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RESS > data analysis interpretation and causality confounding > Flashcards

data analysis interpretation and causality confounding Flashcards

1
Q

when should you be concerned about estimates?

A

with CI - if the CIs show a contradictory estimate

clinically - if the doctor could diagnose something else 95% of time

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2
Q

what do 95% CIs mean?

A

that 95% of the time the estimate lies within the confidence intervals

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3
Q

what is the p value?

A

the probability that the p value is at least as big as your assuming the coefficient is actually 0

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4
Q

what does a small p value mean?

A

zero-assumption is probably wrong and an effect is likely

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5
Q

what does a large p value mean?

A

a zero assumption is probably right and an effect is unlikely

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6
Q

what is R2?

A

how good a fit the model is - how well the points align to the line of best fit

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7
Q

what is S?

A

the deviation of the point

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8
Q

how would you carry out a statistical test?

A

observe, guess using model, test R2, S, CI and p value and assess using p value, R2 and CI

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9
Q

what is the use of causal inference?

A

understanding and identifying causal effects help us to understand changing care and improvements

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10
Q

what is association not?

A

causation

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11
Q

why is causation of infectious disease and cellular processes fairly simple?

A

bacteria + person = illness

relaxed myometrial cells + prostaglandin E2 = contracted myometrial cells

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12
Q

how are cellular processes used in clinical practice?

A

relaxed mymometrial cells + prostaglandin E2 = contracted myometrial cells - can be used to induce labour

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13
Q

the deterministic approach to causation is easy. How is it used in life?

A

it is appealing to toddlers and how they work but is actually inaccurate as association does not necessarily mean causation

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14
Q

what are the multiple causes of pre-term birth?

A

obesity, smoking, diabetes, alcohol, SGA, country of residence, bacterial vaginosis, iatrogenic etc

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15
Q

why does presentation of data matter?

A

the way you present and type of graph convinces public in different ways

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16
Q

how do we study how things work?

A

come up with an estimate of the counterfactual

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17
Q

how can we infer if a change has worked?

A

can look at different things with different exposures and work out if one variable has more of an effect however everyone is different and there are different environments

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18
Q

what is exchangeability?

A

when estimating the counterfactual the best way to do this is by finding groups that are comparable through randomisation in a population. This is a biased tool however as you rely on probability to balance out all of the different factors that make people different and therefore need enough numbers to account for the differences and balance them out

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19
Q

what is random sampling error?

A

it is the random error in out population estimates, that result from chance fluctuations in our sample profile - sample population will never be perfectly representative of a whole population

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20
Q

why wont a bigger sample help to achieve exchangeability without randomisation ?

A

the exposure without randomisation is assigned by the underlying bio-psycho-social determinants - larger samples want to reduce error but without randomisation this cannot be achieved

21
Q

what is one of the most important skills in observational research?

A

learning to identify and address confounding

22
Q

what is confounding?

A

it is the distortion of the association between the two variables due to a common shared cause - can generate spurious associations or mask, exaggerate or suppress and association

23
Q

what are common confounders?

A

age and sex

24
Q

what is conditioning?

A

when we reduce confounding by examining like for like and look specifically at one group - more comparable groups that are exchangeable is conditioning

25
Q

what is restriction?

A

when you restrict the sample to a single value of the confounder

26
Q

what is stratification?

A

when you calculate category specific effects for different levels of the confounder

27
Q

what is covariate adjustment?

A

when you adjust for a covariate in regression

28
Q

why can conditioning not completely remove confounding?

A

other confounding variables not considered - unobserved confounding and error in measure - imperfect conditioning and therefore residual confounding

29
Q

larger samples reduce random error but do not reduce systematic error, what is this?

A

bias

30
Q

what is imprecise and inaccurate?

A

imprecise - lower sample size

low quality - inaccurate

31
Q

what is the difference between measurement error and measurement bias?

A

measurement error is error in measurement due to random factors whereas bias is due to non random

32
Q

when do misclassification errors and bias occur?

A

when measurement errors and bias are present

33
Q

what are the types of bias?

A

selection, confounding, information, experimenter, analytic, inferential - not mutually exclusive

34
Q

what is selection bias?

A

occurs due to a systematic difference between those selected and those not for a study - differences between the observed and the unobserved group

35
Q

what are the types of selection bias?

A

attrition - loss of participants
participant - who takes par - people having differential preferences or opportunities to take part - willingness varies with all possible bio-psycho-social factors

36
Q

what is information bias?

A

when there is systematic error in reporting, measuring or recording information

37
Q

what types of information bias are there?

A

response bias - when people answer inaccurately (also acquiescence in this category - inherently more likely to say yes)
recall - people remembering things differently
observer effect - people responding differently when they know they’re being observed

38
Q

what is experimenter bias?

A

it can be unconscious or conscious and is due to the behaviours or actions of the experiments

39
Q

what are the types of experimenter bias?

A

confirmation - more likely to accept results if fit with what we expect and refute those that dont
systemic - more likely to chase positive associations - p hacking - seek novel results, or publish those that help us

40
Q

what must be checked in a study?

A

funding for conflicts of interests to see experimenter bias

41
Q

what are the two types of error in population research?

A

random and systematic

42
Q

what does the sample size have no effect on ?

A

the degree of systematic error and therefore the accuracy

43
Q

what is confounding bias?

A

it is the distortion of the causal association between two variables due to a common shared cause which is known as the confounder

44
Q

what is a measurement error?

A

it is an error in measurement due to random factors such as weighing scales varying in climate

45
Q

what is measurement bias?

A

it is an error in measurement due to non random factors such as weighing scales broken

46
Q

what is misclassification error or bias?

A

it is when measurement error or bias result in misclassification

47
Q

what is bias?

A

there are different types and they are not mutually exclusive - a study can be biased in many different ways

48
Q

what types of bias are there?

A

inferential, informational, selection, confounder, experimenter and analytic

49
Q

what are common confounders and when will these occur?

A

age and sex when there are non standardised rates

RESS (7 decks)

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