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what is randomisation and what does it achieve

randomly splitting participants into groups. Eliminates confounding because known and unknown confounders should be balanced


cluster randomisation

randomise groups (clusters) of participants instead of individuals, which may be difficult. E.g. GP practices, hospital wards


stratified (block) randomisation

If you want to be certain that important confounders are eliminated. Randomise individuals within each age group, sex, or hospital


cross over studies

Each person gets both treatments -confounding is effectively eliminated. Can only be done for long-term conditions and treatments that do not cure disease


protecting randomisation

  • concealment of allocation - Make sure that people can’t cheat and pick the treatment that they prefer
  • intention-to-treat analysis - Analyse participants as randomised, this reflects real world
  • blinding - of researchers and participants
  • complete follow-up
  • use large numbers - balance confounding


per-protocol analysis

Analyse as treated (not necessarily as randomised)

Lose the benefit of randomisation


potential sources of bias

  • lack of blinding - participants or researchers may act differently if they know the groups
  • loss to follow up - cant analyse their results
  • non-adherence - e.g. stop taking treatment


4 strengths of RCTs

  1. The best study design to test an intervention
  2. Well conducted studies should eliminate confounding and bias
  3. You can calculate Incidence, Relative Risks, and Risk Differences
  4. The strongest design for testing cause-and-effect associations


clinical equipoise

must have genuine uncertainty about benefit or harm of intervention in RCTs


practical issues of RCTs

  1. can be expensive - need many participants, long time
  2. often funded by pharmaceutical companies - unlikely to fund studies for cheap treatments
  3. Participants in RCTs are often not representative - They need to meet all the inclusion criteria

  4. RCTs are not efficient for rare outcomes


internal validity

whether or not there is a real association in the group you looked at, or if its due to chance, bias or confounding


external validity

can findings be generalised to broader population


effect of increasing sample size in random sample

  • Makes it more likely to represent sample
  • Reduces sample variability (standard deviation etc)
  • Increases precision of parameter estimate - Confidence intervals get narrower


2 interpretations of confidence intervals

  • A 95% Confidence Interval represents the range of values within which the parameter will lie between 95% of the time if we continue to repeat the study with new samples
  • 95% CI = We are 95% confident that the true population value lies between the limits of the confidence interval


what does it mean if 0 in included in a CI for difference between two means

result could be due to chance


null hypothesis

There is no association between exposure and outcome

There is no difference between groups

Parameter equals null value

if this is true, any differences must be due to chance


alternative hypothesis

There is an association between exposure and outcome

Parameter does not equal null value


p value

The probability of getting an estimate as extreme as the one that you have observed if there is really no association

i.e. probability of it occurring by chance


statistically significant p value

P values < 0.05

Less than 5% probability of a result this extreme due to chance

We can reject the null hypothesis and accept alternative hypothesis


not statistically significant

if P value > 0.05

More than 5% probability of this result occurring by chance

We cannot reject the null hypothesis


type I error

false positive

occurs when we find a “statistically significant” result when there is no real difference

we reject H0 even though it is correct and the difference is due to chance

P(type I error) = alpha (significant level - usually 0.05)


type II errors

false negative

occurs when we don’t find a “statistically significant” result when there is a real difference

we fail to reject H0 even though it is false and the difference is real

More likely if we use smaller samples

Also more likely if we look for a smaller P value (e.g. ≤ 0.01)



clinical importance

if it will have a substantial effect/make a decent difference that makes it work funding


selection bias in case-control studies

Controls not representative of the population which gave rise to cases

If inclusion/exclusion criteria differ between cases and controls



selection bias in cohort studies

Loss to follow up

If comparison group selected separately from exposed group can lead to bias - healthy worker effect


how can measurement error occur

Participants provide inaccurate responses

Data is collected incorrectly/inaccurately


effect of measurement error

in a descriptive study could over/underestimate prevalence

In an analytic study can lead to misclassification


non-differential misclassification

When measurement error and any resulting misclassification occur equally in all groups being compared


differential misclassification in cross-sectional study

people with the outcome might report the exposure differently to those without the outcome


differential misclassification in a case-control study

cases might more accurately recall past exposures compared to controls