Chapter 9: Case Control Studies Objectives: -List advantages and disadvantages of case-control studies -Identify how selection and information bias can affect results -Describe the use of confidence intervals -

Case-control studies 2 Introduction Hypothesis: Pesticide exposure increases the risk of breast cancer. Consider a hypothetical prospective cohort study of 89,949 women aged 34-59; 1,439 breast cancer cases identified over 8 years of follow-up Blood drawn on all 89,949 at beginning of follow- up and frozen Exposure: Level of pesticides (e.g. DDE) in blood characterized as high or low

Introduction Results YesNoTotal High36013,27613,636 Low1,07975,23476,313 Total1,43988,51089,949 Breast Cancer Pesticide Relative Risk = RR = (360/13,636) / (1,079/76,313) = 1.9

Case-control studies 4 Introduction Practical Problem: Quantifying pesticide levels in the blood is very expensive -- it's not practical to analyze all 89,949 blood samples To be efficient, analyze blood on all cases (N=1,439) but just take a sample of the women who did not get breast cancer, say two times as many cases (N=2,878)

Introduction YesNo High Low1,0792,446 Total1,4392,878 DDE Breast Cancer These data can be used to estimate the relative risk. Here we identified cases of disease from a defined population, and then taken a sample of that population for comparison. Exposure histories are determined for each group. This is an example of the case-control study.

Overview of Case-Control Studies One group has the disease of interest (cases) and a comparable group does not have the disease (controls). This study identifies possible causes of disease by determining how two groups differ with respect to exposure to some factor(s).

Case-Control Studies Obtain exposure and outcome information Inexpensive (relatively) Useful for rare diseases Retrospective in design Useful to study many risk factors Number of cases to controls may be up to 1:4 to increase power Test hypothesis; suggests cause-effect fireflyexpress.blogspot.com

Definition of a Case An individual who has a diagnosed disease or condition (syndrome) –Must meet all criteria of disease –Meets criteria of study design This disease should be recently diagnosed (usually within one year) A person who is willing to help understand the causes of his/her disease

Sources of Cases Ideally, identify and enroll all cases in a defined population in a specified time period. A tumor registry or vital statistics agency may provide a listing of potential cases. Hospitals, physicians’ practice, clinics also may be a source of cases, but not always incident cases. Disease organizations (Am Cancer, Am Heart)

Components of Controls Selected from a population whose distribution of exposure is similar to the case Has as similar of an opportunity to be exposed as the case Similar demographically to the case Does not have the disease of interest A sample of the source population which gave rise to the cases

Sources of Controls Population-based controls –Choosing someone from the same population –Random digit dialing Hospital controls (when cases r from hospital) –Someone with an unrelated disease or condition Relatives of cases Friends of cases--SES control Those who can accurately measure exposure

Criteria for Selecting Controls Controls are a sample of the source population that gave rise to cases. “Would Criteria” –If a member of the control group actually had the disease WOULD he/she end up as a case in the study?

Determining Relationships Case control studies look for associations between exposure and disease Answers to questions are compared Level of association is calculated by the odds ratio (OR) Cases and controls asked the same questions about exposure

Odds Ratio (OR) Provides a numerical estimation of risk in a case control study An OR provides a good approximation of risk when: –Cases are representative of all cases. –Controls are representative of a target population. –Cases and controls are well matched. –The frequency of disease in the population is small.

What is the Odds Ratio? An OR is a ratio of odds. Odds are ratios of probabilities. Risk is a probability – usually the probability of some undesirable outcome. Odds = probability / 1 – probability Odds = risk / 1 – risk If the probability of winning is 0.25 then the probability of losing is 0.75

Odds Ratio Case-control studies identify the probability of being exposed given the person has the disease and the probability of being exposed given the person does not have the disease. OR = odds of being a case among exposed odds of being a case among not exposed

Analysis of case-control studies Two possible outcomes for an exposed person: case or not Odds=a/b Two possible outcomes for an unexposed person: case or not Odds=c/d Odds ratio = odds of an exposed person being a case = a/b = ad/bc odds of unexposed person being a case c/d Just like the incidence rate ratio and cumulative incidence ratio, the odds ratio is a ratio measure of association.

2 x 2 Table Disease Status Exposure YesNo Yes No A + CB + D A + B C + D A B CD CASESCONTROLS

Esophageal Cancer and GERD Adenocarcinoma of Esophagus Heartburn once a week YesNo Yes No OR = 60/40 20/80 = 60 X X 40 = 6

Numerical Measurements of Risk Risk is expressed numerically on scale where 1 = no increased or decreased risk 2.5 = two and a half times more likely 2.0 = two times more likely 1.8 = 80% more likely 1.5 = 50% more likely 1.0 = NO RISK 0.8 = 20% less likely 0.5 = 50% less likely 0.2 = 80% less likely Less than 1.0 indicates protection from exposure

Strengths of Case-Control Studies Efficient for rare diseases and diseases with long latent period Can evaluate many risk factors for the same disease. So good for diseases where little is known.

Limitations of Case-Control Studies Provide indirect estimate of risk (no incidence) Difficult to determine temporality Representativeness of cases and controls often unknown Not efficient way to study rare exposures Bias possible due to retrospective nature Controls may have undetected diseases

Bias in Case-Control Observation bias –Recall bias Inaccurate recall of possible exposures Not tell socially undesirable exposures –Information bias Interviewer bias Faulty equipment Selection bias –Hospital cases vs controls in general population –Volunteers for study on colon cancer –Healthy worker bias

Confounding An effect of a factor on the ability to determine associations between exposures and outcome Confounder must be associated with the exposure and must be a predictor of the disease Smoking Low birth weight Alcohol Exposure Outcome Confounder

Controlling Confounding Collect information on all possible confounders Determine the amount of association between confounder and outcome Match cases with controls Strict recruitment of controls (e.g. no one who drinks alcohol - haha) Control in analysis phase by multivariate analysis