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OST interviews > Critical Appraisal > Flashcards

Critical Appraisal Flashcards

1
Q

Confounder

A

confounders make it appear as if there is a direct relationship between an exposure and an outcome. They may also mask a true relationship. A confounder must have an association with exposure but not be a consequence of it. It must also be associated with the outcome but independently of the exposure.

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2
Q

Selection bias

A

where the individuals, group or data selected for a study is not appropriately randomised meaning the sample is not representative of the desired population. This will normally be identifyed by differences in the baseline characteristics between exposure and control groups.

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3
Q

Bias

A

Anything that influences the study in a non random way deviating the results from the truth.

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4
Q

observation bias

A

When there is differences between how the data is gathered in a study and how the outcome is measured. Such that the results may be unduly influenced by the expectations of the researchers or subjects.

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5
Q

cross sectional studies

A

studies a group at a specific point in time

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6
Q

ecological studies

A

study a community or population

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7
Q

pragmatic studies

A

take place in a real life setting e.g. hospital or clinic, gives good effectiveness data and external validity

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8
Q

case reports

A

observational and descriptive, prone to chance association and bias. useful for generating hypothesis and as for setting for clinical reminders.

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9
Q

cohort studies

A

recruiting subjects based on a risk factor being present or absent and then study going forward. May be expensive to set up and long time from exposure to outcome. Also bias may be present if subject drop otu. They are prospective, good if risk factor is rare

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10
Q

case control studies

A

recruiting subjects who either have or haven’t had an outcome and then look back on data (retrospective). The disadvantages of these are that they rely on memory, old records. and can be difficult to recruit a matching control group, good if outcome is rare

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11
Q

temporality of exposure

A

did exposure proceed outcome

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12
Q

gradient of exposure

A

increased exposure leads to increased risk

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13
Q

analogy of exposure

A

is the association analogous to any previous proved causal association

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14
Q

factorial studies

A

use more than one intervention at a tme to see how they interact

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15
Q

meta analysis

A

combines results of more than one study to create a quantitative assessment

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16
Q

phase 0

A

microdosing for pharmacokinetics/bioavailabilty etc

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17
Q

phase 1

A

in healthy individuals

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18
Q

phase 2

A

in relevant illness subjects

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19
Q

phase 3

A

large group in clinical setting

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20
Q

phase 4

A

post marketing surveillance

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21
Q

exclusion criteria

A

a common exclusion criteria is to exclude those too unwell, co-morbid or with confounding factors. Too much exclusion criteria and may reduce effectiveness of data/ cause selection bias

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22
Q

simple random sampling/representative sampling/proportionate sampling

A

everyone in TP has equal chance of being selected

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23
Q

systematic sampling

A

every nth person selected after first randomly chosen

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24
Q

stratified sampling

A

subjects are divided into suubgroups and equal number are drawn from each

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25
Q

cluster sampling

A

subjects are divided into clusters and some are sampled some are not. disadvantages are that differences between clusters and that different sized clusters may be given same weighting

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26
Q

SB: berkson admission rate bias

A

sample taken from hospital setting

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27
Q

SB: diagnostic purity bias

A

excludes those with comorbidities

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28
Q

SB: survival bias/neyman/incidence-prevalence

A

end up analysing those who have survived or lasted long enough, therefore milder forms of disease are represented

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29
Q

SB: membership bias

A

those from a specific group e.g. cancer charity

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30
Q

SB: historical control bias

A

where groups are chosen at different times this may mean they encountered different treatments/RFs/disease

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31
Q

SB: response bias

A

those more likely to respond are more likely to be motivated etc

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32
Q

randomisation

A

allocation of different subjects to intervention groups

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33
Q

adaptive randomisation

A

adjustment of study arms to maintain similarity

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34
Q

open label trial

A

no blinding, therefore observation bias is an issue

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35
Q

triple blinding

A

subject, researcher and analyst dont know

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36
Q

surrogate endpoints

A

a biomarker used as a substitute for a clinical endpoint e.g. LDL level. Useful as can be shorter as dont need to wait for outcome but not necessarily reflective of clinical outcome

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37
Q

composite endpoints

A

composite of several clinical endpoints e.g. death, MI, stroke

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38
Q

validity

A

the extent to which a test measures what it is supposed to measure

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39
Q

reliability

A

how consistent a test may be on repeated measurements

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40
Q

criterion validity

A

compared to an existing test

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41
Q

relevance of a large drop out

A

if in the experimental arm could suggest intolerable treatment or lack of efficacy of treatment, if in placebo arm may indicate individuals in group may have required treatment

