Chance, Bias, Confounding,Principles ofCausality

HSC 3102

Ms. SashaWalrond

August,2015

Outline1 Define chance 2 Define Sampling Error 3 Define Bias and types of bias 4 Use the principles of causality to interpret estimates

of association

Chance1 Chance is a random error appearing to cause an

association between an exposure and an outcome. 2 A principal assumption in epidemiology is that we can

draw inference about the experience of the entire population based on the evaluation of a sample of the population.

3 However a problem with drawing such an inference isthat the play of chance may affect the results of anepidemiological study because the effects of randomvariation from sample to sample.

Sampling Error1 Because of chance, different samples will producedifferent results and therefore must be taken intoaccount when using a sample to make inferencesabout a population. 2 This difference is referred to as the sampling error andthe variability is measures by the standard error. 3 Sampling error may result in:

1 Type 1 error- Rejecting the null hypothesis when it is true 2 Type 2 error- Accepting the null hypothesis when it is false

4 Reducing sampling error: (Cannot be eliminated but canbe reduced):

1 An appropriate study design

2 Use of an appropriate sample size

Bias1 Bias is any systematic error in the design, conduct or

analysis of a study that results in a mistaken estimateof an exposures effect on the risk of disease.

2 Types of biases in epidemiologic studies: 1 Selection Bias 2 Information Bias

Selection Bias1 Selection bias is an error in selecting a study group or groups within the study and can have a major impact on the internal validity of the study and the legitimacy of the conclusion. 2 If the way in which case and controls, or exposed and non-exposed individuals were selected is such that an apparent association is observed-even if , in reality, exposure and disease are not associated- the apparent association is the result of selection bias. 3 Response bias is a type of selection bias. Example, if we are studying the possible relationship of an exposure and a disease and the response rate of potential subjects is higher in people with the disease who were exposed than in people who were not exposed, an apparent association could be observed even ifin reality there is no association. Another example, those who agree to be in a study may be in some way different from those who refuse to participate. Volunteers may be different from those who are enlisted.

Information Bias1 Information bias can occur when the means for obtaininginformation about the subjects in the study are inadequate so thatas a result some of the information gathered regarding exposuresand/or disease outcomes is incorrect.

2 Types of information bias: 1 Recall Bias: In a case-control study, data on exposure are collected retrospectively. The quality of data is determined to a large extent by the patients ability to accurately recall past exposure(s). Those persons with a particular outcome or exposure may remember events more clearly or amplifytheir recollections. 2 Interviewer Bias: An interviewers knowledge may influence thestructure of the questions and the manner of presentation, which mayinfluence responses 3 Observer Bias: observers may have preconceived expectations of whatthey should find in an examination. 4 Loss to follow up- Those that are lost to follow up or who withdraw fromthe study may be different from those who are followed for the entirestudy. 5 Surveillance bias: The group with the known exposure or outcome maybe followed more closely or longer than the comparison group 6 Misclassification Bias: errors are made in classifying either disease or

exposure status

Association to Causation1 Once an association is established by an

epidemiological study (Relative Risk (RR) or Odds Ratio (OR) not equal to 1) the next step is to determine whether the observed association is causal.

Approaches for studying disease etiology In animal studies:

Advantages: allows the control of the exposure dose and environmental conditions and genetic factors precisely; minimise loss to follow up

Disadvantages: require extrapolation of findings across species, from animals to human populations. Certain diseases seen in humans have neither occurred been produced in animals. It is also difficult to extrapolate animal doses to human doses, and species differ in their responses.

In vitro systems ( cell or organ culture)Disadvantage: Difficulty in extrapolating from artificial systems to whole human organism.

If we want to be able to draw a conclusion as to whether a substance causes disease in human beings, we need to

make observations in human populations.

Approaches for studying disease etiology in human populations1 We cannot ethically randomise humans to most

common exposures 2 Epidemiology takes advantage of natural

experiments e.g. by comparing people who have beenexposed for non- study reasons to people who are not exposed.

3 A common sequence is followed in conducting humanstudies:

Types of association1 There are 2 types of association:

1 Real: (Is it a true (real) association) 2 False (Spurious): (Is it a false or spurious association)

2 A spurious association may be observed because ofchance or issues with the study design that lead to bias.

3 Examples of well-known real associations: 1 Smoking and lung cancer 2 Hypertension and stroke 3 Obesity and type ii diabetes

Interpreting Real Associations If an association is real, is it causal?Not if it is accounted for a third factor associated with both exposure and outcome (confounding).

Confounding1 A confounder accounts, totally or in part, for the

observed association between exposure and outcome. 2 The confounder is associated with both the

exposure and the outcome.

