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Medicine > Critical appraisal > Flashcards

Critical appraisal Flashcards

1
Q

Randomisation

A

Purpose is to try and ensure that any characteristics of the sample population that may affect the results ( confounders) are distributed equally between the two study groups, and avoiding selection bias

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2
Q

Tools for randomisation:

A

centralized computer randomization (with contact by phone or computer) is ideal and often used in multicentre trials.
smaller trials may use an independent person (eg the hospital pharmacy to manage the randomization

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3
Q

Stratified randomisation

A

With powerful confounders ( eg age, sex) , patients can first be split, or stratified , into different groups before randomisation, so there will be the same number of patients with and without the confounder ( young and old, male and female) in each arm of the trial

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4
Q

allocation concealment

A

whether the person recruiting the patient to the trial could know or anticipate the group allocation that patient would receive, preventing selection bias
Prevents clinicians predicting which group patients would be in before recruiting them to the trial

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5
Q

Blinding

A

When some of the partiticapits of a trial ( patient/clinicians/researchers) are prevented from knowing certain information that may lead to conscious or subconscious bias

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6
Q

Blinding advantages

A
  • Prevents observer bias = form of reactivity in which researcher’s cognitive bias causes them to subconsciously influence the participants of experiment = could influence extra quality of care to these patients
  • Or confirmation bias = see results that aren’t there
  • Expectation bias (Pygmalion effect) = Observers may subconsciously measure or report data in a way that favours the expected study outcome
    Also prevents placebo effect – or reduces it
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7
Q

double blinded trial

A

both patients and investigators are unaware of treatment allocation

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8
Q

when blinding is best to use

A

outcome is subjective (eg measurement of symptoms or function)
or if outcome measurement is based on patient self report

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9
Q

instances when blinding is not needed

A

if outcome is objective eg death
sometimes impossible to achieve eg in a trial involving physiotherapy , they will know whether or not they have received it
in a trial involving warfarin ,clinical cannot be blinded due to safety reasons

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10
Q

Confounding

A

distortion (or potential for distortion) of association between outcome and exposure
by third factor
which has an association with both exposure and outcome

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11
Q

common causes of confounding.

A

Confounding occurs when there is a non random distribution of risk factors in the populations. Age, sex and social class are common causes

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12
Q

How to control confounding factors

A

In the design stage of an experiment, confounding can be controlled by randomisation which aims to produce an even amount of potential risk factors in two populations.
In the analysis stage of an experiment, confounding can be controlled for by stratification.

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13
Q

Study least subject to bias

A

RCT - the groups are likely to be similar with respect to known and unknown determinants of outcome therefore we can be more confident that any observed differences in outcome are due to the intervention.

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14
Q

‘intention to treat’ analysis

A

Statistical analysis of data from subjects according to the group to which they were assigned despite noncompliance with the study protocol

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15
Q

‘per protocol’ analysis

A

An analysis of patient outcomes based only on those subjects who completed all aspects of the protocol. Also called on-treatment analysis.

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16
Q

Treatment fidelity

A

how accurately the intervention is reproduced from a protocol or model

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17
Q

Validity

A

describes how accurately a study, instrument, test or equivalent measures what it is supposed to.

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18
Q

Factors affecting validity

A

study size
inter-participant variability the use of different measuring instruments (instrumentality)
Certain biases such as attrition and selection bias

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19
Q

internal validity

A

how well the study was conducted, the degree to which the effects observed in an experiment are due to the independent variable and not confounds-true, accuracy

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20
Q

Threats to internal validity

A 
Reliability of measurement instruments
Regression towards the mean
Sampling
Experimental mortality
Instrument obtrusiveness
Maturation
Measurement instrument learning
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21
Q

external validity

A

extent to which we can generalise findings to real-world settings-useful, generalisability

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22
Q

Threats to external validity:

A

Representativeness of the sample
Reactive effects of setting (is the research setting artificial)
Effect of testing (if a pre-test was used in the study that will not be used in the real world this may affect outcomes)
Multiple treatment inference (this refers to study’s in which subject receive more than one treatment, the effects of multiple treatments may interact)

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23
Q

Reliability

A

is the extent to which an experiment, test, or any measuring procedure yields the same result on repeated trials.The higher the reliability the more likely you are to obtain similar results if the study was repeated.Reliability does not ensure accuracy.

