Case Control Studies ERIC Notebook

ERIC at the UNC CH Department of Epidemiology Medical Center Case-Control Studies E R I C N O T E B O O K S E R I E S Case-control studies are used to determine if there is an association between an exposure and a specific health outcome. These studies proceed from effect (e.g. health outcome, condition, disease) to cause (exposure). Case-control studies assess whether exposure is disproportionately distributed between the cases and controls, which may indicate that the exposure is a risk factor for the health outcome under study. Case- control studies are frequently used for studying rare health outcomes or diseases. Unlike cohort or cross-sectional studies, subjects in case-control studies are selected because they have the health outcome of interest (cases). Selection is not based on exposure status. Controls, persons who are free of the health outcome under study, are randomly selected from the population out of which the cases arose. The case-control study aims to achieve the same goals (comparison of exposed and unexposed) as a cohort study but does so more efficiently, by the use of sampling. After cases and controls have been identified, the investigator determines the proportion of cases and the proportion of controls that have been Second Edition Authors: Lorraine K. Alexander, DrPH Brettania Lopes, MPH Kristen Ricchetti-Masterson, MSPH Karin B. Yeatts, PhD, MS ERIC Notebook Second Edition At baseline:  ·Selection of cases and controls based on health outcome or disease status  Exposure status is unknown *Exposure at some specified point before disease onset

ERIC at the UNC CH Department of Epidemiology Medical Center exposed to the exposure of interest. Thus, the denominators obtained in a case-control study do not represent the total number of exposed and non-exposed persons in the source population. After the investigator determines the exposure, a table can be formed from the study data. Measures of incidence in case-control studies In case-control studies the proportion of cases in the entire population-at-risk is unknown, therefore one cannot measure incidence of the health outcome or disease. The controls are representative of the population-at-risk, but are only a sample of that population, therefore the denominator for a risk measure, the population- at-risk, is unknown. We decide on the number of diseased people (cases) and non-diseased people (controls) when we design our study, so the ratios of controls to cases is not biologically or substantively meaningful. However, we can obtain a valid estimate of the risk ratio or rate ratio by using the exposure odds ratio (OR).* Diseased person-years RR = (a/n1)/(c/n2) Case-Control Study OR = (a/c)/(b/d) = (a/b)/(c/d) = (axd)/(cxb) If b and d (from the case-control study) are sampled from the source population, n1 + n2, then b will represent the n1 component of the cohort and d will represent the n2 component, and (a/n1)/(c/n2) will be estimated by (a/b)/ (c/d). Interpreting the odds ratio The odds ratio is interpreted the same way as other ratio measures (risk ratio, rate ratio, etc.). For example, investigators conducted a case-control study to determine if there is an association between colon cancer and a high fat diet. Cases were all confirmed colon cancer cases in North Carolina in 2010. Controls were a sample of North Carolina residents without colon cancer. The odds ratio was 4.0. This odds ratio tells us that individuals who consumed a high fat diet have four times the odds of colon cancer than do individuals who do not consume a high-fat diet. In another study of colon cancer and coffee consumption, the OR was 0.60. Thus, the odds of colon cancer among coffee drinkers is only 0.60 times the odds among individuals who do not consume coffee. This OR tells us that coffee consumption seems to be protective against colon cancer. Types of case-control studies Case-control studies can be categorized into different groups based on when the cases develop the health outcome and based on how controls are sampled. Some ERIC NOTEBOOK PAGE 2 Cases Controls Exposed a b Unexposed c d Odds of exposure among cases = a/c Odds of exposure among controls = b/d Disease No Disease Exposed a n 1 Unexposed c n 2 Cases Controls Exposed a b Unexposed c d OR = 1 Odds of disease is the same for exposed and unexposed OR > 1 Exposure increases odds of disease OR < 1 Exposure reduces odds of disease *Note: Under some conditions, the odds ratio approxi- mates a risk ratio or rate ratio. However, this is not always the case, and care should be taken to interpret odds ratios appropriately. In case-control studies the proportion of cases in the entire population-at-risk is unknown, therefore one cannot measure incidence of the health outcome or disease. The controls are representative of the population-at-risk, but are only a sample of that population, therefore the denominator for a risk measure, the population- at-risk, is unknown. We decide on the number of diseased people (cases) and non-diseased people (controls) when we design our study, so the ratios of controls to cases is not biologically or substantively meaningful. However, we can obtain a valid estimate of the risk ratio or rate ratio by using the exposure odds ratio (OR).* Odds of exposure among cases = a divided by c Odds of exposure among controls = b divided by d RR = (a divided by n1) divided by (c divided by n2) OR = (a divided by c) divided by (b divided by d) = (a divided by b) divided by(c divided by d) = (axd) divided by (cxb) The odds ratio is interpreted the same way as other ratio measures (risk ratio, rate ratio, etc.). OR = 1 Odds of disease is the same for exposed and unexposed OR > 1 Exposure increases odds of disease OR < 1 Exposure reduces odds of disease For example, investigators conducted a case-control study to determine if there is an association between colon cancer and a high fat diet. Cases were all confirmed colon cancer cases in North Carolina in 2010. Controls were a sample of North Carolina residents without colon cancer. The odds ratio was 4.0. This odds ratio tells us that individuals who consumed a high fat diet have four times the odds of colon cancer than do individuals who do not consume a high-fat diet. In another study of colon cancer and coffee consumption, the OR was 0.60. Thus, the odds of colon cancer among coffee drinkers is only 0.60 times the odds among individuals who do not consume coffee. This OR tells us that coffee consumption seems to be protective against colon cancer.

