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Bpt > Stats > Flashcards

Stats Flashcards

1
Q

Null hypothesis

A

A strawman set to argue against the data

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

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2
Q

Type 1 error

A

Rejecting the null hypothesis when it is in fact true

A False positive result.

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

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3
Q

P value

A

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

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4
Q

Type 2 Error

A

Accepting the null hypothesis when it is in fact false

A false negative result

You decided you weren’t right when you were

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5
Q

Power

A

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%

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6
Q

Modifiers of Power

A

Bigger is better:

  • Size of effect
  • Sample size

Lower is preferred:

  • Desired significance
  • Standard deviation
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7
Q

Risk

A

The chance of something occuring

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

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8
Q

Odds

A

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

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9
Q

Relative Risk

A

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​

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10
Q

Absolute Risk Reduction

A

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%​

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11
Q

Number Needed to Treat

A

NNT = 1/ARR

E.G ARR = 10%

Taking 10 people and treating them prevents 1 death

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12
Q

Odds Ratio

A

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

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13
Q

Which study uses odds ratio instead of relative risk

A

Case control

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14
Q

Cross Sectional Study

A

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
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15
Q

Case Control Study

A

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
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16
Q

Prospective Cohort study

A

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
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17
Q

Retrospective cohort study

A

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

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18
Q

Nested Case Control

A

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

19
Q

RCT

A

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

20
Q

Per Protocol

A

Only patients who followed protocol are included in the analysis​

Loses randomisation

21
Q

As Treated​

A

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

Also loses randomisation

22
Q

Likelihood Ratio

A

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

23
Q

Positive LR

A

(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)​

​

24
Q

Negative LR​

A

(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​

​

25
Q

Kaplan Meier

A

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

26
Q

Waterfall Plots

A

Each bar represents a patient with the y-axis representing response​

Can be very helpful in visualising groups with differing underlying molecular mechanisms

27
Q

Non inferiority

A

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

28
Q

Selection Bias

A

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

Implicaitions for generalisability

29
Q

Allocation Bias

A

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

30
Q

Channeling bias

A

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

31
Q

Ascertainment bias

A

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

32
Q

Information Bias

A

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

33
Q

Interviewer Bias

A

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

34
Q

Chronological Bias

A

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

35
Q

Recall bias

A

CAn’t remember experiences accurately

36
Q

Transfer Bias/Non-response bias

A

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

37
Q

Attrition Bias

A

Participants leave a study

38
Q

PErformance bias

A

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

39
Q

Confirmation bias

A

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

40
Q

Anchoring bias

A

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

41
Q

pre test odds

A

pre test odds = p/1-p

p=prevalence

42
Q

Post test odds

A

post test odds = pretest oddsxLR

43
Q

post test probability

A

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

44
Q

pre test probability

A

= prevalence

Bpt (15 decks)

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