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42
Q

difference between systematic error and random error

A

random error is variable, systematic error is consistent e.g. scales that measure 1gram too low

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43
Q

test-retest reliability

A

level of agreement of two assessments of the same data/sample

44
Q

accuracy

A

how close is the measurement to its true value

45
Q

precision

A

how close repeat measurements are

46
Q

incidence

A

number of new cases over a period of time/population size

47
Q

mortality rate

A

incidence of death, morbidity rate is incidence of new non fatal cases

48
Q

prevalence

A

number of people with a given disease at a specific time/population size. i.e. the proportion of a defined population with the disease at a given time

49
Q

lifetime prevalence

A

proportion of a population who has had or has a given disease at a given point in time

50
Q

discrete vs continuous data

A

discrete includes whole numbers continuous is infinite

51
Q

qualitative data

A

non numerical, can be converted to quantitative data by using cut off points e.g. HTN 130/90

52
Q

nominal scales

A

no relation to eachother

53
Q

ordinal scales

A

inherent order

54
Q

interval scales

A

start at a set point, 2 degress is not twice as hot as 1 degrees

55
Q

probability distribution

A

links all the possible values of a random variable with the likelihood of that occuring, e.g. flipping a coin once, heads has a probablity distribution of 0.5

56
Q

a binomial experiment

A

a fixed number of runs in which a random variable can have two possible outcomes

57
Q

binomial distribution

A

the probability distribution of a binomial random variable, yes or no

58
Q

poisson experiment

A

two possible outcomes of a random variable but number of runs is not fixed

59
Q

poisson distribution

A

can be used to determine the probablility of an outcome from a poisson experiment i.e. if you know on average 5 babies are born a day on a ward you can calculate the probablility that exactly 6 will be born tomorrow

60
Q

bimodal distribution

A

two modes therefore two peaks

61
Q

normal distribution/gaussian

A

mean=mode=median, bell shaped

62
Q

weighted mean

A

increased significance or weight is attached to some values

63
Q

variance

A

is the distance of all the different values from the mean, average distance from the mean

64
Q

standard deviation

A

a statistical measure that describes the degree of data spread about the mean, it is the square root of variance. 1SD 68%, 2SD 95%, 3SD 99.7%. can be used to calculate the proportion of variables that lie between any two values

65
Q

z score

A

the distance of a value from the mean using standard deviation as a unit i.e. +1.2 is +1.2SD from the mean

66
Q

coefficient of variance

A

The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean.

67
Q

effect size/standardised mean difference

A

compares the mean difference between experimental and control groups and devides it by the standard deviation. if bigger then bigger effect

68
Q

positively skewed

A

mean>median

69
Q

negatively skewed

A

mean

70
Q

kurtosis

A

how peaked is the distribution

71
Q

central limit theorum

A

the idea that if you take lots of samples, the mean of all the sample means will be the same of the population mean. The standard error of the mean is the standard deviation of the sample mean

72
Q

95% confidence interval

A

if normally distributed, 95% of the means in a sample will like within 1.96 standard errors above or below the population mean. The confidence interval may be smaller with a larger sample size and bigger with a smaller population size. It is the range around the mean that we think the true population mean falls into 95% of the time. can be worked out with the sample mean and the standar error. the wider/larger the confidence interval the less certain the result

73
Q

per protocol analysis

A

only those who complied with the trial protocol and completed are analysed

74
Q

intention to treat analysis

A

all sibjects who were randomised are included in the analysis regardless of if they dropped out, this is more representative of real life; includes worst case scenario, hot desk (similar subject results transposed), last observation carried forward

75
Q

last observation carried forward

A

can either underestimate or over estimate effect

76
Q

absolute risk

A

the percentage chance of an outcome, can be either CER (control event rate) or EER (experimental event rate)

77
Q

absolute risk reduction

A

the change in risk between the control group and the experimental group = CER - EER

78
Q

relative risk

A

the ratio of risk in the control compared to the experimental group = EER/CER, if = 1 there is no increased risk, if 2 then twice as likely, if 0.5 twice as unlikely

79
Q

relative risk reduction

A

is the drop in risk from the experimental group from the control group = CER-EER/CER

80
Q

numbers needed to treat

A

number of subjects who need to have the intervention to have benefit compared with the control. reciprocal of the absolute risk reduction = 1/CER-EER

