1. Sample size

2. Study Design • Cohort Study • When do you use it? • What do you select participants on? • Continuous variable

3. Study Design • Cohort Study • Continuous exposure • Example • Measure sbp levels and follow people to see if they have a stroke. • Outcome • Stroke yes or no

4. Study Design • Exposure is exposure to disease. • Exposed group – women with GDM • Unexposed group – women without GDM • Outcome • Infant birth weight

5. Study Design • Cohort Study • Exposure to surgical procedure A • Exposure to surgical procedure B • What if clinic 1 only does A and clinic 2 only does B. • What is the unit of observation clinic or individual? • Is it valid to compare procedure A to B in a study with these two clinics? • What if clinic 1 and clinic 2 do both procedures? • Is it valid to compare procedure A to B in a study with these two clinics?

6. Study Design • Cohort Study versus Interventional Study • Cohort is only observational • A change in protocol or implementation of an intervention can be the exposure; however, if the intervention is implemented as part of the study then it is an interventional study not a cohort.

7. Study Design • Case – Control study • When do you use it? • What do you select participants on? • What are you measuring in this type of study? • Dichotomous exposure • Continuous exposure • What can you not estimate using this type of study design?

8. Sample size calculations • Outcome or exposure is continuous: comparing means in two groups • Effect size – Mean Difference • alpha=0.05, • Power = 80% • Need to have an estimate of what proportion of the subjects are in group 1. If this is a cross-sectional study you need to justify why you think you will have this proportion of people in group 1. • State the minimum effect size you want to be able to detect in words. • State the number of people required in each group to detect this effect size. • Do you think it is possible to get this many people.

9. Sample size calculations • Outcome and exposure are dichotomous • Effect size – Risk Ratio or difference in proportions • Alpha = 0.05 • Power = 80% • Need to have an estimate of what proportion of the subjects are in group 1. If this is a cross-sectional study you need to justify why you think you will have this proportion of people in group 1. For a cohort study the proportion in group 1 is the proportion of unexposed. For a case-control study the proportion in group 1 is the proportion of controls. • You need to have an estimate of the risk of disease in the unexposed for a cohort study. You need to have an estimate of the odds of exposure in the controls for a case-control study. In both cases, you need to justify where you are getting this number from. • You need to know what effect size you want to bet able to detect and why you have chosen this effect size. • Effect size: difference in proportions given the proportion in one group, risk ratio or odds ratio. • Assuming 11% of the unexposed (i.e., women without a lot of children) are diabetic we will be able to detect a difference if the risk of prevalent diabetes is 14.8% or greater in the exposed women (i.e., women with a lot of children). • The minimal detectable risk ratio for prevalent diabetes comparing women with greater than 3 children to women with 3 or fewer children in our data set is 1.35. • The minimal detectable odds ratio for prevalent diabetes comparing women with greater than 3 children to women with 3 or fewer children in our data set is 1.41. • Can determine this because OR= [P2 (1-P1)]/[(1-P2) P1] • Based on this the sample size program will calculate the n required. • Do you think it is possible to get this many people.