hypothesis testing field epidemiology. hypothesis hypothesis testing is conducted in etiologic study...
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Hypothesis Testing
Field Epidemiology
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Hypothesis
• Hypothesis testing is conducted in etiologic study designs such as the case-control or cohort as well as the experimental study designs.
• An hypothesis is a a statement of association between exposure (predictor) and an outcome (disease or health event).
• Hypotheses are one-tailed or two tailed.The null hypothesis states that there is no association.
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Examples
• Smoking is not associated with lung cancer (Null hypothesis)
• Smoking is associated with a higher incidence of lung cancer (One-tailed hypothesis)
• Smoking is associated with a lower incidence of lung cancer (or it is protective) (One-tailed hypothesis)
• Smoking has some association with lung cancer (uncertain of how it influences lung cancer) (Two-tailed hypothesis)
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Rules of Thumb
• Usually there is one main hypothesis and a couple of secondary hypotheses
• The more specific you are in your statement of hypothesis, the easier it will be to answer your question
• Usually stated in the paper as “The purpose of the study is to….”
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Epidemiologic Decision Making
Disease No DiseaseExposure a b a+bNo Exposure c d c+d
a+c b+d N
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Relative Risk
R.R.= a/(a+b)
------------
c/(c+d)
RR = the likelihood of developing the disease in the exposed group compared to the unexposed group
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Relative Risk for a disease exposure
CVD No CVD Obesity 75 25 100 No Obesity 25 75 100 100 100 200
RR = 75/100 = 3.00 25/100
C.I. (2.10 - 4.29)
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Relative Risk for preventive intervention
Disease No Disease Counseling 25 75 100 No Counseling 50 50 100 75 125 200
RR = 25/100 = .50 50/100
C.I. (.39-.79)
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Relative Risk Calculation
RR = =
Ct No CT TotalUsed Condoms 30 70 100Did not useCondoms
60 40 100
90 110 200
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Attributable Risk
• AR = Ie - Io
the difference between incidence rates in the exposed and nonexposed groups
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Odds Ratio
• a/c
b/d• or the odds of
exposure in disease
compared to odds of exposure in
non diseased
• a*d
b*c• mathematically
equivalent to the
simpler formula
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Odds Ratio
O.R. = 60 * 70 = 3.50 40 * 30
Ct No CT TotalDouching 60 30 90No douching 40 70 110
100 100 200
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T-test - Continuous data
Number Mean CD4
Standard Deviation
p-value
Men 4350 326 288 < .001 Women 925 431 330
Formula t-test = mean A - mean B - diff Null variance for the entire study pop
Which group is more immuno-suppressed?
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C.I. For Mean CD4
Number Mean S.D. S.E. 95% C.I. Men 4350 326.2 288.4 4.37 317.6 - 334.8 Women 925 430.7 330.1 10.86 409.4 - 451.9
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T-test
• If the sample sized are different - first must pool the variances
• pooled var = (4015-1)71.0 + (955-1)84.9 =74
(4015+955-2)
t-test = 34.8-29.9 - 0 =4.9 =16
________________
41(1/4015+1/955) .23
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Normally Distributed Data
Std. Dev = 8.83 Mean = 34.0N = 5877.00
AGE_YRS
Age at Entry
Freq
uenc
y
1600
1400
1200
1000
800
600
400
2000
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Non-normally distributed data
Std. Dev = 298.80 Mean = 344.5N = 5275.00
TH_L_CNT
CD4/mm3
Freq
uenc
y
1200
1000
800
600
400
200
0
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2 Test of statistical association
• Used to determine statistical association for categorical data
2 = (O - E) 2
E
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2 Test - Categorical data
< 200 200 p-value Men 37.6 62.4 < .001
Women 20.4 79.6
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2 Test of statistical association
• Used to determine statistical association for categorical data
2 = (O - E) 2
E
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2 Calculation
ER use No ER use TotalGiven Hotline Number 200 300 500
No Hotline Number 200 300 500400 600 1000
ER use No ER use TotalGiven Hotline Number 100 400 500
No Hotline Number 300 200 500400 600 1000
(100-200)2 + (400-300)2 + (300-200)2 + (200-300)2 200 300 200 300
2 = 166.7, 1 D.F. (look up in table)
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Multivariable techniques
Continuous Outcome Categorical Outcome
Linear regression Logistic Regression
Generalized estimating equations (GEE)
Cox Regression
ANOVA GEE
Polychotomous