Transcript Sufficient Causes

3 Causal Models
Part I: Sufficient Causes
Matthew Fox
Advanced Epidemiology
1
2
Review of This Morning


“Modern” epidemiology
Goal of etiologic research
–

Why not everything is as we were taught
–

Statistics
Review of study designs, measure of effect
–
–
3
Valid and precise estimate of the effect of exposure
on disease
Odds ratios and case-control sampling
Hopefully changed a few minds (and set the tone)
This Session
Why are we in this business?

–
Things may have missed in intro epi

–
How causal models and causal inference
helps clarify what we do and how we do it
Sufficient Causes Model

–
4
What is the goal of epi investigations?
Rothman’s Model (Sufficient Cases Model)
I turn on a light switch and the light
doesn’t go on. The globe (bulb?) is
burned out.
What prevented the
light from going on?
5
I turn on a light switch and the
light does go on.
What caused the light
to go on?
6
Is there any disease for which
all cases are attributable to
one and only one cause?
7
Smoking causes lung cancer.
So why doesn’t every smoker get lung
cancer?
8
Is the occurrence of disease
deterministic or random within
what we know?
Is a coin flip random?
9
What is a “strong” risk factor?
What determines strength?
10
Why study causal models?

Helps understand why we do what we do
–

Gives meaning to our measures of effect
–

11
What do we mean when we say that smoking
causes lung cancer?
Theoretical and practical
Helps clarify important epidemiologic
concepts
A definition of a cause

An antecedent event, characteristic, or
condition that was necessary for the
occurrence of disease at the time it
occurred all other things being fixed
–
–
–
–
12
Antecedent
Necessary
At the time it occurred
Other things fixed
The Sufficient Cause Model
Ken Rothman
13
History


Goes back to John Leslie Mackie (1964)
Necessary causes:
–
–

Sufficient causes:
–
–

If x is a sufficient cause of y, then x necessarily implies y
Since other things can cause y, y does not imply x
Typical use of cause refers to:
–
14
If x is a necessary cause of y, then y necessarily implies x
x does not imply y will occur
Insufficient and non-redundant parts of unnecessary but
sufficient causes (INUS)
The Sufficient Cause Model

Lots of ways to get a disease
–
–

Mechanisms exist independent of us
–

15
Think of each way as a pie
Called a sufficient cause
But we’re susceptible to them if we
acquire the components
Go through life picking up
exposures and filling in pies
The Sufficient Cause Model

Person is susceptible to multiple diseases
–
–
–
–
–
16
Diseases have multiple sufficient causes
Each sufficient cause has
multiple component causes
Each component cause
has attributes
Shared components
between sufficient causes
It is theoretically possible every
case of outcome has a unique pie
Take home message 1:
All disease causation is multifactoral
and mechanisms are complex
17
Sufficient Causes

Minimally sufficient
–

Necessary cause
–
–

Component cause appearing in all sufficient causes
for a disease
Poole proposes “universally necessary”
Complementary component causes
–
18
Each sufficient cause has a unique set of
components and none is extraneous
Set of component causes required to complete a
sufficient cause, aside from one (exposure)
How much do we know?

U is often largest piece of a sufficient cause
–

But if we could specify the mechanisms
perfectly, could we predict all disease?
–
–

19
We understand poorly disease causation
Deterministic
Might be infinite combinations
In risk factor epidemiology, we focus on
one component and ignore the complement
So what might HIV infection look
like?

One SCM (pie) might be:
–
Exposed to HIV through sex
Unvaccinated (OK for now), no natural immunity
No condom use
–
Why doesn’t everyone exposed through sex get HIV? U
–
–


Is each component necessary?
–
–

20
Circumcision? STDs? Genetic factors? No use of microbicide?
If so, take away one and you prevent disease
Must be more causes than just the HIV virus
Other SCMs exist
–
Mother to child, transfusion, needle stick, etc.
The Sufficient Cause Model

Components can be positive or negative
–

Component causes should be specific
–

Can be identical except for timing
Don’t need to understand
entire pie to prevent
–
21
Lack of vaccination
Removing one piece
renders the pie incomplete
Disease and Causation


The sufficient cause acts when all of the
component causes have been gathered
Disease occurs at completion of temporally
last component cause
–

Each disease occurrence has a latency
–
22
Model is deterministic
Time between its occurrence and its detection
Component Causes (Exposures)

Component causes (i.e. exposures we
might want to study) have attributes
–
–
–

23
Dose
Duration
Induction period (NOT LATENCY)
Specifying the attributes
improves the resolving
power of the study
Does smoking cause lung
cancer?

Smoking is too imprecise
–
–

Infinite combinations
–

Each might have a different risk
Ignoring the attributes means we are lump
all exposures (from 1 lifetime cigarette to a
10 packs a day) together as exposed
–
24
How much for how long? What type?
Starting at what age?
This biases towards no effect!
Dose attributes

Time weighted average dose
–

Maximum dose
–

Grams of alcohol per kilogram body weight
Cumulative dose
–
25
Highest adult body weight
Body weight or surface area scaled
–

Grams of fat per day
Pack years of cigarettes
Duration attributes

Total time of exposure
–

Biologically relevant time of exposure
–

Years of driving after age 25
Time of exposure after gathering another
component cause
–
26
Smoking before first pregnancy
Time of exposure beyond a minimum
–

years employed
HIV infection after HPV infection
Take home message 2:
To study effects of exposures, the
exposures must be precisely defined
27
Induction period attributes

Induction period is the time between
completion of a COMPONENT cause (i.e. the
exposure of interest) and completion of the
SUFFICIENT cause (i.e. disease occurrence)
–
–
–
–
28
Induction period doesn’t characterize disease
Characterizes component cause-disease pair
Every disease has a component cause with zero
induction time
Failure to exclude induction time from person time
biases towards the null
Take home message 3:
Diseases don’t have induction times
29
Induction Time Example

Diethylstilbestrol, adenocarcinoma of the vagina
–
–
–

Other processes assumed to occur in the interim
–
–

30
A synthetic non-steroidal estrogen, given to pregnant women
to prevent miscarriage (’40s-’70s)
Exposure is known to have occurred during gestation
Cancer occurs in the offspring between 15-30 years of age
Other components in the causal pie still occur
Adolescent hormonal activity may be one
If outcome can’t occur before 10 years, don’t
include 1st 10 years of person-time
–
Similar to immortal person-time
What about promoters or
catalysts?

