CAUSAL REASONING; CONFOUNDING (INTRODUCTION) Dr. N. Birkett, Department of Epidemiology & Community Medicine, University of Ottawa 5/6/2014 SUMMER COURSE: INTRODUCTION TO EPIDEMIOLOGY AUGUST 28,

Session Overview  Review historical approaches to establishing causation  Current models of causation  Introduce the concepts of effect modification and confounding. 5/6/2014

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? 5/6/2014

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? 5/6/2014

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. 5/6/2014

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 5/6/2014

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. 5/6/2014

Cause (4) ASSOCIATION ≠ CAUSATION 5/6/2014

Cause (5) 5/6/2014 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 5/6/2014

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? 5/6/2014

Cause (8)  Some important theories of cause  Religious beliefs  Hippocrates Imbalance of four humours Phlegm Yellow bile Blood Black bile  Miasmas 5/6/2014

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. 5/6/2014

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 5/6/2014

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 5/6/2014

Cause (12)  Germ theory: OrganismDisease  Epidemiological triangle: Agent HostEnvironment 5/6/2014

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 5/6/2014 TB

Cause (14) Web of Causation sample 5/6/2014

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 5/6/2014

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 5/6/2014

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) 5/6/2014

Cause (18)  One more ‘saying: The absence of evidence is not evidence of absence 5/6/2014

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 5/6/2014

Confounding (1) Vital Status Drug Use DeadAliveTotal Yes ,000 No , ,5002,000 5/6/2014 CRUDE table

Confounding (2) Vital Status Drug use DeadAlive Yes No ,000 Vital Status Drug Use DeadAlive Yes No ,000 5/6/ 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. 5/6/2014

Confounding (4) Vital Status Drug use DeadAlive Yes No ,000 Vital Status Drug Use DeadAlive Yes No ,000 5/6/ 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. 5/6/2014

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: 5/6/2014

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: 5/6/2014

Confounding (8) Smoking AlcoholMouth 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). 5/6/2014

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 5/6/2014

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. 5/6/2014

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. 5/6/2014

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 5/6/2014