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Pop health > Lecture 23 Chance 1 > Flashcards

Lecture 23 Chance 1 Flashcards

1
Q

Critical thinking

A

What study is telling you

Start to interrogate
Start to critically evaluate the study and how it was done and what influenced how the study was done will have on the results

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2
Q

External validity

A

Extent to which you can generalise the results of your study

Judgement call, argue the case

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3
Q

To get external validity

A

Need internal validity

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4
Q

Internal validity meaning

A

Do study findings represent the truth
Are they of accurate reflection of the truth or are they misleading in some way
To what extent are they false

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5
Q

Internal validity

A

Chance
Bias
Confounding

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6
Q

What are we talking about when talking about chance?

A

Sampling

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7
Q

Sampling

A

Subset of population (good representation)

whole pop not feasible

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8
Q

what do you use sample for?

A

estimate of the measure occurrence / association of the population
Estimate population parameter

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9
Q

Parameter

A

True value in population

Trying to get to know

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10
Q

Estimate

A

What we get out of our study sample

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11
Q

Study that samples gets an

A

Estimate of population parameter

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12
Q

Problem of sample

A

Study samples vary in estimates they provide of the population parameter

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13
Q

If keep randomly selecting people for that study and measuring the same thing

A

Will get a variety of estimates
Most will be around the parameter
There will be weird outliers

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14
Q

Sample error

A

Sample varies in the estimates they give us

Some close to the population parameter some will not

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15
Q

Sample error is a form of and occurs by

A

Form of random error

Occurs by chance

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16
Q

What can be done with this sampling error?

A

Increase number of people in study (sample size)

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17
Q

Increase sample size

A

Reduces sample variability (standard deviation etc.)

Increases likelihood of getting a representative sample

Increases precision of parameter estimate
- Increases likelihood that its close to parameter as opposed to further away

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18
Q

More of samples

A

accurate representation of population parameter

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19
Q

What we mean by chance and sampling error is that our

A

sample is weird (distorted estimate from population parameter) and estimate isn’t an accurate reflection of the parameter

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20
Q

Sampling

A

Chance occurs (distorted estimate from population parameter)

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21
Q

Paths might take to deal with a problem of chance and assessing impact on studies

A

Confidence Intervals

P-values (hypothesis testing)

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22
Q

Confidence Intervals 95%

A

Contains the true population parameter (value)
5% wouldn’t

give insight into precision

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23
Q

Width of confidence interval shows

A

How precisely estimating the population parameter

24
Q

Narrow CI

A

More precise

25
Q

Wide CI

A

Less precise

26
Q

Increase sample size

A

More likely get an estimate closer to the parameter
Narrow CI
- As study estimate will most likely be closer to the population parameter
- Decrease width of CI

27
Q

Use of 95% CI

A

In assessing clinical importance

  • Test for effectiveness of intervention whether it actually helps people
  • Often state what is a clinically important difference or clinically meaningful effect
28
Q

10% reduction risk

A

= RR 0.9

- Clinically important threshold

29
Q

CI help us interpret

A

Study findings from RCT in relation to clinical importance

30
Q

Any study RCT that produces RR lower than 0.9 closer to 0

A

Meet threshold

Produce clinically important effect

31
Q

Any study RCT that produces RR above 0.9

A

Does Not meet clinical importance threshold

32
Q

Tight CI below threshold

A

Study is clinically important
Estimate below threshold
CI below threshold
Population parameter below threshold

33
Q

Wider confidence interval

A 
Lowest end (more effective if PP there), or less effective of what threshold is and be a slight risk factor
Possibly clinically important 
Does Not contribute a great deal to knowledge
34
Q

Tight CI above clinically important threshold (CI spans below threshold across to just below null)

A

Consistent with reduction of risk below null
Crosses clinically important threshold
Possibly clinically important

35
Q

Tight CI above clinically important threshold

A

Precisely estimates
Not clinically important
CI doesn’t meet threshold

36
Q

Tight CI above important threshold

A

CI inconsistent with population parameter

37
Q

When sampling

A

Generate estimate of population parameter

Parameter may truly never know

38
Q

Estimating population parameter for sampling

A

Introduces random error and sampling error and chance

39
Q

Just by chance

A

Might get samples that produce an estimate really weird
Isn’t a great reflection of population parameter
Can’t eliminate only reduce by increasing sample size

40
Q

A way to assess how precisely and reduce the influence of chance

A

Use 95% confidence intervals

Tells value between that we are 95% confident that the true value population parameter lies

41
Q

Precision

A

How precisely estimating population parameter

How close likely to be to it with estimate

42
Q

If wide CI

A

Study has a wide range of interpretations

Find point estimate a measure associations something is a risk factor but CI being consistent with population parameter of the exposure being a protective factor

43
Q

Precision is important for

A

What we take away from our measures of association

44
Q

Larger sample size

A

Better for CI and precision

45
Q

Narrow CI

A

Great

Need larger sample size

46
Q

chance consists of

A

validity (ext and int)
CI
sampling error

47
Q

External validity

A

Generalisability - The extent to which the findings of the study be applied.

Judgement call depending on what is being studied and who it is being applied to

48
Q

Internal validity

A

The extent to which the findings of the study are free of chance, bias and confounding

Are there other explanations for the study findings, apart from them being right?

49
Q

pop parameter

A

True value of measure

we’re interested in

50
Q

sample estimate

A

Study’s estimate of the parameter

51
Q

Parameter

A

The true value of the measure in the population that the study is trying to discover

52
Q

Estimate

A

The measure found in the study sample. Sometimes referred to as the point estimate

53
Q

If you repeatedly sampled randomly from the same population,

A

get a sample with a similar composition to the population you sampled from

some samples would be different by chance
sampling error, and is a form of random error known as chance

54
Q

Narrow CI

A

more precise

RR = 2.0 (95% CI: 1.8 – 2.2)

55
Q

Wide CI

A

less precise

RR = 2.0 (95% CI: 0.8 – 3.2)

56
Q

Increasing the sample size makes the

A

CI narrower

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