1. We’re ready to TEST our Research Questions! In science, how do we usually test a hypothesis?

2. EXPERIMENTALOBSERVATIONAL A study is set up to determine the effect of changing one or more variables on an outcome, while all other variables are held constant Risk factors (variables) are under the direct control of the investigator A study is created in which observations of subjects and measurements of variables are made without changing any variables Risk factors (variables) are NOT under direct control of investigator Was our ACT passage today EXPERIMENTAL or OBSERVATIONAL? Is EPIDEMIOLOGY an EXPERIMENTAL or OBSERVATIONAL study?

3. Example: Suppose we want to study the effect of smoking on lung capacity in women … EXPERIMENTALOBSERVATIONAL 1. Find 100 women age 20 who do not currently smoke. 2. Randomly assign 50 of the 100 women to the smoking treatment and the other 50 to the no smoking treatment. 3. Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke free for 10 years. 4. Measure lung capacity for each of the 100 women. 5. Analyze, interpret, and draw conclusions from data. 1. Find 100 women age 30 of which 50 have been smoking a pack a day for 10 years while the other 50 have been smoke free for 10 years. 2. Measure lung capacity for each of the 100 women. 3. Analyze, interpret, and draw conclusions from data. 1.What might be a problem here? 2.Which type of study would be more reliable?

4. Why can’t we always do experiments if they are more reliable? Ethics – Ex: Trying to determine if abortion causes breast cancer – Why experimental design wouldn’t work: It’s unethical to ask subject to have abortions Lack of Control – Ex: Trying to determine if smoking bans reduce lung cancer rates – Why experimental design wouldn’t work: Researchers can’t control laws and policies Impractical – Ex: Trying to determine if a certain medicine causes rare symptoms – Why experimental design wouldn’t work: Population size would have to be extremely large

5. Steps to Testing your Research Question: 1)Determine whether primary or secondary data is best for answering your research question 2)Choose a study design 3)Collect & analyze data 1 m

6. Step 1: Determine whether primary or secondary data is best for answering your research question What would you guess is the DIFFERENCE between Primary and Secondary data?

7. Primary and Secondary Data Primary data: you collect it yourself – Ex: a survey that you administer that asks about the health behaviors of people in your community. Secondary data: has already been collected by someone else, and you analyze it in a new way to answer the question that you are interested in – Ex: data collected by the Centers for Disease Control and Prevention (CDC) on disease rates in different states

8. Quick Quiz If your question is: Why are rates of heart disease different around the US? What type of data would you most likely collect? Primary data ORSecondary data This is probably not data you would be able to collect yourself!

9. Step 2: Choose a Study Design Descriptive: answers who, what, when, where Analytical: answers why or how Steps to Identifying the Problem: 1)Choose health-related outcome 2)Clearly define the outcome (“case”) 3)Choose a population 4)Describe the problem (descriptive study) Prevalence Incidence

10. Step 2: Choose a Study Design Descriptive: answers who, what, when, where Analytical: answers why or how Case-Control Cross-Sectional Cohort

11. Analytical Study Design Case-Control Cross-Sectional Cohort STEPS: Cohort (FORWARD) (CAUSE to EFFECT) Cross-Sectional (“Snapshot”) (cause & effect) Case-Control (BACKWARD) (EFFECT to CAUSE) Break up your population into: - Have RF - Don’t have RF - No initial groups- Have Outcome - Don’t have Outcome …then: - Wait - Usually a long period of time - survey/interview/test them - identify who has RF & Outcome at the same time -Look backwards into their past - Usually done through survey, interview, or lab tests …to determine if: - Outcome? - No outcome? RF & OUTCOME show up in the same people? - Had RF? - Didn’t have RF?

12. Cohort Set-up

13. What might be some drawbacks to a cohort study? Cohort Example

14. Case-Control Set-up Population Cases (Outcome) Controls (No Outcome) RFNo RF RFNo RF (Outcome) (Risk Factor)

15. Case-Control Example Illinois Population Cases of Brain Tumors Controls (no Brain Tumors) High usage of cell phone Low usage of cell phone High usage of cell phone Low usage of cell phone (Outcome) (Risk Factor) What might be some advantages of a case-control study over a cohort study?

16. Quick Quiz: Identify the study method below as CASE-CONTROL or COHORT 1) Assemble a group of 300 persons with lung cancer and a group of 300 persons without lung cancer and question them about their past smoking history 2) Follow a group of smokers and a group of nonsmokers over time to see who develops lung cancer

17. Cross-sectional Study Information on the exposure or risk factor is collected at the same time as information about the health outcome Often performed by using a survey Example: High school athletes given a survey including questions on: 1) Risk factors: helmet use, training, & type of sport they play 2) Outcomes: types of injuries, severity of injuries

18. Step 3: Collect & Analyze Data We’ll practice this during the case!

19. Extension: Clinical trial (experimental) Cohort Case-control Cross-sectional MOST Reliable LEAST Reliable

20. What calculation would I use? Cohort: Follows population forward in time, from suspected cause to effect – Quantified by calculating the relative risk for the exposure Case-control: Works backwards, from suspected effect to cause – Quantified by calculating the odds ratio (we’ll learn about odds ratio later on, but it’s quite similar to relative risk!) Extension:

21. In general, five criteria must be met to establish a cause-and-effect relationship: Strength of association—the relationship must be clear. Consistency—observation of the association must be repeatable in different populations at different times. Temporality—the cause must precede the effect. Plausibility—the explanation must make sense biologically. Biological gradient—there must be a dose- response relationship.