Sample size

Sample size calculations Outcome or exposure is continuous: comparing means in two groups alpha=0.05, Power = 80% Effect size – Mean Difference an idea of the mean and standard deviation of the unexposed group clinically relevant difference worth detecting between groups 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?

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. You need to have an estimate of the risk of disease in the unexposed for a cohort study. Need to justify where you are getting this number from. You need to know what effect size you want to be able to detect and why you have chosen this effect size. Effect size: difference in proportions given the proportion in one group – risk 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. Based on this the sample size program will calculate the n required. Do you think it is possible to get this many people.