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3 Causal Models Part I: Sufficient Causes. Matthew Fox Advanced Epidemiology. Review of This Morning. “Modern” epidemiology Goal of etiologic research Valid and precise estimate of the effect of exposure on disease Why not everything is as we were taught Statistics

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3 causal models part i sufficient causes

3 Causal Models Part I: Sufficient Causes

Matthew Fox

Advanced Epidemiology

review of this morning
Review of This Morning
  • “Modern” epidemiology
  • Goal of etiologic research
    • Valid and precise estimate of the effect of exposure on disease
  • Why not everything is as we were taught
    • Statistics
  • Review of study designs, measure of effect
    • Odds ratios and case-control sampling
    • Hopefully changed a few minds (and set the tone)
this session
This Session
  • Why are we in this business?
    • What is the goal of epi investigations?
  • 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
    • 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

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?

smoking causes lung cancer so why doesn t every smoker get lung cancer
Smoking causes lung cancer.

So why doesn’t every smoker get lung cancer?

why study causal models
Why study causal models?
  • Helps understand why we do what we do
    • What do we mean when we say that smoking causes lung cancer?
  • Gives meaning to our measures of effect
    • Theoretical and practical
  • Helps clarify important epidemiologic concepts
a definition of a cause
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
    • Antecedent
    • Necessary
    • At the time it occurred
    • Other things fixed
history
History
  • Goes back to John Leslie Mackie (1964)
  • Necessary causes:
    • If x is a necessary cause of y, then y necessarily implies x
    • x does not imply y will occur
  • 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:
    • Insufficient and non-redundant parts of unnecessary but sufficient causes (INUS)
the sufficient cause model15
The Sufficient Cause Model
  • Lots of ways to get a disease
    • Think of each way as a pie
    • Called a sufficient cause
  • Mechanisms exist independent of us
    • But we’re susceptible to them if weacquire the components
  • Go through life picking up exposures and filling in pies
the sufficient cause model16
The Sufficient Cause Model
  • Person is susceptible to multiple diseases
    • Diseases have multiple sufficient causes
    • Each sufficient cause hasmultiple component causes
    • Each component causehas attributes
    • Shared components between sufficient causes
    • It is theoretically possible every case of outcome has a unique pie
sufficient causes
Sufficient Causes
  • Minimally sufficient
    • Each sufficient cause has a unique set of components and none is extraneous
  • Necessary cause
    • Component cause appearing in all sufficient causes for a disease
    • Poole proposes “universally necessary”
  • Complementary component causes
    • Set of component causes required to complete a sufficient cause, aside from one (exposure)
how much do we know
How much do we know?
  • U is often largest piece of a sufficient cause
    • We understand poorly disease causation
  • But if we could specify the mechanisms perfectly, could we predict all disease?
    • 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
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
      • Circumcision? STDs? Genetic factors? No use of microbicide?
  • Is each component necessary?
    • 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 model21
The Sufficient Cause Model
  • Components can be positive or negative
    • Lack of vaccination
  • Component causes should be specific
    • Can be identical except for timing
  • Don’t need to understand entire pie to prevent
    • Removing one piecerenders the pie incomplete
disease and causation
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
    • Model is deterministic
  • Each disease occurrence has a latency
    • Time between its occurrence and its detection
component causes exposures
Component Causes (Exposures)
  • Component causes (i.e. exposures we might want to study) have attributes
    • Dose
    • Duration
    • Induction period (NOT LATENCY)
  • Specifying the attributes

improves the resolving

power of the study

does smoking cause lung cancer
Does smoking cause lung cancer?
  • Smoking is too imprecise
    • How much for how long? What type?
    • Starting at what age?
  • 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
    • This biases towards no effect!
dose attributes
Dose attributes
  • Time weighted average dose
    • Grams of fat per day
  • Maximum dose
    • Highest adult body weight
  • Body weight or surface area scaled
    • Grams of alcohol per kilogram body weight
  • Cumulative dose
    • Pack years of cigarettes
duration attributes
Duration attributes
  • Total time of exposure
    • years employed
  • Biologically relevant time of exposure
    • Smoking before first pregnancy
  • Time of exposure beyond a minimum
    • Years of driving after age 25
  • Time of exposure after gathering another component cause
    • HIV infection after HPV infection
induction period attributes
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)
    • 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
induction time example
Induction Time Example

Diethylstilbestrol, adenocarcinoma of the vagina

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 processes assumed to occur in the interim

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

30

what about promoters or catalysts
Catalyst of diseases

Anything that speeds up (or slows down) the occurrence of a disease that would occur anyway

Are they causes of disease?