Confounding

Confounding

1. Smoking isa knownrisk factorforpancreaticcancer

2. Smoking isassociatedwith coffeedrinking, butis not aresult of

coffee drinking.

Causal pathways1 Direct or indirect

2 In direct causation, a factor directly causes adisease without any intermediate step. 3 In indirect causation, a factor causes a disease, butonly through an intermediate step or steps. 4 In human biology, intermediate steps are virtually alwayspresent in an causal process. Example, smoking inhalationof carcinogens various metabolic changes cellularabnormalities tissue abnormalities lung cancer

What is a cause?1 In epidemiology, the cause of disease may be

defined as: 1 ..An event, condition or characteristics 2 . That preceded the disease 3 and without which the disease event would not

have occurred 4 at all or until some later time

Rothmon and Greenland, 1998

1 There are four types of causal relationships thatare possible:

1 Necessary and sufficient

2 Necessary, but not sufficient 3 Sufficient, but not necessary 4 Neither sufficient nor necessary

Types of causal relationships1- Necessary and sufficient cause:

1 Without the factor, the disease never develops (the factor isnecessary)

2 In the presence of the factor , the disease always develops ( thefactor is sufficient)

3 This situation rarely ever occurs 4 Example, lead poisoning.

2- Necessary, But not sufficient cause:1 Without the factor the disease never develops, but 2 The presence of the factor does not inevitable lead to

disease 3 Example: tuberculosis (Tubercle bacillus is a necessary

factor, even though its presence may not be sufficient toproduce the disease in every infected individual.)

Types of causal relationships3- Sufficient, but not necessary cause:1 The presence of the factor invariably leads to disease,

but 2 The disease can occur even when the factor is absent 3 Also uncommon because very few causes are sufficient

on their own 4 Example,

Types of causal relationships4- Neither sufficient nor necessary cause:1 Disease can occur even if the factor is absent 2 Factor usually needs to work in combination with some

other factor 3 Most likely model of causality for most diseases,

especially chronic diseases 4 These are known as contributory causes and the

disease are called multifactorial diseases

Causality in epidemiology: important note1 In epidemiology, causation is determined by what

occurs in groups of people or populations as opposed to what occurs in any particular individual

2 From the group data, we can make predictions aboutindividuals, but we cannot expect that the predictionswill always be correct.

3 For example, everyone seems to know someone whosmoked four packs of cigarettes a day, had highblood pressure and drank like a fish, but lived until103.

4 However, health care policy decisions must be basedon the majority and not the exceptions.

Evidence for a causal relationship1 Historically, major diseases were infectious. 2 Evidence for causality related to proving that aparticular organism was responsible for a particulardisease. 3 Kochs postulates for causation: 1. The organism is always found with the disease 2. The organism is not found with any other disease 3. The organism, when isolated from one who has the disease, and cultured through several generations, produces the disease (in experimental animals) 4. Even without transmission, the regular andexclusive presence of the organism supportscausality.

Evidence for a causal relationship1 Kochs postulates apply to infectious diseases 2 What about non-communicable (chronic) diseases

where there is no infectious agent involved? 3 Mid- 20th century, guidelines for judging whether a relationship is causal were developed by an expert committee reviewing evidence about tobacco smoking and lung cancer.

Guidelines for judging whether an observed association is causal

1. Temporal relationship: If a factor is believed to be thecause of a disease, exposure to the factor must haveoccurred before the disease.

2. Strength of the association: Is measured by the RR or OR.The stronger the association, the more likely it is that therelationship is causal

3. Dose- Response relationship: A s the dose of exposureincreases, the risk of disease also increases.

4. Replication of the findings: If the relationship is causal, wewould expect to find it consistently in different studies andin different populations.

5. Biologic Plausibility: Coherence of theory/ finding with existing biological knowledge. Epidemiologic evidence

sometimes precedes biologic knowledge. Criterion should be applied with caution.

Guidelines for judging whether an observed association is causal6. Consideration of alternate explanations : In judging whether a reported association is causal, the extent to which the investigators have taken other possible explanations into account and the extent to which they haveruled out such explanations are important considerations. 7. Cessation of exposure: If a factor is a cause of a disease,we would expect the risk of the disease to decline whenexposure to the factor is reduced or eliminated. 8. Consistency with other knowledge: If the relationship iscausal, we would expect the findings to be consistent withother data. 9. Specificity of the association: An association is specific when a certain exposure is associated with only one disease, this is the weakest of all the guidelines and shouldprobably be deleted from the list.

References

1 Gordis L. (2014) .Epidemiology (5th edition).Philadelphia. Elsevier Saunders. (Chapter 14 and 15)2 Lecture notes from Dr. Reeta Gobin, November 2014

and January, 2015