Week 9 - Confounding Flashcards
What are disease determinants?
Most diseases are caused by high exposure to certain risk factors and/or low
exposure to certain protective factors.
These factors are collectively called disease determinants and differ from
disease to disease.
What does disease determinants include?
-Genetic factors
-Biological/physiological factors (e.g. ageing, other disease status,
biochemical factors)
-Lifestyle factors (e.g. diet, physical activity, smoking, alcohol consumption,
sleeping patterns)
-Psychosocial factors (e.g. stress, social isolation)
-Sociodemographic (e.g. socioeconomic position, educational attainment,
urbanization)
-Wider environmental factors (e.g. air pollution, impure water, toxic agents,
radiation)
What are the disease determinants of Cardiovascular disease?
What are the disease determinants of Type 2 diabetes?
What are the disease determinants of Alzheimer’s diabetes?
What are the disease determinants of breast cancer?
What is cofounding?
A parameter that is linked to the multifactorial nature of disease.
Similar to chance and bias, may introduce error.
A third factor (confounder) that explains all or part of the
association between an exposure and an outcome
What criteria does a potential cofounder need to fulfil?
- Has to be associated with the outcome of interest
- Has to be associated with the exposure of interest
- Should not lie in the causal pathway between exposure and
outcome
Note!
- Note 1: If a factor is associated with the outcome but not with the exposure
(or vice versa), then this factor cannot be a confounder in the specific
association! - Note 2: If a factor is associated with both the outcome and the exposure, but
lies in the causal pathway between the two, then this factor is not called a
confounder but a mediator!
-Note 3: Adjusting for mediators may have exactly the same effects on an
exposure-outcome association as adjusting for a confounder (i.e. association
becomes weaker, stronger, etc.). The only difference is in the interpretation!
What is the 3-step procedure for cofounding?
- Identify potential confounders
- Adjust for potential confounders
- Compare crude and adjusted estimates
What is statistical adjustment?
-The process of statistical adjustment aims at eliminating or reducing the confounding effect of potential confounders in any exposure-outcome
association
-In other words adjustment aims at removing any effects of the potential
confounder on the outcome, thus providing a more ‘clean’ estimate of the
exposure-outcome association
-After adjusting (also called ‘controlling’) our estimates for a potential confounder, these estimates are then said to be adjusted for
that confounder (i.e. age-adjusted estimates, smoking-adjusted, etc.)
How do we apply statistical adjustment?
What statistical adjustment is doing is keeping the potential confounder
constant (i.e. making it a non- variable) and re-calculating the estimates for the
exposure-outcome association (i.e. Odds Ratios, mean difference, regression
coefficient, etc.)
Explain the process of statistical adjustment.
- One way of adjusting for a given confounder is to stratify the analysis based on
the categories of the confounder.
This process of stratification involves performing the analysis for the exposure-outcome association separately in the categories of the confounder (i.e. for
smokers and non-smokers if smoking is the confounder). - This will therefore give us 2 different (stratified) estimates (assuming that the
confounder had 2 categories) for the exposure-outcome association, one for
each category of the confounder variable - These stratified estimates will then need to be combined, in order to get a
combined (adjusted) estimate.
This adjusted estimate is ‘clear’ of any confounding effect from the specific
confounder, since the analysis was performed separately in its categories and the
results were then combined. - Note that if the exposure-outcome association is substantially different in the
categories of the confounder, then the estimates cannot be combined and we
present them separately (this is known as effect modification).
What is a crude estimate?
Crude estimates are simply estimates for exposure-outcome associations (Odds
Ratio, mean difference, etc.) before applying any adjustment.
What is an adjusted estimate?
Adjusted estimates are estimates for exposure-outcome associations after
applying statistical adjustment for any potential confounder
It aims to answer the question: “ What would the exposure-outcome association be if everyone had the same cofounder value?”
How do we combine two estimates?
The two estimates are combined in a weighted manner, based on the sample size of
each category of the confounder variable.
How can we determine if something is a cofounder?
Compare crude and adjusted estimates.
