Causal reasoning; confounding (introduction)

Summer Course:Introduction to Epidemiology August 28, 1330-1500 Causal reasoning; confounding (introduction) Dr. N. Birkett, Department of Epidemiology & Community Medicine, University of Ottawa

Session Overview • Review historical approaches to establishing causation • Current models of causation • Introduce the concepts of effect modification and confounding.

Scenario (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: Is his smoking the cause of his lung cancer?

Scenario (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: Now what is the cause?

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, often many sources contribute together as a cause. • Epidemiology aims to establish causes within groups • Etiological research.

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

Cause (3) • Risk factor • A behaviour, exposure or inborn characteristic which is known to be associated with a health-related condition. • Being Jewish is a risk factor for breast cancer. • Does being Jewish ‘cause’ breast cancer? • NO. • Genetic variation associated with within-religion breeding.

Cause (4) ASSOCIATION ≠ CAUSATION

Cause (5) A B A B C A B

Cause (6) Association is a matter of fact Causation is a matter of judgment. Needs a range of evidence, not a single study

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) • Casual • How do we establish that something is a cause?

Cause (8) • Some important theories of cause • Religious beliefs • Hippocrates • Imbalance of four humours • Phlegm • Yellow bile • Blood • Black bile • Miasmas

Cause (9) • Some important theories of cause • Aristotle • Explained in several of his books. • ‘We think we do not have knowledge of a thing until we have grasped its why, that is to say, its cause’ • Presented four categories of causes: • The material cause: “that out of which”, e.g., the bronze of a statue. • The formal cause: “the form”, “the account of what-it-is-to-be”, e.g., the shape of a statue. • The efficient cause: “the primary source of the change or rest”, e.g., the artisan, the art of bronze-casting the statue, the man who gives advice, the father of the child. • The final cause: “the end, that for the sake of which a thing is done”, e.g., health is the end of walking, losing weight, purging, drugs, and surgical tools.

Cause (10) • Some important theories of cause • Aristotle • Not everything needs all four type of causes • Need to consider all four types. • Final cause is given primacy in most cases

Cause (11) • 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

Cause (12) • Germ theory: Organism Disease • Epidemiological triangle: Agent Host Environment

Cause (13) • 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

Cause (14)Web of Causation sample

Cause (15) • 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 a cause of lung cancer

Cause (16) • Hill criteria (1965) • Strength of association • Consistency • Specificity (good if present but not needed) • Temporality (essential) • Biological gradient • Plausibility • Coherence • Experimental evidence • Analogy

Cause (17) • Counterfactuals • Person ‘A’ is killed when thrown from a car after a collision • Wasn’t wearing a seat belt • What would have been the outcome if he had been wearing a seat belt? • ‘Modern’ foundation for thinking about causation in epidemiology • Impacts on study designs • Directed Acyclic Graphs (DAGs) • ‘Colliders’ • An advanced topic (usually at the PhD level)

Cause (18) • One more ‘saying: The absence of evidence is not evidence of absence

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

Confounding (1) CRUDE table Vital Status Drug Use

Confounding (2) 45-79 80-99 Best ‘guess’of RR would be about 2.2, not 4.0!!

Confounding (3) • Previous example is confounding • The estimate of the effect of an exposure is distorted or confounded by a third factor. • We’ll come to ‘why’ in a minute. • Tables in previous slide are called stratified tables (here, age stratified). • Let’s consider a new situation based on the same crude table.

Confounding (4) 45-79 80-99 What is best ‘guess’ of RR? It depends on age. There is no single answer!

Confounding (5) • Previous example is effect modification • The effect of an exposure on an outcome depends on the level of a third variable • In this example: • For people under age 79, it looks like the drug protects against death • For people over age 80, it looks like the drug increases the risk of dying, • No single number or statement is an appropriate summary when this pattern occurs. • Links statistically to interactions. • Gene-environment interactions are a ‘hot’ topic of study.

Confounding (6) • Why was there confounding? • Numerical/mathematical answer can be given but let’s talk more conceptually. New Example • Does heavy alcohol drinking cause mouth cancer? • A case-control study was done which found an OR of 3.2 (95% CI: 2.1 to 4.9). • Does this prove the case? • Consider the following:

Confounding (7) Alcohol mouth cancer • This is what we are trying to prove. • But: • We know that smoking can cause mouth cancer. • And, people who drink heavily tend, in general, to be heavy smokers. • So, we might have:

Confounding (8) Smoking Alcohol Mouth cancer ??? • The association between alcohol and mouth cancer is explained away by the link to smoking. • Adjusted OR is 1.1 (95% CI: 0.6 to 2.0).

Confounding (9) • Confounding requires three or more variables. • Two variables, each with multiple levels, cannot produce confounding. • Three requirements for confounding • Confounder relates to outcome • Confounder relates to exposure • Confounder is not part of causal pathway between exposure and outcome

Confounding (10) In our initial drug use example, we have: • OR relating age and death in people without drug use = 2.8 • OR relating age and drug use in people who didn’t die = 68.5 • There is no suggestion that drug use causes death because people are getting older.

Confounding (11) • In ‘real’ research, these three ‘rules’ are not applied to identify confounding. • Inefficient and prone to false negatives • Instead, we compute an adjusted RR or OR and compare this to the crude RR or OR. • If these differ enough to ‘matter’, then we say there is confounding. • Usual guideline is a 10% change. • There is much more to this area but it goes way beyond this course.

Summary: Confounding • Confounding occurs when a third factor explains away an apparent association • This is a major problem with epidemiological research • If you measure a confounder, you can adjust for it in the analysis • Many potential confounders are not measured in research studies and so can not be controlled

Causal reasoning; confounding (introduction)