1. Case Study - Relative Risk and Odds Ratio John Snow’s Cholera Investigations

2. Population Information 2 Water Providers: Southwark & Vauxhall (S&V) and Lambeth (L) –S&V: Population: 267625 # Cholera Deaths: 3706 –L: Poulation: 171528 # Choleta Deaths: 411

3. Sampling Distribution of RR & OR Goal: Obtain Empirical Sampling Distributions of sample RR and OR and observe coverage rate of 95% Confidence Intervals Process: Take independent random samples of size n SV and n L from the 2 populations and observe X SV and X L deaths in sample. These X SV and X L are approximately distributed as Binomial random variables (approximate due to sampling from finite, but very large, populations)

4. Binomial Distribution for Sample Counts Binomial “Experiment” –Consists of n trials or observations –Trials/observations are independent of one another –Each trial/observation can end in one of two possible outcomes often labelled “Success” and “Failure” –The probability of success, p, is constant across trials/observations –Random variable, X, is the number of successes observed in the n trials/observations. Binomial Distributions: Family of distributions for X, indexed by Success probability (p) and number of trials/observations (n). Notation: X~B(n,p)

5. Binomial Distributions and Sampling Problem when sampling from a finite sample: the sequence of probabilities of Success is altered after observing earlier individuals. When the population is much larger than the sample (say at least 20 times as large), the effect is minimal and we say X is approximately binomial Obtaining probabilities: Table C gives probabilities for various n and p. Note that for p > 0.5, use 1-p and you are obtaining P(X=n-k)

6. Simulating Binomial RVs Select n and p Obtain n random numbers distributed uniformly between 0 and 1 (any software package should have built-in random number generator): U 1,…,U n Let X be the number of U i values that  p X~B(n,p) Finite population adjustments can be made by “correcting” p after each draw EXCEL has built in Function: –Tools --> Data Analysis --> Random Number Generation –--> Binomial --> Fill in p and n

7. Simulation Example Simulate by taking samples of n SV =n L =5000 individuals from each population of customers Generate X SV ~B(5000,.013848) and X L ~B(5000,.002396) Compute sample relative risk, ln(RR), odds ratio, ln(OR), and estimated std. errors of ln(RR) and ln(OR) Obtain 95% CIs for RR, OR (based on ln(RR),ln(OR) Repeat for a large number of samples (1000 samples) Obtain the empirical distribution of each statistic Obtain an indicator of whether the 95% CI for RR contains the population RR (5.78) and whether the 95% CI for OR contains the population OR (5.85)

8. Computations

9. Note that the distribution of Relative Risks is not normal

10. Note that distribution of ln(RR) is approximately normal