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Phase 1 Critical Numbers > Critical Numbers > Flashcards

Critical Numbers Flashcards

1
Q

Name the 5 types of study design

A

Case control
Cross-sectional
Cohort
RCT
Ecological

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2
Q

What are case control studies?

A

Retrospective studies that find people with an outcome + look back and see if they have the risk factor in question

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3
Q

Name 2 positives of case control studies

A

Fast + cheap
Good for rare outcomes

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4
Q

Name 2 negatives of case control studies

A

Can’t prove causation/can’t eliminate confounders
Difficult to establish order of events

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5
Q

What do cross-sectional studies look at?

A

An outcome at one point in time

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6
Q

Name 2 positives of cross sectional studies

A

Fast + cheap
Generates hypotheses

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7
Q

Name 2 negatives of cross-sectional studies

A

Not causative
Less suitable for rare diseases

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8
Q

What do cohort studies do?

A

Prospective follow-up of an outcome over time, have separate groups, some with , some without exposure

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9
Q

Name 2 positives of cohort studies

A

Little ethics
Can clearly sequence events

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10
Q

Name 2 negatives of cohort studies

A

Can’t prove causation
Time-consuming + expensive

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11
Q

What are RCT’s?

A

Gold standard study, has multiple groups which have different exposures + compare outcomes. Can use crossing over, placebos, double-blind matching etc

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12
Q

Name 2 positives of RCTs

A

Can prove causation
Random=less bias

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13
Q

Name 2 negatives of RCTs

A

Time consuming + expensive
Often unethical

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14
Q

What are ecological studies

A

Large population-look at data, prevalence, correlation + trends

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15
Q

Name 2 positives of ecological studies

A

Fast + cheap

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16
Q

Name a negative of ecological studies

A

Ecological fallacy-correlation does not always equal causation

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17
Q

Name the 5 types of sampling

A

Random
Systematic
Quota
Cluster
Stratified sampling

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18
Q

Give an example of random sampling

A

Using a random number generator

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19
Q

Give an example of systematic sampling

A

Numbering people 1,2,3,4 and assigning them to one of four groups

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20
Q

Give an example of quota sampling

A

Filling up a group until you have all the people you need

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21
Q

Give an example of cluster sampling

A

Divide people into groups (usually geographical), each group=1 cluster, clusters randomly selected

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22
Q

Give an example of stratified sampling

A

Divide into male/female, then take random sample of each

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23
Q

What is sample bias?

A

Non-representative sample

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24
Q

What is recall bias?

A

Lack of specifics/can’t remember details

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25
Q

What is social desirability?

A

Lying about things for societal reasons

26
Q

What is information bias?

A

Intentionally wrong data measure

27
Q

What is volunteer bias?

A

Volunteers unlikely to be representative

28
Q

What is selection bias?

A

Similar to volunteer bias-choosing of volunteers isn’t random

29
Q

What is lead time bias?

A

Screening seems to improve life expectancy but actually just picked up earlier

30
Q

What is length-time bias?

A

Those with a less aggressive condition/longer living are more likely to be picked up

31
Q

What are confounders?

A

Factors related to both the outcome + exposure that may affect results + need to be accounted for

32
Q

What does PICO stand for?

A

P-patient/population
I-intervention
C-control/comparison
O-outcome

33
Q

Name 3 types of variable

A

Binary
Categorical
Numerical

34
Q

What are binary variables?

A

Only two answers e.g. yes/no, true/false

35
Q

What can categorical variables be divided into?

A

Nominal
Ordinal

36
Q

What are nominal variables?

A

They have no distinct hierarchy e.g. hair colours

37
Q

What are ordinal variables?

A

They have a hierarchy e.g. social classes

38
Q

What can numerical variables be divided into?

A

Continuous + discrete

39
Q

What are continuous variables?

A

Numbers that can take any value within a range e.g. height, age

40
Q

What are discrete values?

A

Whole numbers e.g. number of people

41
Q

What is standard deviation?

A

Measures spread of data around the mean for this population

42
Q

What is the standard error?

A

Measures spread of several means from various populations + estimates how far away from sample mean the true mean is

43
Q

What is the difference between the SD + SE?

A

SD=spread around mean, SE=estimates real mean

44
Q

How do you calculate standard error?

A

Sample SD/ square root of number of samples

45
Q

What is kurtosis?

A

Vertical data skew

46
Q

What is the interquartile range + how do you calculate it?

A

Measure of spread, UQ-LQ

47
Q

What is the confidence interval?

A

Range of values the population mean is likely to be within e.g. 95% CI

48
Q

How do you calculate the risk difference?

A

Risk-risk

49
Q

How do you calculate risk?

A

Number of examined outcomes/total possible

50
Q

How do you calculate risk ratio(/relative risk)?

A

Risk/risk

51
Q

What should the relative risk be lower than for it to be a low risk?

A

Less than 1

52
Q

How do you calculate odds ratio?

A

Ratio of (probability of event): (probability of non event)

53
Q

How would you write a null hypothesis?

A

There will be no difference…

54
Q

How would you write an alternative hypothesis?

A

There will be a significant difference…

55
Q

What does the P value show?

A

Probability result is due to chance

56
Q

What is the equation for simple linear regression?

A

y=mx+c

57
Q

What does multiple linear regression do?

A

Considers several independent variables/confounders

58
Q

When is logistic regression used?

A

When an outcome variable is a binary variable

59
Q

When do you use multiple regression as opposed to correlation?

A

Correlation=no distinction between variables, no causation, simply association
Multiple regression=y causes x, prediction

60
Q

What do you use scatter plots to show?

A

Correlation

61
Q

What do regression models do?

A

Try to predict data using an equation

62
Q

What do the letters stand for in y=mx + c?

A

y=dependent variable
m=gradient
x=independent variable
c=y-intercept

Phase 1 Critical Numbers (1 decks)

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