Catalyst of diseases
–

Are they causes of disease?
–
–
31
Anything that speeds up (or slows down) the
occurrence of a disease that would occur anyway
Remember the “at the time it occurred” part of the
definition of a cause
Does it matter to you?
Applications of the sufficient cause
model

The effect of the index condition, relative
to the reference condition:
–
–

Interaction between component causes
–
32
The number of completed sufficient causes among
those with index condition
Minus number of completed sufficient causes
among those with reference condition
Arises when one or more sufficient causes
contains both component causes
Take home message 4:
Interaction is ubiquitous
33
Strength is Determined by
Complements

Strength of a risk factor
–

Is determined by the relative
prevalence in the population of the
causal complements
–
34
Typically measured on the relative scale
Also affected by the competing risks of other
sufficient causes for the same disease
Imagine a gene-environment
interaction
U
U
G
E
Phenylketonuria (PKU) - a genetic disorder characterized by a deficiency in
the enzyme to metabolize the amino acid phenylalanine. Untreated, it can
cause problems with brain development. However, PKU is a rare genetic
diseases that can be controlled by diet, one low in phenylalanine.
35
Imagine a gene-environment
interaction

Imagine a population where:
–
–
–
36
10% get disease through U no matter what
60% of the population has U and G completed
If we randomly assigned the exposure (diet), would
the relative risk be high or low?
Imagine a gene-environment
interaction
Randomized to get E
Randomized to not get E
U
G
U E
G
U E
G
U E
G
U E
G
U E
G
U E
U
Exposed
70% get disease
(10% U +
60% U/G/E)
G
U
G
U
U
G
U
RR = 70%/10% = 7
37
U
U
G
G
G
Unexposed
10% get disease
(10% U)
Imagine a gene-environment
interaction

Imagine a population where:
–
10% get disease through U no matter what

–
–
38
Same as 1st example
10% of the population has U and G completed
If we randomly assigned the exposure, would the
relative risk be high or low?
Imagine a gene-environment
interaction
Randomized to get E
Randomized to not get E
U
U
Exposed
20% get disease
(10% U +
10% U/G/E)
Unexposed
10% get disease
(10% U)
G
U E
G
U
RR = 20%/10% = 2.0
39
Imagine a gene-environment
interaction

Imagine a population where:
–
–
40% get disease through U no matter what
10% of the population has U and G completed

–
40
Same as last
If we randomly assigned the exposure, would the
relative risk be high or low?
Imagine a gene-environment
interaction
Randomized to get E
U
U
U
U
Exposed
50% get disease
(10% U +
40% U/G/E)
Randomized to not get E
U
U
U
U
G
U E
G
U
RR = 50%/40% = 1.25
41
Unexposed
40% get disease
(40% U)
Another example

Vaccines are usually extremely protective
–

But what if we tested our vaccine in a population
where everyone had natural immunity?
–
–
–
42
What we often call a strong protective effect
Lack of natural immunity is a piece in the pie (causal
complement)
When it is common, the effect appears strong
When it is rare, the effect appears weak
Take home message 5:
“Strength” of an exposure’s effect is a
function of:
1) prevalence of causal complements
(what we usually ignore)
and
2) the % of all disease that goes through
mechanisms without the exposure
(U
for
short)
43
% with spina bifida
Illustrates arbitrariness of effects:
Effect of folic acid on: Spina bifida
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.85
0.65
0.55
Folic Acid
No FA
0.35
0.25
0.05
RD = 0.2
RR=0.2
44
Assume same sample
size (N=100)
A
RR=0.63
B
RR=0.75
C
% with neural tube closure
Illustrates arbitrariness of effects:
Effect of FA on: Neural tube closure
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.95
0.75
0.65
0.45
0.35
0.15
RD = 0.2
RR=1.3
45
Folic Acid
No FA
Same A
RR=1.4
RR=2.3
B
C
Take home message 6:
We are often best studying rare
occurrences when possible
46
Proportion of disease caused by…


What % of all cases are caused by genes?
What % of are caused by environment
(dietary phenalynine)?
G
U
E
47
Phenketunuria - 100% of all cases
Take home message 7:
The % of all cases of a disease
attributable to different causes can
(and will) sum to > 100%
48
Advantages of the SCM

Applies to disease mechanisms in
individuals
–

Illustrates:
–
–
–
–
–
49
Tells us about how disease occurs, not just what
caused it
How component causes act together
Attributes of component causes
How strength is function of complements
The ubiquity of interaction
Process of causation, although not complete
Disadvantages of the SCM

Cannot easily apply to populations
–

Deterministic in nature
–
–

Assumes no randomness to disease causation
Doesn’t easily deal with continuous variables
Obscures importance of reference group
definition
–
50
Though our examples show can be used this way
Comparison is between those with component cause
and those without the component cause under study
Disadvantages of the SCM

Difficult to assess validity of measures of
association
–

No distinction between mutable, immutable
variables
–

51
Can sex can be a component cause?
Does not include temporal sequence
–

No easy definition of bias
Could be revised to do so
Mechanisms cannot be fully articulated