significant? A B C D Odds Ratio Question Vitamin Supplementation  Investigators want to determine whether vitamin D deficiency was associated with birth defects. By reviewing the birth certificates during a single year, the researcher located 189 infants born with NTDs. A total of 600 other births were selected at random from the certificates. Mothers were given a dietary questionnaire. Among mothers who gave birth to an infant with an NTD, 84 reported no use of supplementary vitamins; a total of 137 control mothers did not use a vitamin supplement. Construct the appropriate 2 by 2 table and calculate the OR between vitamin use and NTDs. Are the results statistically significant? NTDs Odds Ratio Question Vitamin Supplementation  Investigators want to determine whether vitamin D deficiency was associated with birth defects. By reviewing the birth certificates during a single year, the researcher located 189 infants born with NTDs. A total of 600 other births were selected at random from the certificates. Mothers were given a dietary questionnaire. Among mothers who gave birth to an infant with an NTD, 84 reported no use of supplementary vitamins; a total of 137 control mothers did not use a vitamin supplement. Construct the appropriate 2 by 2 table and calculate the OR between vitamin use and NTDs. Are the results statistically significant? NTDs 105 463 84 137 189 600 AD 105×137 BC 463×84 0.37 NTDs  Investigators want to determine whether vitamin D deficiency was associated with birth defects. By reviewing the birth certificates during a single year, the researcher located 189 infants born with NTDs. A total of 600 other births were selected at random from the certificates. Mothers were given a dietary questionnaire. Among mothers who gave birth to an infant with an NTD, 84 reported no use of supplementary vitamins; a total of 137 control mothers did not use a vitamin supplement. Construct the appropriate 2 by 2 table and calculate the OR between vitamin use and NTDs. Are the results statistically significant? > Vitamin Supplementation Odds Ratio Question AD BC 105 463 84 137 189 600 105×137 463×84 0.37 The odds of NTDs is 63% lower among infants with mothers who took vitamin supplementation during pregnancy. Odds Ratio Question Vitamin Supplementation NTDs 105 84 137 0.18 Lopez-Carillo et al (1994) 463×84 1 1 1 1 A B C D 1 1 1 1 105 463 84 137 LB ln(OR) - 1.96(SE) ln(0.37) - 1.96(0.18) -1.35 UB ln(OR) + 1.96(SE) ln(0.37) + 1.96(0.18) -0.64 LB UB 1 OR SE 463 105×137 e(LB) e(-1.35) 0.26 e(UB) e(-0.64) 0.53 0.37 OR=0.37, 95% CI 0.26-0.53 Interpreting ORs Which of the following statements is NOT correct regarding the interpretation of the results? A) No association was observed between living with at least one household smoker during the postnatal period and children ever having or currently having asthma B) No association was noted between maternal smoking during pregnancy and children ever having or currently having asthma. C) Children currently having asthma had significantly higher odds of having a mother who smoked prior to pregnancy. D) There was no relationship between maternal smoking, either prior or during pregnancy, and childhood asthma (ever or current). Tanaka K, Arakawa M, Miyake Y. (2020). Allergol Immunopathol. Interpreting ORs Which of the following statements is NOT correct regarding the interpretation of the results? A) No association was observed between living with at least one household smoker during the postnatal period and children ever having or currently having asthma B) No association was noted between maternal smoking during pregnancy and children ever having or currently having asthma. C) Children currently having asthma had significantly higher odds of having a mother who smoked prior to pregnancy. D) There was no relationship between maternal smoking, either prior or during pregnancy, and childhood asthma (ever or current). Adjusted ORs for postnatal living with at least one household smoker and ever and current asthma are not statistically significant (includes the null value 1.0), which means there is no association. Tanaka K, Arakawa M, Miyake Y. (2020). Allergol Immunopathol. Interpreting ORs Which of the following statements is NOT correct regarding the interpretation of the results? A) No association was observed between living with at least one household smoker during the postnatal period and children ever having or currently having asthma B) No association was noted between maternal smoking during pregnancy and children ever having or currently having asthma. C) Children currently having asthma had significantly higher odds of having a mother who smoked prior to pregnancy. D) There was no relationship between maternal smoking, either prior or during pregnancy, and childhood asthma (ever or current). Adjusted ORs for maternal smoking during pregnancy and ever and current asthma are not statistically significant (includes the null value 1.0), which means there is no association. Tanaka K, Arakawa M, Miyake Y. (2020). Allergol Immunopathol. Interpreting ORs Which of the following statements is NOT correct regarding the interpretation of the results? A) No association was observed between living with at least one household smoker during the postnatal period and children ever having or currently having asthma B) No association was noted between maternal smoking during pregnancy and children ever having or currently having asthma. C) Children currently having asthma had higher odds of having a mother who smoked prior to pregnancy. D) There was no relationship between maternal smoking, either prior or during pregnancy, and childhood asthma (ever or current). Adjusted ORs for maternal smoking prior to pregnancy and a current asthma condition in children is not statistically significant (includes the null value 1.0), which means there is no association. Even though the OR is >1.0, which would be a positive association, the results cannot be interpreted as such UNLESS it is statistically significant. Tanaka K, Arakawa M, Miyake Y. (2020). Allergol Immunopathol. Interpreting