ERIC at the UNC CH Department of Epidemiology Medical Center investigators biased information regarding the level of exposure among the controls over the course of the study. Source populations for case-control studies Source populations can be restricted to a population of particular interest, e.g. postmenopausal women at risk of breast cancer. This restriction makes it easier to control for extraneous confounders in the population. Controls should represent the restricted source population from which cases arise, not all non-cases in the total population. The cases in the study do not have to include all cases in the total population. Sources of cases  Cases diagnosed in a hospital or clinic  Cases entered into a disease registry, e.g. cancer, birth defects, deaths  Cases identified through mass screening, e.g. hypertensives, diabetics  Cases identified through a prior cohort study, e.g. lung cancers in an occupational asbestos cohort Sources of controls  Population controls are non-cases sampled from the source population giving rise to cases. This is the most desirable method for selecting controls. Sampling randomly from census block groups, or a registry such as the Department of Motor Vehicles (of adults who are able to drive) are examples of ways to find and recruit population-based controls.  Neighborhood or friend controls are appropriate for selection as controls if these individuals would be included as cases if they developed the health outcome of interest. It is not appropriate to select neighbors or friends as controls if they share the exposure of interest.  Hospital controls - There are certain problems with hospital controls in that they may not be from the same source population from which the cases arose. Hospital controls may not be representative of the exposure prevalence in the source population of cases, e.g. there may be a higher prevalence of smokers in hospitals. Hospital controls also may have diseases resulting from the exposure of interest, e.g. the exposure (smoking) is related to the disease of interest (cancer) and to heart and lung diseases from which the controls may be suffering.  Controls with another disease - However if the study is on lung cancer, for example, it is essential to exclude cancers known or suspected to be related to the study exposure of interest. These controls also share some of the same problems as hospital controls. Advantages of case-control studies Case-control studies are the most efficient design for rare diseases and require a much smaller study sample than cohort studies. Additionally, investigators can avoid the logistical challenges of following a large sample over time. Thus, case-control studies also allow more intensive evaluation of exposures of cases and controls. Case- control studies that use incidence density sampling or risk set sampling yield a valid estimate of the rate ratio derived from a cohort study if incident cases are studied and controls are sampled from the risk set of the source population. If properly performed (i.e. appropriate sampling), case-control studies provide information that mirrors what could be learned from a cohort study, usually at considerably less cost and time. Disadvantages of case-control studies Case-control studies do not yield an estimate of rate or risk, as the denominator of these measures is not defined. Case-control studies may be subject to recall bias if exposure is measured by interviews and if recall of exposure differs between cases and controls. However, investigators may be able to avoid this problem if historical records are available to assess exposure. Choosing an appropriate source population is also difficult and may contribute to selection bias. Case-control studies are not an efficient means for studying rare exposures (less than 10% of controls are exposed) because very large numbers of cases and controls are needed to detect the effects of rare exposures. ERIC NOTEBOOK PAGE 4

ERIC at the UNC CH Department of Epidemiology Medical Center Self-Evaluation Questions 1. Suppose that in a case-control study using incident cases of colon cancer you found that 80% of the cases were married. Does this demonstrate that being married increases the risk of developing colon cancer? 2. In the same case-control study above, assume that 90% of the control group is married. If there are 200 cases and 200 controls estimate the risk ratio of colon cancer for single men. Construct a 2x2 table and determine and in- terpret the exposure odds ratio. Self-Evaluation Answers 1. No. In order to assess whether or not the exposure of interest (marriage) increases the risk of having colon can- cer, the proportion of controls that are married must also be known and the exposure odds ratio must be comput- ed. 2. OR = (40x180) / (20x160) = 2.25 An odds ratio of 2.25 means that single men have 2.25 times the odds of being a case compared to married men. ERIC NOTEBOOK PAGE 5 Acknowledgement The authors of the Second Edition of the ERIC Notebook would like to acknowledge the authors of the ERIC Notebook, First Edition: Michel Ibrahim, MD, PhD, Lorraine Alexander, DrPH, Carl Shy, MD, DrPH, and Sherry Farr, GRA, Department of Epidemiology at the University of North Carolina at Chapel Hill. The First Edition of the ERIC Notebook was produced by the Educational Arm of the Epidemiologic Research and Information Center at Durham, NC. The funding for the ERIC Notebook First Edition was provided by the Department of Veterans Affairs (DVA), Veterans Health Administration (VHA), Cooperative Studies Program (CSP) to promote the strategic growth of the epidemiologic capacity of the DVA. Terminology Cohort studies: An observational study in which subjects are sampled based on the presence (exposed) or absence(unexposed) of a risk factor of interest. These subjects are followed over time for the development of a health outcome of interest. Cross-sectional studies: An observational study in which subjects are sampled at one point in time, and then the associations between the concurrent risk factors and health outcomes are investigated. Exposure odds ratio (OR): the odds of a particular exposure among persons with a specific health outcome divided by the corresponding odds of exposure among persons without the health outcome of interest. Yields a valid estimate of the incidence rate ratio or risk ratio derived from a cohort study, depending on control sampling. Incident case: a person who is newly diagnosed as a case. Prevalent case: a person who has a health outcome of interest that was diagnosed in the past. Risk ratio (RR): the likelihood of a particular health outcome occurrence among persons exposed to a given risk factor divided by the corresponding likelihood among unexposed persons. Source population: the population out of which the cases arose. From: Medical Epidemiology, R.S. Greenberg, 1993, 1996. Marital status cases controls total single 40 20 60 married 160 180 340 total 200 200 400