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24
Q

Cohort study

A

sample that has been exposed to a certain exposure and follow that sample to observe the outcome. Cohort studies can be retrospective or prospective. used for prognosis and studying rare exposures. But if uncommon event cohort study would have to be unfeasibly large to answer the study question

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25
Q

Cohort study advantages

A
  • Best information about causation of a disease, can work out incidence
  • Able to examine a range of outcomes from a particular exposure
  • good for rare exposures
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26
Q

Cohort studies negatives

A
  • Often large, difficult to follow up large groups of patients, especially with something such as monitoring diet, expensive and time-consuming
  • Hard to conduct if length of time from exposure to outcome is very long (eg for some cancers) or if exposure you’re observing is rare
  • Need to look out for confounders
  • Bad for long latent periods
  • bad for rare outcomes
  • Misclassified exposure
  • Different follow up for exposed/ non exposed
  • Outcomes assessors not blinded to exposure category
  • Selection bias eg sample patients all live near a nuclear power plant
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27
Q

Loss of follow up can result in :

A
  • measurement error
    reduce available sample size and effect study’s ability to detect a true association between exposure and outcome->increases chance of type 2 statistical error
    if it is systematic, it will introduce bias
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28
Q

Limiting confounding in a cohort study

A

Restriction - limit participants of study that have possible confounders
Matching and stratification - make comparison groups,adjust for confounding
Multiple variable regression - coefficients are established for the confounder groups. Allows for better adjustment

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29
Q

Bradford hill’s criteria

A
  • Strength of association
  • Specificity - Does A always only cause B?
  • Temporal association - effect has to come after cause
  • Theoretical plausibility
  • Consistency - Do you always find the same relationship?
  • Coherence - Does the data fit in with what we know now?
  • Dose-response relationship - Does greater exposure lead to greater effect?
  • Experimental evidence - Can we test this experimentally?
  • Analogy - If A causes B, does something similar to A cause something similar to B?
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30
Q

Case control study

A

sample that already has a certain outcome, follow them back to find out if they were exposed to a certain exposure.Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups.used for Studying cause of rare diseases

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31
Q

Challenges to measuring exposures in case controls:

A

Recall bias
Variable exposure- patients environment may have changed eg moved house
Unavailable data - eg patient can’t remember, medical records unavailable, incomplete or inaccurate

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32
Q

recall bias

A

participant cannot remember when they were exposed, or their outcome changes their perception of the exposure

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33
Q

Case-control studies negatives

A
  • Often affected by recall bias participant cannot remember when they were exposed, or their outcome changes their perception of the exposure
  • Or affected by selection bias where control group has other factors that may influence their exposure
  • Needs a large sample size for rare exposures
  • Cases don’t represent the full disease spectrum
  • Confounders need to be recognised/addressed
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34
Q

Case control advantages

A
  • simple/easy to conduct, do not require long follow up, outcome already present
  • Good for rare outcomes, can select all patients with a certain disease
  • Good for long latent periods, not waiting for it long after the exposure
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35
Q

Observational study bias

A

In an observational study patient or clinician preference rather than randomisation determines whether a patient is allocated to the intervention or comparison group. In the absence of randomisation there is a greater risk of imbalance in both the known and unknown determinants of outcome, and consequently any observed differences in outcome might be unrelated to the intervention but due to differences between groups at baseline

36
Q

systemtic reviews

A

A rigorous summary of all the research evidence that relates to a specific question

37
Q

Finding all the evidence in a systematic review:

A

Searching bibliographic databases eg medline
Searching for non english papers( avoids language bias)
Searching through references of other trials
Trial registries
Contacting authors
Conducting a thorough search for evidence in a systematic review can eliminate publication bias, language bias and selection bias

38
Q

Systematic review- how to appraise validity

A

Is this a systematic review of RCTs? - anything less than RCT is inadequate
What was the search strategy? Studies with negative results or foreign languages are unlikely to be included
How was validity of individual studies assessed?
Are the results consistent from study to study?

39
Q

disadvantages of single studies:

A

Individual studies may have inadequate sample sizes to detect important differences (leading to false negative results).The results of apparently similar studies may vary because of chance. Subtle differences in the design of studies and the participants may lead to different or even discrepant findings).

40
Q

A systematic review has some advantages over a primary data study:

A

first, speed (why conduct a new RCT if the answer is already available in published research?); second, ethics (is it defensible to conduct a controlled trial if the answer is available in trials already conducted?); third, statistical power (all else being equal, combining data from more than one study increases certainty in findings and precision of estimates).
By bringing together all the relevant evidence, disadvantages of single studies can be guarded against

41
Q

Sensitivity

A

Proportion of patients with the disease who get a positive
= No. True positives / all those with disease

Tests with high sensitivity correctly classify a high proportion of people who really have disease

42
Q

Specificity

A

Proportion of patients without the disease who get a negative test
= No. Of true negatives / all those without disease
Tests with high specificity correctly classify a high proportion of people who don’t have disease

43
Q

Positive predictive value

A

Chance of having disease if your test is positive
No. Of true positives/ all those that test positive
As prevalence Rises this also rises

