Epidemiological concepts Measures of frequency and...
Transcript of Epidemiological concepts Measures of frequency and...
07-04-2013
1
Epidemiological concepts
PhD-course in epidemiology
Lau Caspar Thygesen
Associate professor, PhD
9th April 2013
Agenda
• Measures of frequency and association
• Confounding vs. interaction vs. intermediate variable
• Choosing study design
• Causality
Epidemiological measures
• Measures of disease frequency
• Measures of association
• Measures of potential impact
Measures of disease frequency
• Incidence
• Cumulative incidence (CIP)
• Incidence proportion
• Risk
• Incidence rate (IR)
• Incidence density
• Person-time incidence
• CIP can be calculated from IR
• Prevalence
• Point prevalence (prevalence proportion)
• Period prevalence
Exposure-outcome table
Outcome
Yes No P-years
Exposure Yes a b RT1 a+b
No c d RT0 c+d
a+c b+d RT
Relationship between prevalence and incidence
• Prevalence depends on incidence and disease duration
• Inflow: Incidence • Outflow: Cure and mortality
• Assumptions: – No change in incidence over time – No change in duration over time – No change in age structure
07-04-2013
2
Example
• IR=0.001/p-years
• dur=5 years
• Prevalence = 0.001*5/(1+0.001*5) = 0.5%
• IR=0.001/p-years
• dur=10 years
• Prevalence = 0.001*10/(1+0.001*10) = 1.0%
Measures of association
• Relative measures
• Relative risk / risk ratio (RR)
• Relative incidence rate (incidence rate ratio - IRR)
• Odds ratio (OR)
• Prevalence ratio
• Absolute measures
• Risk difference (RD)
• Incidence rate difference (IRD)
• Number needed to treat (= 1 / RD)
Measures of potential impact
• Impact of exposure removal on exposed • Attributable risk (AR)
• Attributable risk percent (AR%)
• (Excess risk / etiologic fraction among the exposed / relative risk reduction / attributable fraction (exposed))
• Impact of exposure removal on population • Population attributable risk (PAR)
• Population attributable risk percent (PAR%)
• (Attributable fraction (population))
• Only for causal associations!
Attributable risk
• Risk among exposed = 5.1%
• Risk among non-exposed = 2.5%
• Risk difference = 5.1% - 2.5% = 2.6%
• Risk ratio = 5.1% / 2.5% = 2.04
• AR = risk difference (RD)
• AR% = RD / risk(exposed) = 2.6% / 5.1% = 51%
Population attributable risk
• PAR
= N(cases because of exposure) / N(all cases)
= CIP – CIP0
• PAR%
= (CIP – CIP0) / CIP * 100
= Pr(exp)*(RR-1) / (Pr(exp)*(RR-1) + 1) * 100
Population attributable risk
• Cum incidence exposed = 10.6 per 1000 • Cum incidence non-exposed = 3.4 per 1000 • Cum incidence in population = 5.8 per 1000 • Pr(exposure) = 32.5%
• AR = 10.6 – 3.4 = 7.2 per 1000 • RR = 10.6 / 3.4 = 3.1 • PAR = 5.8 – 3.4 = 2.4 per 1000 • PAR% = 2.4 / 5.8 = 41% • PAR%(2) = .325*(3.1-1) / (.325*(3.1-1) + 1) = 41%
07-04-2013
3
Cot death
• RR(sleep on stomach) = 5
• Pr(sleep on stomach) = 50%
• PAR% = 0.5*(5-1) / (0.5*(5-1)+1) = 2/3
• Today this exposure is less important because fewer babies are exposed
Smoking and heart disease
Risk(exp) = 0.06
Risk(non-exp) = 0.03
Pr(exp) = 0.5
AR% = ?
PAR% = ?
RR
1
2
50 % 50 %
Non-smoker Smoker
Which situation is worst?
50 % 50 %
1,0
1,3
RR
95 % 5 %
1,0
4,0
RR
Many exposed
Low RR
PAR%=13%
Few exposed
High RR
PAR%=13%
Etiologic fractions of mortality in Denmark
Juel. Ugeskr Læger 2001;163:4190-95.
1993 - 1997
Males Females
Tobacco
Alcohol
Drugs
22.8 % 16.5 %
6.3 % 2.5 %
1.2 % 0.7 %
How do you add PAR%?
Example
Factor a: PAR% = 50%
Factor b: PAR% = 50%
Factor c: PAR% = 50%
The formula
• PAR%total= 1 - (1-PAR%a)*(1-PAR%b)*(1-PAR%c)
= 1 – 0.5 * 0.5 * 0.5
= 87.5%
• Even in this situation 12.5% will not be preventable
07-04-2013
4
More than 100%?
• The sum of PAR%s can be more than 100%
• PAR%(1) + PAR%(2)+…….+PAR%(n) ∞
• PAR%(1+2+3…….n)= 100 %
Introduce a third variable
Mediator
Confounder
Effectmodifier
Exposure Outcome
?
