Part 2 Flashcards
Random error and beyond
What is random sampling error and why does it happen?
Random sampling error occurs when we can’t include everyone eligible in a study so each study will have a different sample which will give a different result from the true population just due to chance
What does the 95% confidence interval measure?
The amount of random error in the estimates of EGO, CGO, RD and RR (that we’re trying to calculate for the whole population).
How to reduce random sampling error
Take a large sample because a larger sample will be more likely a closer representation of the actual population
Taking a random sample means that the chance that someone gets into the sample from the population is
equal
What is random measurement error (two causes)
Due to humanness the ability to make biological measurements the exact same way is poor. Also there is an inherent variability in biological measures that still leads to variation in measurements despite measuring instruments being perfect (eg BP)
What are the 3 types of random error
Random measurement, random sampling error and random allocation error
How can you reduce random measurement error
Take multiple measures of the exposure/ outcomes and taking the average. Also using more objective/ automatic measurement devices
Why does taking multiple measurements/ having more people in the study- help to achieve a more accurate result (reduce random allocation error)
It reduces the number of extreme events by chance because there is a tendency to get a bell shaped distribution curve when dealing with events only caused by randomness
When does random allocation error occur
when people are allocated randomly to CG and EG but the sample size is small so confounding could still be there just due to chance.
The 95% CI can be likened to
a net trying to catch the true value that is related to the actual population the participants were recruited from
A wide 95% CI means the amount of random error is
a lot
What population is the 95% CI trying to tell me about
the whole population that my sample was taken from, not my sample population
Is there something special about ‘95%’ in 95% CI
no
What is the easy definition of a 95% CI
There is a 95% chance that the true value in a population (from which participants were recruited from) lies within a 95% confidence interval. (assuming no NR error)
What is the true definition of a 95% CI
In 100 identical studies using samples from the same population, 95/100 of the studies 95% CI, will include the true value for the study
What is the ‘no effect’ line and when is it shown?
It is a vertical dotted line at 1 and 0 For CIs relating to RR and RD respectively.
When the CI for EGO and CGO do not overlap what is this called and why is it important?
This means that the EGO is statistically significantly different from CGO. And this means that the true values for the underlying population are likely to be truly different from each other, not just because of random error.
When the CIs for CGO and EGO don’t overlap, this means that the CIs for RR and RD will … and this is called
also not overlap, statistically significant
When a CI for RR/ RD crosses the ‘no effect’ line it doesn’t mean that there is no association between exposure and outcome, it just means that there is too much …
random error to determine whether there is a real difference between EGO and CGO
What is the most common reason for CI with differing widths ?
The size of the sample being studied
Clinically significance is about whether the Clinician
can accurately judge to which way the true value lies within the confidence interval.
Clinically significant RR and RD are ones that affect
more people
If a CI is narrow, does that mean that it is more clinically significant
no
What is meta analyses
Meta analyses is the combining of RCT with small samples that study the same question to produce a summary estimate of the effect
Why do we do meta analyses
small RCTs have too much random error to have statistically significant results, but combining them is equivalent to undertaking one large RCT, and therefore they can find out whether the intervention has any real effect.
What are two features that help to differentiate epidemiological studies ?
How participants are allocated to EG and CG and how are the outcomes measured
What are the two types of allocation to EG and CG and give an example of studies that follow these allocations
Allocation by experiment -> RCT and Allocation by observation -> cohort, cross sectional
What are the two ways outcomes can be measured and give an example of the studies
Longitudinal- outcomes are measured as they occur during the follow up period (cohort/RCT)
Cross sectional- outcomes are measured at the same time that participants are allocated to EG and CG (measuring prevalence)
What the are the differences between ‘descriptive’ and ‘analytical’ studies and why are they not so useful for distinguishing between study types
Descriptive studies are about stating the frequency of health behaviours, risk factors and outcomes. Analytic studies are about analysing these things. They aren’t useful because all epidemiological studies do both.
What are the biggest problems in ecological cross section
measurement errors–> eg. including local sellers of dairy goods vs sales by the big companies
What are the strengths of Ecological studies
They are good at measuring risk factors and effects of intervention. Data more likely to be already collected. Good at finding the effects of behaviours that are too common in one country to measure the effects. efficient for rare outcomes.
What are the weaknesses of Ecological studies
Prone to confounding because there are more than one difference between different countries and it is difficult to adjust for this in analyses because there isn’t many measurements of the confounders available.
What type of error is present in both ecological cross sectional and longitudinal studies and how does it expressed in each?
Measurement error in ecological x sectional comes down to not being able to accurately measure the exact thing for every single person in a population- butter providers
In longitudinal studies, measurement error is in the objective way of measuring which stops cultural differences influencing the results.
What are the weaknesses of Cross sectional studies
They can be subject to recruitment error- sample needs to be representative.
Not good at determining causal relationships- Reverse causality
Define reverse causality
This is when the outcome causes the person not to be exposed rather than the exposure causing the outcome- eg people with poor lung function don’t do a lot of exercise rather than not a lot of exercise causing poor lung function.
Reverse causality can be a problem in cohort studies too provided that
the exposures were measured after the person has developed the study outcome.
What are the strengths of cross sectional studies
Maintenance error isn’t a problem
What are RCTs really good at?
assessing the benefits/ risks/ effectiveness of interventions and being less prone to confounding
Types of interventions include
preventative, therapeutic and screening
Weaknesses of RCTs
Often unethical and impractical . Costly so it leads to small studies with more random error. RECRUITMENT error- May not be able to apply results to the real population if participants are highly motivated
MAINTENANCE error- if its a long study, but this can be kept relatively similar in EG and CG groups if there is blinding.
ALLOCATION error- when the random allocation isn’t done properly
Weaknesses of cohort studies
MAINLY CONFOUNDING
ALLOCATION by measurement–> can have measurement errors.
MAINTENANCE errors–> people may change their allocation
What is a systematic review
It is the first step of meta analysis, collecting studies with similar research questions
Meta analyses is an
analytical technique used to combine the results of studies with low amount of NR error to reduce the amount of R error
Health related decisions are better based on systematic review of studies rather than
an individual study or meta analysis based on unreviewed studies
the validity of a systemic review of studies depends on the
validity of each individual study included in the review
Purpose of cohort studies
to identify causes of disease
Strengths of cohort studies
Recall bias is avoided because the exposure is measured before the outcome, and there is a clear timeline of events so reverse causality is harder to occur. Usually cheaper than RCTS
Purpose of ecological studies
identify trends in causes of disease incidence and prevalence
