Definitions Flashcards
Q: Attributable risk.
A: a measure of exposure effect that indicates, on an absolute scale, how much greater the frequency of disease in the exposed group is compared with the unexposed, assuming the relationship between exposure and disease is causal (an important assumption).
It is the difference between the incidence rate in the exposed and non exposed groups, i.e. it represents the risk attributable to the exposure of interest.
Q: Case.
A: an individual with the outcome under study (in a case-control study).
This could be a person who has the disease, health disorder, or suffers the event of interest (by “event” we mean a change in health status, e.g. death in studies of mortality or becoming pregnant in fertility studies).
The epidemiological definition of a case is not necessarily the same as the clinical definition.
Q: Case-control study. What’s explored? Always results in?
A: study in which individuals are selected on the basis of whether or not they have the outcome of interest; usually some relatively rare outcome.
Exposure (risk factor) status is explored to establish whether the exposure is more common in the case (those that have the outcome) or control (those that do not have the outcome) group.
This type of study always results in an odds ratio, for example comparing the odds of being exposed (e.g. a smoker) in those who had the outcome (e.g. pancreatic cancer), with the odds of being a smoker in those who did not have pancreatic cancer.
Q: Count.
A: the most basic measure of disease frequency is a
simple count of affected individuals. The number (count) of cases that occurred in a particular population is of little use in comparing populations and groups.
Q: Chi squared test.
A: a statistical procedure for testing whether two proportions are similar (e.g. whether the proportion of lung cancer cases in males who smoke is significantly different to the proportion of lung cancer cases in males who do not smoke).
Q: Cohort study. Method? Usually results in?
A: study in which individuals are selected on the basis of exposure status and are followed over a period of time to allow the frequency of occurrence of the outcome of interest in the exposed and non exposed groups to be compared.
Take a group of people, note whether they’ve been exposed or not, observed them over time and wait for them to get ill, to die etc.
This type of study typically produces a relative risk.
Q: (95%) Confidence interval. Example?
A: an estimated range of values calculated from a given set of sample data which are likely to contain the ‘true’ population value.
E.g. a range of values around a relative risk measure which would, in 95% of such studies, contain the ‘true’ risk (the true risk being the relative risk that would be obtained if the study had included the entire population of patients).
By “contain (or ‘span’) the true value”, we mean that the true value lies above the lower value of the confidence interval but below the upper values of the confidence interval.
For example, for a 95% confidence interval of 1.2 – 3.4, we can say that we are 95% confident that the true value of risk will not be lower than 1.2 and will not be higher than 3.4.
Q: Confounding.
A: a possible explanation for the study finding if confounding variables have not been taken into account in the study.
Q: Confounding variable.
A: a factor that is associated with both the exposure and outcome of interest. Common confounders include age, smoking, socio-economic deprivation.
Q: Control.
A: (as opposed to a case) – a person without the outcome under study (in a case-control study), or a person not receiving the intervention (in a clinical trial).
Q: Exposure.
A: when people have been ‘exposed’, they have been in contact with something that is hypothesised to have an effect on health e.g. tobacco, nuclear radiation, pesticides in food, HRT. These are typically called ‘risk factors’ of disease.
Q: Incidence. Not same as? Measures? Calculation?
Interpreted as?
A: the number of new cases of the outcome of interest occurring in a defined population over a define period of time. Note that this is not the same as prevalence, which includes new and old cases.
Incidence measures events (a change from a healthy state to a diseased state).
number of disease free people at beginning of time period
This measure of incidence can be interpreted as the probability, or risk, that an individual will develop the disease during a specific time period.
Q: Matching.
A: method for “controlling for” (i.e. effectively removing) the effect of confounding at the design stage of a case-control study; controls are selected to have a similar distribution of potentially confounding variables to the cases, e.g. they are said to be “matched” for sex if there are similar proportions of men and women in both groups.
Q: Normal distribution.
A: a set of values and frequencies that describe many things in nature, at least approximately, e.g. height, weight, blood pressure. This symmetrical distribution (see Figure 1) is the basis of many statistical tests because, if you know the average value (usually called the mean) and the standard deviation, then you can draw every point of a normal distribution and you know what proportion of values are greater than (or less than) any given point
Q: Null hypothesis. Comparing rates? Case-control studies? Normally distributed variables?
A: the statistical test tries to disprove is that there is no difference between the two groups in the measure being tested.
If we are comparing rates, then the null hypothesis would be that rate A equals rate B, which means that the relative risk (rate A divided by rate B) equals 1.
For case-control studies, the null hypothesis would be that the odds of exposure for group A equal the odds of exposure for group B, i.e. the odds ratio (odds of exposure for A divided by the odds of exposure for B)
equals 1.
However, for normally distributed variables such as blood pressure (BP) in Question 4, the null hypothesis would be that the average BP for group A equals the average BP for group B, i.e. the difference between the two average BPs equals 0.