21
Confounding
• When an observed association can be partly or completely explained by different distributions of other risk factors between exposed and non-exposed
• The classic three conditions
• Confounder should be associated with exposure
• An independent risk factor for the outcome
• Not be a mediator between exposure and outcome 22
Confounding
Exposure Outcome
Confounder
Confounding: example
Drinker Non-drinker
100
200
Lung cancer
No lung cancer
50
50
50
150
50 1503.0
50 50OR
Confounding: Is smoking a confounder?
Smoker Non-smoker
100
200
Drinker Non-drinker
60
40
40
160
Smoker
Non-smoker
100
200
Lung cancer
No lung cancer
75 25
25
175
OR=60x160/(40x40) = 6 OR=75x175/(25x25) = 21
07-04-2013
5
Confounding: example
Drinker Non-drinker
75
25
Lung cancer
No lung cancer
45
15
30
10
45 101.0
15 30sOR
Drinker Non-drinker
25
175
Lung cancer
No lung cancer
5
35
20
140
5 1401.0
35 20n sOR
Smokers Non-smokers
Confounding: example
Drinking Lung cancer X
Smoking
• Drinking is not associated with lung caner • Smoking is a confounder
Control of confounders
1. Confounder control in design phase 1. Randomization
2. Restriction
3. Matching
2. Confounder control in analysis phase 1. Standardization
2. Stratification
3. Multivariate analysis
Fra: K Rothman: Epidemiology – an introduction
A: Incidence of Down-syndrome by birth order
Fra: K Rothman: Epidemiology – an introduction
B: Incidence of Down-syndrome by maternal age
Fra: K Rothman: Epidemiology – an introduction
A: Incidence of Down-syndrome by birth order and
maternal age
07-04-2013
6
Residual confounding
• Broad confounder categories
– Smoker/non-smoker
Introduce a third variable
Mediator
Confounder
Effectmodifier
Exposure Outcome
?
Effectmodification
• When the association between exposure and outcome varies with respect to a third variable
• When effectmodification is observed it is incorrect to report only one estimate – stratum specific estimates should be reported
• aka ’interaction’
Risk of oral cavity and pharynx cancer by alcohol
intake and smoking
0
20
40
60
80
100
120
140
160
0 1-13 14-28 >28
Genstande (per uge)
Kræ
ftti
lfæ
lde (
per
100000 å
r) Ikke-ryger
Ryger
Effectmodification or interaction?
• The correct term is
”Effect-measure-modification”
• Effectmodification depends whether a absolute
or relative association measure is used (RD, IRD
vs. RR, IRR)
Effectmodification
+ asbestos - asbestos
+ smoking 50 10
- smoking 5 1
Incidence rate of lung cancer (cases pr 100.000 person-years)
Interest in whether the effect of smoking on lung cancer depends on asbestos exposure
Is there effectmodification?
07-04-2013
7
Effectmodification
+ asbestos - asbestos
+ smoking 50 10
- smoking 5 1
Incidence rate of lung cancer (cases pr 100.000 person-years)
IRD+asbestos=50-5=45 IRR+asbestos=50/5=10
IRD-asbestos = 10-1= 9 IRR-asbestos=10/1=10
Effectmodification when calculating IRD but not when calculating IRR
Additive or multiplicative interaction
• Normally the ratio measure is used
• This means that interaction is measured on a multiplicative scale
• Additive scale interaction is often also of interst – public health implications
Confounding
• Something we want to get rid off
• The association between exposure and outcome is the same in all strata, when stratifying on the confounder
• Mantel Haenzel can be used to adjust
• The weighted estimate will differ from the crude estimate
Effectmodfication
• Interesting which can tell us something about how causes co-work
• The association between exposure and outcome varies between strata, when stratifying on the confounder
• You cannot use Mantel Haenzel for adjustment
• Stratified estimates should be presented
Interaction: example
Drinker Non-drinker
100
200
Lung cancer
No lung cancer
50
50
50
150
50 1503.0
50 50OR
Interaction: example
Drinker Non-drinker
60
25
Lung cancer
No lung cancer
45
15
15
10
45 102.0
15 15sOR
Drinker Non-drinker
40
175
Lung cancer
No lung cancer
5
35
35
140
5 1400.57
35 35n sOR
Smokers Non-smokers
Introduce a third variable
Mediator
Confounder
Effectmodifier
Exposure Outcome
?
07-04-2013
8
Mediator
• Mediation refers to intermediate variables on the causal pathway from exposure to outcome
• In observational epidemiology much energy spent on confounder control
• Intermediate variables are less considered
• Recently this area has come into focus in methodological research
Why interested in mediation?
• Strengthen evidence that the main effect is causal
• Test of pathway-specific hypothesis • Focus on explaining an observed association that may be poorly
understood
• Evaluate and improve an intervention • Find mediating variables for improving interventions
• Studying (inexpensive) surrogate outcomes • Precursor for chronic disease
Woodward (1999) – just one classic
• Definition of a confounder:
– Be related to the disease, but not be a consequence of the disease.