Remember the “at the time it occurred” part of the definition of a cause

Does it matter to you?

What about promoters or catalysts?
applications of the sufficient cause model
Applications of the sufficient cause model
  • The effect of the index condition, relative to the reference condition:
    • The number of completed sufficient causes among those with index condition
    • Minus number of completed sufficient causes among those with reference condition
  • Interaction between component causes
    • Arises when one or more sufficient causes contains both component causes
strength is determined by complements
Strength is Determined by Complements
  • Strength of a risk factor
    • Typically measured on the relative scale
  • Is determined by the relative prevalence in the population of the causal complements
    • Also affected by the competing risks of other sufficient causes for the same disease
imagine a gene environment interaction

G

U

E

Imagine a gene-environment interaction

U

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.

imagine a gene environment interaction36
Imagine a gene-environment interaction
  • Imagine a population where:
    • 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 interaction37
Imagine a gene-environment interaction

Randomized to get E

Randomized to not get E

U

U

Exposed

70% get disease

(10% U +

60% U/G/E)

Unexposed

10% get disease

(10% U)

G

G

G

G

U

U

U

U

E

E

G

G

G

G

U

U

U

U

E

E

G

G

G

G

U

U

U

U

E

E

RR = 70%/10% = 7

imagine a gene environment interaction38
Imagine a gene-environment interaction
  • Imagine a population where:
    • 10% get disease through U no matter what
      • 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 interaction39
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

G

U

U

E

RR = 20%/10% = 2.0

imagine a gene environment interaction40
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
      • Same as last
    • If we randomly assigned the exposure, would the relative risk be high or low?
imagine a gene environment interaction41
Imagine a gene-environment interaction

Randomized to get E

Randomized to not get E

U

U

U

U

Exposed

50% get disease

(10% U +

40% U/G/E)

Unexposed

40% get disease

(40% U)

U

U

U

U

G

G

U

U

E

RR = 50%/40% = 1.25

another example
Another example
  • Vaccines are usually extremely protective
    • What we often call a strong protective effect
  • But what if we tested our vaccine in a population where everyone had natural immunity?
    • 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
slide43

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)

illustrates arbitrariness of effects effect of folic acid on
Illustrates arbitrariness of effects: Effect of folic acid on:

Spina bifida

Assume same samplesize (N=100)

RD = 0.2

A B C

slide45

Illustrates arbitrariness of effects: Effect of FA on:

Neural tube closure

RD = 0.2

Same A B C

proportion of disease caused by
Proportion of disease caused by…
  • What % of all cases are caused by genes?
  • What % of are caused by environment (dietary phenalynine)?

G

U

E

Phenketunuria - 100% of all cases

slide48

Take home message 7:The % of all cases of a disease attributable to different causes can (and will) sum to > 100%

advantages of the scm
Advantages of the SCM
  • Applies to disease mechanisms in individuals
    • Tells us about how disease occurs, not just what caused it
  • Illustrates:
    • 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
Disadvantages of the SCM
  • Cannot easily apply to populations
    • Though our examples show can be used this way
  • Deterministic in nature
    • Assumes no randomness to disease causation
    • Doesn’t easily deal with continuous variables
  • Obscures importance of reference group definition
    • Comparison is between those with component cause and those without the component cause under study
disadvantages of the scm51
Disadvantages of the SCM
  • Difficult to assess validity of measures of association
    • No easy definition of bias
  • No distinction between mutable, immutable variables
    • Can sex can be a component cause?
  • Does not include temporal sequence
    • Could be revised to do so
  • Mechanisms cannot be fully articulated