81
Q

odds ratio

A

used to compare how likely outcomes are between groups, the odds ratio is the ratio of the odds of having the outcome in the experimental group relative to the control group, an odds ratio of 1 reflects the sasme outcome in both groups, if >1 than odds are greater in the experimental group

82
Q

null hypothesis

A

any difference is due to chance

83
Q

p value

A

is the probability of getting the expressed value by chance, the smaller the p value the less likely it is to be due to chance and therefore the more likely it is to be statistically significant

84
Q

type 1 error

A

false positive, usually attributable to bias or confounding or data dredging

85
Q

type 2 error

A

false negative, usually due to small sample sizes, can use power calculations to figure size of sample required to avoid type 2 error

86
Q

regression

A

expresses the relationship between two or more variables, regression lines appear on correlation tables

87
Q

confounding management: restriction

A

using inclusion and exclusion criteria to restrict confounding factors from entering the sample population

88
Q

confounding management: matching

A

people with confounding factors are allocated evenly into different study arms

89
Q

confounding management: randomisation

A

confounding factors known or unknown are randomised into different study groups

90
Q

sensitivity

A

the true positive rate, the proportion of patients with the disorder who have a positive test, if sensitive a negative test will rule a disorder out (SNOUT)

91
Q

specificity

A

the true negative rate, the proportion of patients without the disorder who have a negative test, if specific a positive result will rule a disorder in (SPIN)

92
Q

positive predictive value

A

proportion of patients with a positive result who actually have the outcome,
sensitivity is if a patient has a disorder what are the chances they have a positive test, whereas, PPV is if the patient has a positive test what are the chances they have the disroder

93
Q

negative predictive value

A

proportion of patients with a negative result who do not have that outcome

94
Q

likelihood ratios

A

calculated from specificity and sensitivity, shows how many more times likely a patient with a disorder is to have a particular test than a patient `without the disorder.

95
Q

hazard rate

A

probability of an endpoint event at a time interval divided by the duration of the time interval . hazard ratio compares experimental arm to control arm

96
Q

Quality Adjusted Life Years

A

number of extra years of life obtained x value of the QoL during those years. death =0 QALYs, 1 year of perfect health = 1 QALY

97
Q

minimising bias in qualitative studies

A

reflexivity/bracketing, member checks, triangulation of data by cross referencing

98
Q

meta analysis

A

aids detection of statistical significance by pooling together studies often with smaller samples, results shown in a forest plot

99
Q

PRISMA

A

27 item checklist for systematic reviews

100
Q

publication bias

A

large studies whether with significant or insignificant findings tend to be published, small studies with significant findings tend to be published, we tend to miss small studies with negative findings in MAs/SRs leading to a bias towards +ve results. Can be detected with funnel plots

101
Q

Methodology CA

A 
Is there a focused clinical question?
How were the groups chosen and allocated?
Was there blinding?
Are the groups similar?
What are the baseline demographics?
Are there any confounders?
Are the endpoints relevant?
Does exposure proceed outcome?
Is length of follow up sufficient?
Is there consistent measurement between groups?
102
Q

Results CA

A 
appropriate use of;
What are the NNT/NNH?
Sensitivity/specificity?
PPV/NPV?
Confidence intervals?
Are the results reliable and valid?
103
Q

Discussion/Applicability CA

A 
Are the patients similar to TP?
Are RFs similar to TP?
ShouldRF be stopped/minimised?
Did this answer the clinical question?
Is the null hypothesis proved or disproved?
Is it possible to apply this to your clinical practice?
Is there any evidence of bias?
Are there any conflicts of interest?
104
Q

cost effectiveness analysis

A

analyses the cost compared to the effectiveness of a treatment to figure out if it provides good value for money
the cost of a treatment divided by its effectiveness e.g. the cost of a treatment/QALYs.

105
Q

incremental cost effectiveness ratio

A

difference in costs between two treatments divided by the difference in effectiveness

106
Q

a good screening test

A

Wilson Junger criteria:

  • an important health problem
  • natural history well understood
  • detectable early stage where treatment is more beneficial than at a late stage, test should aim for this stage
  • test should be acceptable i.e. SEs/risks/invasiveness
  • benefits outweigh the risks
  • cost effective
107
Q

power of a stady

A

the probability of finding a difference between the two study arms when a difference does exist, 1-type 2 error, probability of correctly rejecting a false null hypothesis

OST interviews (4 decks)

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