– Be related to the risk factor, but not be a consequence of the risk factor.
Woodward (1999): Not a confounder
”(…) smoking and fibrinogen are both risk factors for CHD, but smoking promotes increased fibrinogen. Controlling smoking for fibrinogen would not be sensible because this would mean controlling the effect of smoking”
Epidemiologic textbooks
• Many textbooks do not deal with mediation
• Rothman et al (2002) give two examples:
Smoking
Heart disease
High blood pressure
Coffee Serum cholesterol
Rothman’s suggestions:
• “Any factor that represents a step in the causal chain between exposure and disease should not be treated as a confounding factor, but instead requires special treatment”
• “In the face of uncertainty, one might conduct two analyses, one in which the mediator is controlled and one in which it is not controlled”
• “The interpretation of the results would depend on which theory about the mediator were correct”
07-04-2013
9
Definitions
• Two pathways from exposure to outcome: – Direct effect
– Indirect effect
Exposure
Intermediate
Outcome
Definitions
• Effects of exposure on outcome: – Indirect effects: Exposure affects an intermediate variable which in
turn affects the outcome
– Direct effect: Effect of exposure is not through changes in the intermediate
– Total effect: Effect of exposure on outcome
Total effect = direct effect + indirect effects
Standard approach for estimating direct effects
• Multivariate regression models
– Estimate influence of exposure adjusted for intermediate variable
– Termed the controlled direct effects (Petersen 2006)
• Newer methods available!!!
Choosing study design
• Ecological study
• Cross-sectional study
• Case-control study
• Cohort study
• Randomized controlled trial
Exercise
Your research question: • Does smoking increase the risk of pancreatic cancer
Please consider: • What study design would you choose? • How large should the sample be? • How would you define exposure and outcome? • How would you obtain information on exposure and outcome? • And confounders? • What measure of association would you use
• What are the main limitations of the study design you choose
Exercise
Your research question: • Does former use of oral contraceptives increase the risk of
miscarriage?
Please consider: • What study design would you choose? • How large should the sample be? • How would you define exposure and outcome? • How would you obtain information on exposure and outcome? • And confounders? • What measure of association would you use
• What are the main limitations of the study design you choose
07-04-2013
10
Causality
• If we observe an assocation, next question is whether it reflects a causal relationship
• The ultimate goal of epidemiology
Epidemiological approaches
• Epidemiology is observational, unplanned and natural experiments
• Hierarchy of study designs – Clinical observations / case series – Ecological study – Cross-sectional study – Case-control study – Cohort study – Randomised trial
Ecological study Epidemiological approaches
• Epidemiology is observational, unplanned and natural experiments
• Hierarchy of study designs – Clinical observations / case series – Ecological study – Cross-sectional study – Case-control study – Cohort study – Randomised trial
Necessary / sufficient
• Necessary and sufficient
• Necessary, but not sufficient
• Sufficient but not necessary
• Neither sufficient nor necessary
Rothman’s pies
Three causal complexes
Each having 5 component causes
A is a necessary cause
07-04-2013
11
Attributes of the causal pie
1. Completion of a sufficient cause is synonymous with occurrence (although not necessarily diagnosis) of disease
2. Component causes can act far apart in time
3. Presence of a causative exposure or the lack of a preventive exposure
4. Blocking the action of any component cause prevents the completion of the sufficient cause
Causal "guidelines" – Hill criteria (1965)
Strength of the association
Consistency
Specificity
Temporality
Biological gradient
Plausibility
Coherence
Experiment
Analogy
Causal "guidelines" – Hill criteria (1965)
Purpose: Guidelines to help determine if
associations are causal
Should not be used as rigid criteria to be
followed slavishly
Hill even stated that he did not intend for
these "viewpoints" to be used as “hard
and fast rules.”
Hill concludes…
“Here then are nine different viewpoints from all of which we should study association before we cry causation.... None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required as a sine qua non. What they can do, with greater or lesser strength, is to help us make up our minds on the fundamental question --is there any other way of explaining the set of facts before us, is there any other answer equally, or more, likely than cause and effect?”
• NOTE: Temporality is a sine qua non for causality
“without which it could not be”
Counterfactual model of causality
• When we are investigating causality, we are interested in the individual counterfactual outcome
• Observe the present outcome AND the counterfactual outcome for the same individual
• NOT POSSIBLE
Counterfactual model of causality
• Population counterfactual effect
• When we are interested to measure the effect of a particular cause, we measure the – Observed amount of effect in a population who are
exposed
– Imagine the amount of the effect which would have been observed, if the same population would not have been exposed to that cause, all other conditions remaining identical
– The difference of the two effect measures is the population effect due the cause we are interested in
07-04-2013
12
Counterfactual model of causality
• The strength of randomized studies
– The two groups are identical
– Bias
• Perfect randomization
• Loss to follow-up (intention to treat)
• Also possible in observational studies
– Assumption of no unmeasured confounders!!
Three relationships
• If we observe an assocation, next question is whether it reflects a causal relationship
E O
B E C
O
E O