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Bias in interpreting results of clinical studies
Bias in performing clinical studies
Attributable risk (AR)
DALY and QALY
Incidence and prevalence
Mortality rates and case-fatality
Relative and absolute risk
Positive and negative predictive value
Sensitivity and specificity
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Vaccination and herd immunity
Cross sectional study
Placebo effect and masking
Randomized control trial
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A cross- sectional study is a study design where an exposure and an outcome are measured at the same time.
For example, let’s say you want to figure out if people who are obese – which means having a body mass index or BMI of 30 or higher - have higher serum cholesterol levels compared to people who are not obese – having a body mass index below 30.
To do this, you might look at the medical records of 100 people to see who has high cholesterol levels and who has low cholesterol levels and compare that to how many people in each group are obese or not obese.
You can think about a cross-sectional study like a snapshot of the population at a certain point in time.
Since you can only collect the information you see in that one moment, you don’t know what happens before or after the snapshot was taken.
So, we can only collect information on prevalence – the proportion of exposures or outcomes that already exist at a certain time – and not incidence – the proportion of new exposures or outcomes that occur in a certain time period.
In terms of prevalence, there’s an outcome prevalence and an exposure prevalence.
The outcome prevalence is the proportion of people who have an outcome in the exposed group and the non-exposed group.
In a cross-sectional study this can be organized in a 2 by 2 table, with the exposure – obesity or no obesity – on the side and the outcome – high or low cholesterol levels – on the top, and each box is labeled a, b, c, or d.
Cell a includes individuals who have high cholesterol and who are obese; cell b includes individuals who have low cholesterol and who are obese; cell c includes individuals who have high cholesterol and who are not obese; and cell d includes individuals who have low cholesterol and who are not obese.
We can calculate an outcome prevalence to figure out if high cholesterol – the outcome – is more prevalent for people who are obese or not obese – the exposure.
Let’s say that there are 50 people in cell a, 15 in cell b, 5 in cell c, and 30 in cell d.
To find the outcome prevalence, we first calculate the proportion of people who have high cholesterol in the group that is obese – so “a” divided by “a plus b”, or 50 divided by 65, which rounds to 0.77.
A cross-sectional study is a type of observational study that assesses the relationship between exposures and outcomes at a single point in time. It can be used to study factors associated with an outcome. In a cross-sectional study, data are collected from a group of participants who have been followed for a certain length of time. Data are then analyzed to see if there is an association between the different exposures and the outcome measure. Cross-sectional are usually quick and cheap studies that help to sample large groups of people.
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