13 Deriving Inferences from Epidemiological Studies Flashcards
Association
The co-occurrence of two or more variables at a frequency that is more than expected by chance
Spurious Association
When a common cause results in both the exposure and disease
Reverse Causality
When the disease causes the exposure
Confounder
Associated with both the exposure and the outcome
4 types of causal relationships
Necessary and sufficient
Necessary, but not sufficient
Sufficient, but not necessary
Neither sufficient nor necessary
Necessary and sufficient causal relationships
Without the exposure, the disease never develops
With the exposure, the disease always develops
Rarely ever occurs
Necessary, but not sufficient causal relationships
Each factor is necessary but by itself is not sufficient to cause the disease
Multiple factors are required
Ex: like TB (need the bacillus but also poor sanitation, etc)
Sufficient, but not necessary causal relationships
The factor alone can produce disease, but so can other factors acting along
The criterion of sufficient is rarely met by one factor alone
Neither sufficient nor necessary causal relationships
A factor by itself is neither sufficient nor necessary to produce the disease
A complex model which likely best depicts the causal relationships for most chronic diseases
Koch’s 3 postulates
- The organism must be present in all who have the disease
- The organism is not found with any other disease
- The organism can be isolated, cultured and capable of producing the disease in others
Sir Bradford Hill
Statistician on the MRC Streptomycin in Tuberculosis Trial (first RCT)
Studies on smoking and lung cancer
9 Criteria for Causation
Temporality Plausibility Consistency Strength Dose-response relationship Specificity Reversibility Coherence Analogy
Criteria for Causation: Temporality
The factor must have occurred before the disease developed
Measurement of the exposure is not required to precede measurement of the outcome though
The only criteria that is absolutely essential
Ex: smoking, then lung cancer
Criteria for Causation: Plausibility
Has coherence with the current body of biologic knowledge
Makes sense from the perspective of known biological mechanisms
Ex: carcinogens cause tumour induction
Associations often precede knowledge of underlying mechanisms though
Criteria for Causation: Consistency
Also called replication of findings
An association is more likely to be causal when it is observed in multiple epidemiological studies using a variety of locations, populations, and methods
Criteria for Causation: Strength of Association
Most commonly measured by risk, rate, or odds ratio
Strong association = strong risk factor
But can’t rule out causation if a weak association exists
Criteria for Causation: Dose-response relationship
The frequency or intensity of the outcome increases when the frequency or intensity of the exposure increases
Ex: more smoking = higher risk for lung cancer
Need to consider that a threshold may exist
Criteria for Causation: Specificity of association
When an exposure is associated with only one disease
Weakest of all guidelines
Rarely applicable, should probably be deleted
Criteria for Causation: Reversibility
Also called cessation of exposure
Risk of disease declines when the exposure is reduced or eliminated
Ex: stopping smoking reduces risk of lung cancer
Criteria for Causation: Coherence
Also called consistency with other knowledge
Consistent with other data
Should not conflict with what is known about natural history, distribution in time or place
Criteria for Causation: Analogy
Also called consideration of alternative explanations
When one causal agent is known, the standards of evidence are lowered for a second causal agent that is similar in some way
2 steps in the basic process to move to guidelines for causal inferences in use today
- Categorize existing evidence by the quality of its sources
- Evaluate the evidence of a causal relationship using standardized guidelines
