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EPI 5240:Introduction to Epidemiology

Concepts of causation and study validity October 19, 2009

Dr. N. Birkett,Department of Epidemiology & Community

Medicine,University of Ottawa

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Session Overview

• Review historical approaches to establishing causation

• Current models of causation

• Introduce the concepts of effect modification and confounding.

• Review what is meant by study validity.

Scenario #1

Mr. S, a 59 year old male, presents at your office with a 2 month history of progressive dysphagia and a 15 kg weight loss. He also has a marked loss of energy. Currently, he is even having trouble swallowing liquids like clear soup.

An emergency gastroscopy reveals a large mass in the upper-third of his esophagus. Biopsy confirms a squamous cell carcinoma.

Question:Question: Why did he get this tumor?Why did he get this tumor?

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Scenario #2 (1)

Mr. A is diagnosed with advanced lung cancer. He has a history of smoking 2 packs of cigarettes per day for the past 40 years.

Question:Question: Is his smoking the causeIs his smoking the causeof his lung cancer?of his lung cancer?

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Scenario #2 (2)

Further enquiry reveals that he has also:– Worked in a uranium mine for 30 years– Has very high levels of radon in his basement– Had both parents and two siblings die of lung

cancer before age 50.

Question:Question: Now what is the cause?Now what is the cause?

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Cause (1)

• Causation of disease is a complex process

• It is impossible to prove the cause for disease in an single person– In any one person, the disease may have

come from a wide range of sources

• Epidemiology aims to establish causes within groups– Etiological research.

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Cause (2)

What is a cause?

• John Stuart Mill– A change in ‘A’ is accompanied by a

subsequent change in ‘B’

• Oxford Dictionary– What produces an effect

• Mervyn Susser– Any factor which makes a difference

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Cause (2a)

• Rothman & Greenland– An antecedent event, condition or

characteristic that was necessary for the occurrence of the disease at the moment it occurred.

– Defines a component of a causal mechanism– Causal pies (later)

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Cause (3)

• Association– Two variables display a tendency to vary

together.• Could be measured by various means such as

correlation, Odd ratio, relative risk, kappa.

• Risk factor– Modifiable risk factor

• Determinant• Cause

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Cause (3a)

• Risk factor– A behaviour, exposure or inborn characteristic

which is known to be associated with a health-related condition.

– A risk factor need not be a cause but a cause must be a risk factor.

– Being Jewish is a risk factor for breast cancer.• Does being Jewish ‘cause’ breast cancer?• Genetic variation associated with within-religion

inter-marriage.

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Cause (3b)

• Modifiable Risk factor– Basic concept is that the risk factor could

potentially be changed• Contrast these two risk factors: sex vs. smoking

– Some researchers require that a modifiable risk factor be one where changing the risk factor reduces the probability of the outcome

• This requires the risk factor to be a cause and confuses two concepts

– Is the risk factor potentially changeable?– Does changing the risk factor make difference?

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Cause (3c)• Determinant

– A behaviour, exposure, etc. which increases the probability of the occurrence of the outcome

– Originated to address two issues• ‘a cause’ was frequently seen as something which

must produce the outcome. ‘Determinant’ was used to designate something which was part of a causal web (more later).

• ‘Risk factor’ became discredited as the basis for social action so ‘determinant’ became a more palatable way to describe the same info.

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Cause (4)

ASSOCIATION

≠CAUSATION

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Cause (5)

A B

A B

C

A B

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Cause (6)

• Association is a matter of fact

• Causation is a matter of judgment.– Needs a range of evidence, not a single study

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Cause (7)

• Associations can be:– Spurious

• False associations• Due to sampling error or bias

– Non-causal• True associations but not causal• Usually due to confounding (more shortly)• Reverse causality

– Casual

• How do we establish that something is a cause?

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Cause (7a)Characteristics of a cause:• Essential attributes

– Association– Time order– Directionality

• Can include:– Host– Environment

• Includes agents which are:– Active– Passive– Static

• Can be– Positive– negative

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Cause (7b)

Some key concepts and theories• Rationalism

– Causes identified based on reason rather the observation

• Empiricism (1700’s)– Inductive inference

• Deductive/falsification (1930’s)– Karl Popper– Science progresses by falsification of hypotheses

• Paradigm shift

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Cause (8)

• Some important theories of cause– Religious beliefs– Hippocrates

• Imbalance of four humours– Phlegm– Yellow bile– Blood– Black bile

• miasmas

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Cause (9)

• Some important theories of cause (cont)– Germ theory (1850’s)

• Single agent/single disease• Pasteur; Henle/Koch• Still dominates our thinking

– Multi-factor causation– Web of causation– Social determinants of the web– Component causes (causal pies)

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Cause (10)

• Germ theory:

Organism Disease

• Epidemiological triangle:

Agent

Host Environment

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Cause (11)

• Multifactorial causation– Supposed to be the basis for modern

epidemiology– No single factor causes disease– Multiple factors come together

• Tuberculus bacillus• Crowded housing• Poor nutrition• Weak immune system

TB

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Cause (11a)

• Multifactorial causation (cont)– Need to consider factors operating at multiple

‘levels’• Personal habits• Determinants of personal habits• Social factors• Political factors

– Often approached using multi-level modeling.

10/2009 24Atherosclerosis

Smokingweight

DIET

Serum Cholesterol

Cause (12)Web of Causation sample

PhiP

PhiP Sulfonate

N-hydoxy PhiP

N acetoxy PhIPN sulfonyloxy PhIP

DNA ADDUCTS

N acetoxy PhiP - GSH

N-hydroxy glucoronide

N- acetyl PhiP

SULTs (N-sulfonation)

NAT2 (N-actelyation)Largely inactive for HCA’s

CYP1A2

UGT1A1

In tissues; mild acidic medium

GSTA1

NAT2 (O-actelyation)SULT1A2 (O-sulfonation)

Repaired adduct (NER)XPD

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Cause (12A)

• Counterfactuals– Assume that JS had the experimental Rx in

an RCT and died.– What would have happened to him if he had

had the control treatment?

