Designing a New Study:

1. Designing a New Study: II. Cross-sectional and Case-control Studies

2. Cross-sectional (prevalence) study All measurements on a single occasion. Determine predictor and and outcome after the data collection Estimate prevalence Case-control study Begin with the outcome, then identify the predictor Explore the potential association A cohort study: the sequence of making the measurements is the same as the chronology of cause and effect.

3. CROSS-SECTIONAL STUDIES Well suited to the goal of describing variables and their distribution patterns • Structure • Similar to cohort study (except that measurements are made at once) • Can examine associations based on the investigator’s hypothesis, not based on the study design. • e.g., age, race---usually predictors blood lead level and hyperactivity ---->misleading (historic information on the time course)

4. Cross-sectional Study Risk factor; No disease Risk factor; Disease Population No risk factor; No Disease No risk factor; Disease Sample Steps: 1. Select a sampling from the population 2. Measure predictor and outcome variables

5. CROSS-SECTIONAL STUDIES • Designing • Settle on the research question • Specify criteria for the target and accessible populations • Establish the design for drawing the sample • Decide the phenomena to study • Define the approach to measuring appropriate variables.Example 8.1: Sexually transmitted disease and the use of oral contraceptives

6. Statistics for expressing disease frequency in observational studies Type of Study Statistics Definition # of people who have the disease at one point in time Cross-sectional Prevalence Cohort Incidence # of people at risk at that point # of new cases of disease over a period of time # of people at risk during that period * Relative prevalence = Relative risk prevalence/incidence bias

7. strength Relatively fast and inexpensive No waiting time to see the outcome No loss to follow-up Provide the prevalence of a disease or a risk factor Convenient for examining networks of causal links alcohol intake and HDL-cholesterol First step for investigations weakness Difficult to establish causal relationship Impractical for the study of rare diseases or risk factors from a general population e.g., 1 in 10,000 in a general population for a stomach cancer in 45-59 year old men Susceptible to prevalence/incidence bias e.g., Kids with HLA-A2 were at increased risk factor for the incidence of leukemia ???[truth was that HLA-A2 kids live longer] Cross-sectional Studies

8. Cross-sectional Studies • Case series =~ cross sectional studies for relatively rare diseases • the sample from a diseased population not from a general population • suitable to describe the characteristics of the disease than to analyzing differences between these patients and healthy people e.g., Of the first 1000 patients with AIDS, for example, 727 were homosexual or bisexual males and 236 were I.V. drug abusers. Furthermore, within a sample of patients with a disease there may be association of interest, the higher risk of Kaposi sarcoma among AIDS patients who are homosexual than among AIDS patients who are I.V. drug abusers.

9. Cross-sectional Studies • Serial Surveys • To draw inferences about changing patterns over time. • e.g., census data • Not a cohort study (i.e., following a single group of people)

10. Case-Control Studies • Structure • the prevalence of the risk factor in subjects with the disease (cases) can be compared with the prevalence in subjects without the disease (controls). • Retrospective • “house red”more modest and a little riskier than the other selections, but much less expensive and sometimes surprisingly good!

11. Case-control design Population with disease (cases) THE PAST OR PRESENT THE PRESENT Risk factor present Risk factor absent Disease Sample of cases Risk factor present Risk factor absent No disease Sample of controls Much larger population without disease (controls)

12. Case-Control Studies • Steps: 1. Select a sample from a population of people with the disease (cases) 2. Select a sample from a population at risk that is free of the disease (controls) 3. Measure predictor variables

13. Designing a case-control study • Settle on the research question • Specify criteria for the target and accessible populations (the cases and the controls) • Establish the design for drawing the sample • Decide the phenomena to study • Define the variables and measurement approaches, and establishes the hypotheses to be tested.

14. Designing a case-control study • Settle on the research question“Whether there is an association between use of aspirin and the development of Reye’s syndrome”

15. Designing a case-control study • Specify criteria for the target and accessible populations (the cases and the controls) • The cases: Children with a viral infection followed by Reye’s syndrome • The controls: Children with a viral infection but no Reye’s syndrome

16. Designing a case-control study • Establish the design for drawing the sample(cases)“all 30 patients with Reye’s syndrome who are accessible to him for study” • Establish the design for drawing the sample(controls)“60 patients drawn from the much larger population of accessible patients who have had minor viral illnesses without Reye’s syndrome”

17. Designing a case-control study • Decide the phenomena to study • Define the variables and measurement approaches, and establishes the hypotheses to be tested.“ask the subjects in both groups about their use of aspirin.”“approximate relative risk can be computed”

18. Designing a case-control study • Cannot yield estimates of the incidence or prevalence of a disease, because the proportion of study subjects who have the disease is determined by how many cases and how many controls the investigator chooses to sample, rather than by their proportions in the population. • Can yield some descriptive information on the characteristics of the cases and an estimate of the strength of the association between each predictor variable and the presence or absence of the disease (odds ratio).

19. Odds ratio and relative risk Disease No disease Risk factor present Risk factor absent a b c d • Odds ratio in a cross sectional studiesad/cb a/b ad/cd = c/d . [a/(a + b) c/(c + d)] . a ( c + d) = c (a + b)

20. Strengths Efficiency for rare outcomes high yield of information from relatively few subjects Usefulness for generating hypotheses Weaknesses no incidence no prevalence no attributable or excess risk Sampling bias, and how to control it randomization is near impossible (pts with a diagnosed) misdiagnosed or misdiagnosed are omitted Case-Control Studies

21. Weaknesses selection of cases ---relatively straightforward selection of controls---?? Sampling the cases and controls in the same way Matching Using two or more control groups Using a population-based sample Differential measurement bias, and how to control it Use of data recorded before the outcome occurred Blinding Case-Control Studies