Stats

Question 1

Null hypothesis

Answer 1

A strawman set to argue against the data

-If your results prove your hypothesis, you reject the null hypothesis

Question 2

Type 1 error

Answer 2

Rejecting the null hypothesis when it is in fact true

A False positive result.

You’re wrong, but you don’t realise it.

Question 3

P value

Answer 3

The probability of a Type 1 error occurring

i.e. the chance that you’re wrong, but you don’t realist it and the null hypothesis is actually true

Usually <0.05 arbitrarily set

Question 4

Type 2 Error

Answer 4

Accepting the null hypothesis when it is in fact false

A false negative result

You decided you weren’t right when you were

Question 5

Power

Answer 5

Likelihood of finding an effect when it is present

Power = 1-p(Type 2 Error)

So If most studies aim for a power of 80%, then it means that 80% of the time if the effect is there it will be noted.
Alternatively, the type 2 error rate would be 20%

Question 6

Modifiers of Power

Answer 6

Bigger is better:

• Size of effect
• Sample size

Lower is preferred:

• Desired significance
• Standard deviation

Question 7

Risk

Answer 7

The chance of something occuring

E.g. A population has a 5% chance of dying when they present with PE

Question 8

Odds

Answer 8

The chance of something occurring compared with it not occurring

E.g A population has a 5% chance of dying compared to a 95% chance of surviving when they present with a PE.

Therefore: 5/95 = 1/19
For every 1 that dies, 19 survive

Question 9

Relative Risk

Answer 9

The chance of something occurring relative to the chance of it occurring under different circumstances.

E.G. A population has a 5% chance of dying when they present with PE and a 15% chance of dying when they present with PE + hypotension​

Therefore: 5%/15% -> 1/3​

Therefore: A person presenting with a PE without hypotension has 1/3 the risk of death relative to one presenting with a PE with hypotension​

Question 10

Absolute Risk Reduction

Answer 10

The absolute difference in chance of something occurring compared to the chance of it occurring under different circumstances​

E.G. A population has a 5% chance of dying when they present with PE and a 15% chance of dying when they present with PE + hypotension​

Therefore: 15% - 5% -> 10%​

Therefore: the absolute risk of death has increased by 10%​

Question 11

Number Needed to Treat

Answer 11

NNT = 1/ARR

E.G ARR = 10%

Taking 10 people and treating them prevents 1 death

Question 12

Odds Ratio

Answer 12

The Odds version of relative risk

The odds of something occurring relative to it occurring under different circumstances​

E.G. A population has a 5% chance of dying when they present with PE and a 15% chance of dying when they present with PE + hypotension​

Odds death nohypo = (1/20)/(19/20) = 1/19​

Odds death hypo = (3/20)/(17/20) = 3/17​

Odd ratio death hypo relative to death nonhypo = (3/17)/(1/19)​

Therefore: (3/17)x(19/1)​

Therefore: (3x19)/17 -> 57/17 -> 3.35​

The odds of death are 3.3x higher with hypo than without​

Note: it’s not 3x higher like with the relative risk

Question 13

Which study uses odds ratio instead of relative risk

Answer 13

Case control

Question 14

Cross Sectional Study

Answer 14

Measure the prevalence of disease and exposure in a random sample of a population at a time point​

Pros​

• Cheap and easy​
• Questionnaires​

Cons​

• Recall bias from self reporting​
• Can’t determine which came first​ (Temporality) ​
• Non response bias (Those who participate may differ from those who don’t​)
• Bad for rare issues as it randomly samples a population​
• Confounding

Question 15

Case Control Study

Answer 15

Sample disease states and then ask retrospectively about exposure​

Pros​
- Efficient for rare diseases and outbreaks​

Cons​

• Hard to find matched controls​
• Can’t determine which came first​
• Cause/effect impossible to ascertain​
• Can’t be used for prevalence, incidence, and risk​
• • You select a control for every case, so you can’t compare one to the other like that​
• • Have to use regression to spit out an odds ratio​
• Recall bias​
• Confounding

Question 16

Prospective Cohort study

Answer 16

Measure risk factors in people disease free at baseline​

Follow them over time, wait for them to develop the outcome, and calculate risk/rates of developing disease​

Pros​

• Exposure occurs prior to outcome​
• Able to study multiple outcomes​
• Can be used for rare exposures and multiple outcomes​
• Can be used for prevalence and incidence​
• Usually generalisable due to sampling from general community​
• Avoid recall bias​

Cons​

• Expensive​
• Take a long time​
• Confounding​
• Loss to follow-up

Question 17

Retrospective cohort study

Answer 17

Cohort assembled after an outcome has occurred using stored data​

Pros​

• Exposure occurs prior to outcome​
• Cheaper and faster than prospective​

Cons​
- Data quality may be limited

Question 18

Nested Case Control

Answer 18

Cases and controls drawn from a prospective cohort study​

Cases who develop an outcome during follow-up are compared with matched controls​

