Title: Master Course based on Rothman: Epidemiology chapter 15
1Master Coursebased onRothman Epidemiology
chapter 1-5
- Hein Stigum
- Presentation, data and programs at
- http//folk.uio.no/heins/
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2Agenda
- Introduction
- Epidemiological thinking
- Concepts
- Causation
- Generalization
- Methods
- Measures
- Design
- Bias
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31. Introduction
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4Epidemiology
- Study of exposure and disease
- Air pollution Heart disease
- Obesity Diabetes
- Education Exercise
- Questions
- How much exposure?
- How much disease?
- More disease among exposed?
5Epidemiologic information
- Experiments
- Randomized Controlled Trials
- Observational data
- Registries
- Medical birth -, Cancer -, Patient registry
- Surveys
- Mother and Child Cohort
- Linking
Observational data ? Systematic errors
www.fhi.no
6Downs syndrome
Age
Downs
Parity
Confounding
7Reading problems
- Study
- 5000 adults
- Questionnaire
- 1000 respond
- 5 reading problems
Selection bias
8Sexual partners
- Males report twice as many partners as females do
Information bias
92. Causation
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10Component-, sufficient cause
Switch
wire
electricity
bulb
light
Pie Sufficient cause for light Parts
Component causes
11Causal pies
- Component cause
- Sufficient cause
- Necessary cause
- Strength
- Interaction
- Induction time
- Attributable fraction
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12Association versus cause
- Observe
- Smoking associated with Lung Cancer
- Infer cause
- Observe
- Yellow fingers associated with Lung Cancer
- Infer cause
13Generalization
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14Generalization
- Do the results apply outside the sample?
- Statistical generalization
- Prevalence of smoking among males, generalize to
females? - Representative sample
- Biological generalization
- Animal studies, generalize to humans?
- Information from outside the study
- Homogenous sample
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15Summing up
- Disease
- Exposure ? Disease
- Observational data
- Cause can not be observed directly
- Generalize representative homogeneous
163. Measures
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17Epidemiological measures
- Frequency
- prevalence
- incidence
- Association
- Risk difference
- Risk ratio
- Odds ratio
- Impact
- Attributable fraction
How much disease?
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18Mathematical concepts
Risk, probability,
Km/h
Odds lives at one place in time
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19Frequency measures
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20Disease frequency
Theoretical concept
Estimator
!
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21Disease frequency depicted
t
a
Risk time
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22Prevalence example
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23Incidence proportion example
1. If no loss to follow up
2. If loss to follow up
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24Incidence rate (small cohort)
Assumption Can only get the disease once
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25Incidence rate (large cohort)
1.
2.
3.
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26Incidence of hip fracture, age 65
Incidence rate pr 10 000 person years
(Lofthus et al. 2001)
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27Other measures
- Attack rate risk of infection
- Case fatality rate risk of death if ill
28Epidemiological measures
- Frequency
- prevalence
- incidence
- Effect
- Risk difference
- Risk ratio
- Odds ratio
- Impact
- Attributable fraction
How much disease?
More disease among exposed?
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29Effect measures
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30Causal effect
Risk is not a measure of causal effect, need a
contrast.
Contrast
Counterfactual
Real
31Causal effect 2
- Ideal
- Counterfactual same subjects same time
- Real
- Crossover same subjects diff. time
- Randomized exchangeable same time
- Observational adjust for confounding same
time
32Association measures
- More disease among exposed?
- Compare frequency among exposed1 and unexposed0
- Difference
- Risk Difference
- Ratio
- Risk Ratio
- Odds Ratio
0no effect
1no effect
1no effect
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33RR and RD example
Disease lung cancer Exposure smoking
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34OR example
Disease lung cancer Exposure smoking
- Why use OR?