• Can be used as basis for study design– Controls in a case-control design need to

reflect the counterfactual experience of the case group

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Cause (12B)

• Buddihism/eastern philosophies extend the ‘web’ further (Dependent Origination)– Any phenomenon exists only because of the

existence of other phenomena in an incredibly complex web of cause and effect covering time past, time present and time future

– Everything depends on everything else – Everything in the Universe is interconnected through

the web of cause and effect such that the whole and the parts are mutually interdependent

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Cause (12C)

• A's parents are the direct causes for A's birth. In a negative sense, however, all the other men and women of the contemporary age who did not become A's parents are the indirect causes for the particular birth

Indra's net

HELP!!!

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AND SO ON

Rothmans’ pies

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Cause (13)

• Henle-Koch postulates– Parasite present in every case of disease– Parasite present in no other diseases– Parasite is isolatable and transmissible,

causing disease in others

• One organism one disease– This paradigm delayed recognition of smoking

as cause of lung cancer

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Cause (14)

• Hill criteria (1965)– Strength of association– Consistency– Specificity (good if present but not needed)– Temporality (essential)– Biological gradient

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Cause (14)

• Hill criteria (1965)– Strength of association– Consistency– Specificity (good if present but not needed)– Temporality (essential)– Biological gradient– Plausibility– Coherence– Experimental evidence– Analogy

Summary: Cause

• Can not establish causation in a single person

• Association between an exposure and outcome suggests possible causation but does not prove it.

• Rule out artifact before accepting association as ‘true’.

• Criteria for causation involve meta-analysis ideas and support from outside epidemiological studies

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STUDY VALIDITY

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Consider a precise number: the normal body temperature of 98.6F. Recent investigations involving millions of measurements have shown that this number is wrong: normal body temperature is actually 98.2F. The fault lies not with the original measurements - they were averaged and sensibly rounded to the nearest degree: 37C. When this was converted to Fahrenheit, however, the rounding was forgotten and 98.6 was taken as accurate to the nearest tenth of a degree.

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Scenario #1

• The Ottawa Citizen publishes a headline reading: ‘Scientific study shows smoking doesn’t cause lung cancer’.

• But, we know there are hundreds of studies showing the opposite.

• Why did this study disagree?

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Scenario #2

• US Cholesterol treatment guidelines:– < 200: do nothing– 200-240: Dietary intervention– > 240: pharmacological treatment

• Mr. Smith had a total cholesterol test done as part of a work-place annual examination and the level was 244.

• Follow-up test with family doctor was 198• Why the difference? What should you do?

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Validity (1)

• Any measurement is subject to error.– Labs make mistakes.– Poor machine calibration.– Biological variation in subject

• Fasting vs non-fasting• External stress• Diet

• Selection of subjects for study• Bad luck

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Validity (2)

• Actually, two key concepts are covered under my general title ’validity’– Reliability: Do you get the same result if you

repeat a study or test more than once?– Validity: Does the test or study give the ‘right’

answer?• E.g. does a test for depression actually identify

depressed people as opposed to people with anxiety or who are just ‘sad’?

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Validity (3)

DValidity low; reliability high

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Validity (4)

BValidity high; reliability low

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Validity (5)

• Concepts can be applied to individual tests and research studies.

• Will return to individual tests later when discussing screening and diagnostic tests.

• Focus for rest of the session is on validity issues in research studies

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Validity (6)

• Four possible explanations for a result from an epidemiological study:– Chance– Bias– Confounding

• A third factor explains the apparent result (more later)

– The TRUTH

• Must always consider other explanations before concluding result is true.

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Validity (7)

• For studies, reliability is mainly related to chance factors (random error)– The domain of statistics– Statistical methods attempt to quantify

amount of chance effect and aid interpretation of study despite this.

– Approaches can get very complex. Sometimes, the results reflect the models and not the data (a problem, but not discussed here).

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Validity (8)

• Bigger studies have less element of chance

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Validity (9)• Random chance in selection of subjects

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Validity (10)

• Before moving to consider bias, let’s take a side-trip.

• External vs internal validity of a study.

• External (generalizibility)– Relates to the degree to which the results of

the study reflect the underlying population– A study of the average BMI for Ottawa which

only looked at men would have poor external validity for the general population.

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Validity (11)

• External (cont)– Main issue concern use of volunteers

• Volunteers differ from the general population• BUT all studies are done in volunteers (informed

consent and ethical issues).

– Eligibility criteria– Limiting study to sub-groups of population

• Most studies of CHD etiology and treatment have been in men. Do they apply to women?

– Harder to study CHD in women due to lower incidence.

– Relationships generalize better than means.

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Validity (12)

• Internal– Does the study produce a valid estimate of

the effect under study?– Largely addressed by study design– Avoid (more to come on these)

• Selection bias• Measurement bias• Confounding

– Include an appropriate comparison group.

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Bias (1)

• A systematic error in a study which leads to a distortion of the results.

• Can be deliberate (fraud) or, much more commonly, due to design weaknesses or problems with study execution.

• A more serious issue with observational studies than RCT’s.

• Can cause the true RR or OR to be distorted away from, or towards, the null

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Bias (2)

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Bias (3)

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Summary

• What can we do about bias?

• Prevention is the key approach– Good design– Careful attention to issues in the field work of

the study– Good strategies to retain study participants.

• There are very few options to handle bias in the study analysis.