ORs Which of the following statements is NOT correct regarding the interpretation of the results? A) No association was observed between living with at least one household smoker during the postnatal period and children ever having or currently having asthma B) No association was noted between maternal smoking during pregnancy and children ever having or currently having asthma. C) Children currently having asthma had significantly higher odds of having a mother who smoked prior to pregnancy. D) There was no relationship between maternal smoking, either prior or during pregnancy, and childhood asthma (ever or current). All adjusted ORs are not statistically significant and should be interpreted as null associations. The statement is correct. Tanaka K, Arakawa M, Miyake Y. (2020). Allergol Immunopathol. Interpreting ORs Which of the following statements is NOT correct regarding the interpretation of the results? A) No association was observed between living with at least one household smoker during the postnatal period and children ever having or currently having asthma B) No association was noted between maternal smoking during pregnancy and children ever having or currently having asthma. C) Children currently having asthma had significantly higher odds of having a mother who smoked prior to pregnancy. D) There was no relationship between maternal smoking, either prior or during pregnancy, and childhood asthma (ever or current). Tanaka K, Arakawa M, Miyake Y. (2020). Allergol Immunopathol. Odds Ratio in Cohort Studies  Ratio of the odds of a disease in the exposed compared with the odds of the disease in the unexposed Odds Ratio Odds of Disease in Exposed Odds of Disease in Unexposed Calculating Odds Ratio in Cohort Studies Disease Exposure Odds Ratio A C B D A A+B 1 A (A+B) C C+D 1 C (C+D) A B AD C BC D Odds Ratios in Case Control and Cohort Studies OR Disease A B C D Exposure Exposure Disease Odds of Exposure in Diseased Odds of Exposure in Nondiseased A OR A B C D Odds of Disease in Exposed Odds of Disease in Unexposed A C AD B AD B BC C BC D D Case Control Calculation of Odds Ratio Neuroblastoma Status What are the odds of congenital malformations for cases Congenital Malformations Number of times exposure occurs in cases 23 116 217 2284 A Odds Ratio C B 23 217 ? 0.105 AD BC D 23 × 2284 116 × 217 2.1 Cohort Calculation of Odds Ratio Congenital Malformations Neuroblastoma Status 23 116 217 2284 What are the odds of a child with congenital malformations developing neuroblastoma Number of times those with congenital malformations develop neuroblastoma A Odds Ratio B C 23 116 ? 0.20 AD BC D 23 × 2284 116 × 217 2.1 Example: Death by sex Death Totals 709 142 851 Sex Male Female 154 308 462 > Males are more likely to die than females ? How much more likely? What are the odds of dying for each sex? Example: Death by Sex Death Totals 709 142 851 Sex Male Female 154 308 462 Odds Ratio C 154 D 308 A 709 B 142 AD 709×308 BC 154×142 > There is a ten-fold greater odds of death for males than females 0.5 5.0 10.0 What about the Relative Risk? Death Totals 709 142 851 Sex Male Female 154 308 462 RR C 154 C+D 462 A 709 A+B 851 0.33 0.83 A/(A+B) 0.83 C/(C+D) 0.33 > There is a 2.5 greater probability of death for males than females 2.5 Odds Ratio vs Relative Risk Odds Ratio There is a ten-fold greater odds of death for males than females Relative Risk There is a 2.5 greater probability of death for males than females  Both measurements indicate that men were more likely to die on the Titanic  Odds ratio implies that men are much worse off than the relative risk estimate  Which number is a fairer comparison?  RR measures events in a way that is interpretable and consistent with the way people really think  RR cannot always be computed in a research design Instances when the Odds Ratio can estimate the Relative Risk  When the cases studied are representative, with regard to history of exposure, of all people with the disease in the population from which the cases were drawn  When the controls studied are representative, with regard to history of exposure, of all people without the disease in the population from which the controls were drawn  When the disease being studied is rare Odds Ratio in Matched Samples  In a matched case-control study, each case is matched to a control according to variables that are known to be related to disease risk  Data analyzed in terms of case-control pairs rather than for individual subject  Four combination of pairs Odds Ratio in Matched Samples Concordant Pairs Discordant Pairs  Pairs in which both the case and the control were exposed  Pairs in which the case was exposed, but the control was not  Pairs in which neither the case nor the control was exposed  Pairs in which the control was exposed, but the case was not Controls Exposure status Cases Exposure status Concordant Discordant Discordant Concordant Odds Ratio in Matched Samples Controls  Concordant pairs are ignored since they do not contribute in the calculation of the effect estimate  Matched OR = Ratio of discordant pairs B # of pairs in which cases were exposed C # of pairs which controls were exposed Cases  Discordant pairs of cases and controls are used to calculate the matched OR Exposure status Exposure status Concordant Discordant Discordant Concordant Example for Matched Case Control Study  Study Aim: To examine the risk factors for brain tumors in children  Hypothesis: Children with certain childhood cancers are more likely to have higher birth weight Cases Children with brain tumors Controls Children without brain tumors Exposure Birth weight > 8 lbs Example for Matched Case Control Study Controls Hypothesis: Children with brain tumors are more likely to have higher birth weights Exposure status >8 lbs ≤8 lbs Exposure status Cases Matched OR >8 lbs 9 18 B # of pairs in which cases were exposed C # of pairs which controls were exposed 18 7 ≤8 lbs 7 38 > 2.6 There is a 2.6-fold greater odds of children with brain tumors having a birth weight >8 lbs compared to having a birth weight ≤8 lbs Questions?