Tutorial 1- Example 2 Flashcards
Q: How is the attributable risk for lung cancer in smokers calculated (risk difference)? What does it give an indication of? What is important when making this statement?
A: the rate of lung cancer amongst smokers minus the rate of lung cancer amongst non-smokers
It gives an indication of how many extra cases for which the exposure is responsible, making the important assumption that the relation between the exposure and the disease is causal (i.e. not explained by other confounding factors)
Q: What are the attributable risk and related measures typically used for?
A: help guide policymakers in planning public health interventions
Q: What is the main aim of epidemiological research? How?
A: investigate the association between exposure to a risk factor (e.g. smoking) and the occurrence of disease (e.g. lung cancer)
compare the incidence in a group of people exposed to the risk factor with a group who were not exposed.
Q: Two key concepts: risk and odds. What is the difference? Rare diseases?
A: A relative risk is much easier to interpret and makes much more sense to the layman - e.g. a relative risk of 7.0 means that the affected group has seven times the risk of a non-affected group
An odds ratio (the ratio of the relative odds of the disease occurring in Group A compared to it occurring in Group B) is more complex conceptually, but has some statistical advantages over the relative risk - essentially it’s more versatile.
The general rule though is that if the prevalence of the disease is <10% or so, the relative risk and the odds ratio will be approximately the same. The rarer the disease, the closer the approximation.
Q: How can we prove that an exposure causes a disease, rather than is merely associated with higher rates of that disease?
A: We try to eliminate (i.e. control or adjust for) the effects of confounders. Confounders are associated with both the exposure of interest and the outcome of interest (e.g. developing a disease or dying).
Q: How is confounding dealt with? Design stage? Analysis stage? (3)
A: at the design stage of a study by randomisation (in a randomised controlled trial), restriction, or matching (in a case-control study).
Stratification- a method for controlling the effect of confounding at the analysis stage of a study - risks are calculated separately for each category of confounding variable, e.g. each age group and each sex separately.
Standardisation- a method for controlling the effect of confounding at the analysis stage of a study. Used to produce a Standardised Mortality Ratio, a commonly used measure in epidemiology.
Regression- a method for controlling the effect of confounding at the analysis stage of a study - statistical modelling is used to control for one or many confounding variables.
Q: Larger samples are of course good in some ways. Why are they not often used?
A: not cost or time effective
Q: 95% CI for the odds ratio of 0.59-0.88. Means?
A: in the population the real value lies within the range for 95% of cases
Q: What does an odds ratio of 1 tell us? greater than 1? less than 1?
A: that exposure is no more likely in the cases than controls (which implies that exposure has no effect on case/control status)
tells us that exposure is more likely in the case group (which implies that exposure might increase the risk of the disease).
tells us that exposure is less likely in the case group (which implies that exposure might have a protective effect).