- Trad. Case-Control
- Logistic regression
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35Relative risk
Risk ratio rate ratio for short-term
risks. Both are termed relative risk
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36Bullying, OR example
- Bullying in the nordic countries
- 17 114 children, 2 584 bullied
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37Dec-09
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38RR and OR depicted
Risks and Risk Ratio
Odds and Odds Ratio
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39Epidemiological measures
- Frequency
- prevalence
- incidence
- Effect
- Risk difference
- Risk ratio
- Odds ratio
- Impact
- Attributable fraction
How much disease?
More disease among exposed?
How important?
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40Impact measures
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41Attributable fraction
- Among exposed
- In population
42Attributable fraction example
43Measures, Summing up
- Frequency
- prevalence
- incidence risk, rate
- Effect contrast R1 , R0
- RD
- RR
- OR ORRR (if rare disease)
- Impact of cases
- Afe , Afp (may sum gt100)
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444. Design
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45True or false?
- It takes 2 to tango
- It takes 3 chords to play the blues
- It takes 4 numbers to be an epidemiologist
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46The 2 by 2 table
.01.01.1 .01 .13
Add 100 to cell d .01.01.1 .005.125
Add 10 to cell c .01.01.05 .01.08
To increase power Cohort balance
exposure Case-Control balance disease
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473 examples
- Gender and Smoking
- Exercise and Coronary Heart Disease (CHD)
- Genes and Diabetes type 1
What design should we use?
- Considerations
- Disease rare / common
- Follow up time short / long
- Reversed causality ?
- Recall bias ?
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48Cross-section
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49Time
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50Cross-sectional example
Pro fast and inexpensive Con reversed
causality
OK
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51Cohort
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52Disease frequency depicted
t
a
Risk time
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53Cohort, Risk and Odds
Pro reliable Con costly, time consuming, loss
to follow up
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54Cohort, Rate
Previous example (Risk and Odds)
Rate 3 years follow up time
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55Case Control studies
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56Gene-Diabetes
Full Cohort
Case-Control
- In practice Cases. 1-4 controls per case
- Sample controls independent of exposure
- Exposure back in time
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57Gene-Diabetes
One Control per Case
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58Case-Control studies
- Cohort studies
- Measure the exposure experiance of the entire
population - Case-Control studies
- Measure the exposure experiance of a sample of
the source population of cases (base) - Key assumption
- Sample controls independent of exposure (same
sampling fraction) - Prospective or retrospective
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59Traditional Case-Control
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60Design, Summing up
615. Bias
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62Precision and validity
- Measures of populations
- precision - random error - statistics
- validity - systematic error epidemiology
- Lack of validity measures are biased
- type of bias
- direction of the bias
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63Type of bias
- Selection bias
- Are those who answer different?
- Information bias
- Do they tell the truth?
- Confounding
- Is the association a cause?
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64Selection bias
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65Sources of selection bias
- Selective response
- sexual survey
- Self selection
- Nevada atom test and leukemia
- Loss to follow up connected to disease
- air pollution and astma
- Healthy worker effect
- aluminium workers and lung disease
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66Selection bias
Population
Sample
Respons
Responders
Non-responders
Outcome
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67Information bias
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68Non-differential misclassification
True smoking
10 of smokers report no smoking
Non-differential RR?
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69Other sources of information bias
- Not true
- males report more partners than females
- Not blinded
- passive smoking and astma
- Selective recall
- alcohol in pregnancy and malformations
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70Confounding
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71Confounding
- Ideal
- Same subjects are both exposed and unexposed at
the same time, (counterfactual) - Practice
- As equal as possible
- Comparison bias
- Confounding
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72Downs syndrome
Age
Downs
Parity
Confounding
73Downs syndrom, logistic regression
Crude
Adjusted
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74Bias, Summing up
- Random / Systematic error
- Systematict error ? bias in measure
- 3 types of bias
- Selection
- Information
- Confounding
- May remove bias in analysis
- New tool Causal models
75Epidemiology, Summing up
- Study of exposure and disease
- Observe association, deduce cause
- Disease
- prevalence, incidence (risk, rate)
- Causal effect
- RD, RR, OR
- Design
- Cross-sectional, Cohort, Case-Control
- Observational ? bias,
- Selection, Information, Confounding