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Case Study - Relative Risk and Odds Ratio
John Snow’s Cholera Investigations
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
85.5002402.
014042.78.5
002396.
013848. :V/L)&(SPopulation
002402.002396.1
002396.)|(002396.
171528
411)|(
014042.013848.1
013848.)&|(013848.
267625
3706)&|(
ORRR
LDoddsLDP
VSDoddsVSDP
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 nSV and nL from the 2 populations and observe XSV and XL deaths in sample. These XSV and XL are approximately distributed as Binomial random variables (approximate due to sampling from finite, but very large, populations)
)002396.0,(~)013848.0,(~ LLLSVSVSV pnBXpnBX
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)
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:
nkknk
n
k
npp
k
nkXP knk ,,1,0
)!(!
!)1()(
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)
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): U1,…,Un
• Let X be the number of Ui 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
Simulation Example• Simulate by taking samples of nSV=nL=5000 individuals
from each population of customers
• Generate XSV~B(5000,.013848) and XL~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)
Computations
OR and RRfor CIsget toln(OR) and ln(RR)for CIs of
boundsupper andlower theofpower the to...718.2 Raise
))(ln(1.96ln(OR) :ln(OR) populationfor CI %95
))(ln(1.96ln(RR) :ln(RR) populationfor CI %95
5000
11
5000
11))(ln(
11))(ln(
)5000(
)5000(
50005000
^^
^
^
^^
e
ORSE
RRSE
XXXXORSE
X
p
X
pRRSE
XX
XX
odds
oddsOR
X
X
p
pRR
X
n
Xp
X
n
Xp
LLSVSV
L
L
SV
SV
SVL
LSV
L
SV
L
SV
L
SV
L
L
LL
SV
SV
SVSV
Histogram of (Sample) Relative Risks
0
10
20
30
40
50
60
70
2.5
3.3
4.1
4.9
5.7
6.5
7.3
8.1
8.9
9.7
10.5
11.3
12.1
RR
Fre
qu
en
cy
Note that the distribution of Relative Risks is not normal
Histogram of Sample ln(RR)
020406080100120140
1
1.2
1.4
1.6
1.8 2
2.2
2.4
2.6
2.8 3
3.2
More
ln(RR)
Fre
qu
en
cy
Note that distribution of ln(RR) is approximately normal