Controls taken from the cohort who didn’t develop the outcome​

Pros​

• Efficient for expensive measurements​
• • Gene assays, ELISA, etc​
• Blood collected prior to disease​
• • Avoids reverse causality issues​

Cons​
- Similar to cohort

Question 19

RCT

Answer 19

Gold standard​

Two matched populations undergo different interventions with (ideally) pre-specified outcomes to be observed​

Pros​

• Randomisation minimises confounding​
• Blinding minimises bias​

Cons​

• Expensive​
• Non-compliance​
• Loss to follow up reduces statistical power​
• Only look at short term outcomes​
• Not always ethically possible​
• • E.g randomising people to ETOH or cigarettes​
• May not have generalisable results​
• • I.E may be internally but not externally valid​

Internal validity = study was statistically robust and followed a well set up protocol​

External validity = that the study design would actually be applicable to the population you want to intervene upon

Question 20

Per Protocol

Answer 20

Only patients who followed protocol are included in the analysis​

Loses randomisation

Question 21

As Treated​

Answer 21

Unlike per protocol, which removes the non-adherents, this approach analyses according to the treatment they received​

Also loses randomisation

Question 22

Likelihood Ratio

Answer 22

The probability that a result would be expected in a patient with the disorder compared to the probability that the same result would be expected in a patient without the disorder​

Allows adjusting pre-test probability via a given test result to post-test probability

Question 23

Positive LR

Answer 23

(Probability individual with disease has a positive test)/(Probability individual without disease has a positive test)​

Note: The numerator is the same definition as sensitivity​

Note: The denominator is the complement of the specificity (1-spec)​

​

Question 24

Negative LR​

Answer 24

(Probability individual with disease has a negative test)/(Probability individual without disease has a negative test)​

Note: The numerator is the complement of the sensitivity (1-sens)​

Note: The denominator is the same definition as specificity​

​

Question 25

Kaplan Meier

Answer 25

Measures time to an event chi2 analysis gives a p-value that determines statistical significance​ Dropouts are signified with an open shape (circle/triangle to the right)​ Hazard ratio describes the difference in hazard rates per unit of time (not instantaneously like in RR) over the study period​ This can become an issue when hazard isn’t constant, like with a surgical intervention which has heavy early risk​ If the curve has a clear crossover point think about there being distinct populations

Question 26

Waterfall Plots

Answer 26

Each bar represents a patient with the y-axis representing response​ Can be very helpful in visualising groups with differing underlying molecular mechanisms

Question 27

Non inferiority

Answer 27

Non-inferiority when it’d be unethical to test against placebo​ or when you want to win on convenience rather than superiority like in DOACs vs warfarin (no INRs, much fewer interactions)​ Non-inferiority should be analysed by per protocol rather than ITT

Question 28

Selection Bias

Answer 28

Method for selecting particiapnts produce sample that is not representative of the pupulation of interest Implicaitions for generalisability

Question 29

Allocation Bias

Answer 29

MAy result if the investigators know or predict whicj intervention the next eligable participant is supposed to receive

Question 30

Channeling bias

Answer 30

When a patient's prognosis or degree of illness influences which group he or she is put into in a study

Question 31

Ascertainment bias

Answer 31

Members of a target population are less likely to be included in the final results than others

Question 32

Information Bias

Answer 32

Systemic difference in the way that information is collected between 2 groups being compared

Question 33

Interviewer Bias

Answer 33

Difference in the way that information is obtained or recoreded in the setting of the interviewer being aware of subject's disease status e.g. retrospective case control when trying to find an exposure

Question 34

Chronological Bias

Answer 34

Differences between those recruited earlier in the process than those recruited later

Question 35

Recall bias

Answer 35

CAn't remember experiences accurately

Question 36

Transfer Bias/Non-response bias

Answer 36

When a sample that is representative is chosen but a subset cannot be contacted or does not respond and differs from responders

Question 37

Attrition Bias

Answer 37

Participants leave a study

Question 38

PErformance bias

Answer 38

Occurs doe to knowledge of itnerventions allocation in either the researcher or participant and can inflate the estimated effect of the intervention

Question 39

Confirmation bias

Answer 39

Whenthe researcher looks for and uses the information to support their own ideas or beliefs

Question 40

Anchoring bias

Answer 40

When researcher depends too heavily on an initial peice of info offered when making decisions

Question 41

pre test odds

Answer 41

pre test odds = p/1-p p=prevalence

Question 42

Post test odds

Answer 42

post test odds = pretest oddsxLR

Question 43

post test probability

Answer 43

post test probability = post test odds/1+post test odds

Question 44

pre test probability

Answer 44

= prevalence