44
Q

Negative predictive value

A

Chance of not having disease if your test is negative
No. Of true negatives / all those test negative
As prevalence increases this value falls

45
Q

Likelihood ratio for positive test result

A

•Sensitivity ÷ (1 – Specificity)

46
Q

Likelihood ratio for negative test result

A

(1 – Sensitivity) ÷ Specificity

47
Q

Risk

A

a ratio of the number of people who develop an outcome to the total number of people

48
Q

Risk ratio

A

probability of disease/ risk in exposed/ probability of disease/risk in unexposed

49
Q

RR<1

A

means that the treatment decreases risk of the outcome, the rate of an event is decreased compared to controls. The relative risk reduction should therefore be calculated

50
Q

RR>1

A

means that the treatment increased the risk of the outcome. the rate of an event (eg experiencing significant pain relief) is increased compared to controls. It is therefore appropriate to calculate the relative risk increase if necessary

51
Q

RR 1

A

means no difference between the groups (treatment has no effect)

52
Q

Number needed to treat (NNT)

A

number needed to be treated to produce one improved outcome (1 ÷ ARR)
Round up to a whole number

53
Q

Number needed to harm

A

number needed to be treated to produce one harmful outcome
Round value down to a whole number
can be calculated by dividing 1 by the absolute risk increase

54
Q

95% confidence interval

A

the range of values of the study sample within which we can be 95% sure the true population value lies

55
Q

statistically significant test result

A

(P ≤ 0.05)

means that the test hypothesis is false or should be rejected.

56
Q

Type 1 error

A 
(False positive)
Rejecting null hypothesis when it is true
shows a difference when there is none
Poor study
Data manipulation
57
Q

Type 2 error

A

(False negative)
Accepting null hypothesis when you should have rejected it/the null hypothesis is false
A type two error occurs when a study fails to detect an effect or an association that does exist. i.e. detects no difference when there is one
Might be related to sample size

58
Q

Power

A

probability of correctly rejecting the null hypothesis when it is false
= 1 - probability of type 2 error
the chance of detecting a true difference when there really is one

59
Q

Power can be increased by

A

increasing sample size, using more precise measuring instruments, and using a higher significance value.

60
Q

Odds

A

a ratio of the number of people who develop an outcome to the number of people who don’t

61
Q

usually used in case control studies

A

odds ratio

Odds ratios are always bigger so they look better in a paper abstract. Odds can be >1 but risk is always <1

62
Q

Multivariate analysis

A

allow confounding factors to be taken into account, by adjusting for these factors.performed using statistical models.

63
Q

Hazard ratio

A

measure of an effect of an intervention on an outcome of interest over time.

64
Q

Precision

A

quantifies a test’s ability to produce the same measurements with repeated tests.

65
Q

Bias

A

systematic introduction of error into a study that can distort the results in a non-random way

is the tendency of a statistic to overestimate or underestimate a parameter.

66
Q

Selection bias

A

Error in assigning individuals to groups leading to differences that may influence the outcome. The subjects are not representative of the population
Especially a problem in cohort studies

67
Q

Types of selection bias

A
  1. sampling bias ( eg due to non random sample of population)
  2. volunteer/non responder bias
  3. Time interval bias
  4. Attrition/ loss to follow up bias
  5. prevalence/incidence bias (Neyman bias)- study is investigating a condition that is characterised by early fatalities or silent cases
  6. admission bias (Berkson’s bias)
  7. healthy worker effect
68
Q

Recall bias

A

Difference in the accuracy of recollection of study participants, possible due to whether they have the outcome or not
Especially a problem in case control studies

69
Q

Publication bias

A

Failure to publish/include results from valid studies, often because they show a negative or uninteresting result
Important in meta-analyses and systematic reviews where studies showing negative results may be excluded

70
Q

Hawthorne effect

A

Group changing its behaviour due to the knowledge that it is being studied

71
Q

Procedure bias

A

Subjects in different groups receive different treatment, other than just the interventions
Eg elderly patients could receive human contact along with intervention whereas control patients may get no extra human contact

72
Q

Measurement bias

A

It occurs when the accuracy of information collected about or from study participants is not equal between cases and controls
Quantitive eg: due to poor calibration of measuring instruments
Qualitative eg: study participant is less likely to answer a question due to stigma associated with the answer

73
Q

Lead time bias

A

Early diagnosis appears to prolong survival

74
Q

Length bias

A

Screening over represents less aggressive disease
A reason why cancers detected by screening may on average be more slowly progressive.
screening tends to detect disease which progresses more slowly and has long asymptomatic periods.. Conversely, aggressive disease is more likely to be missed by screening

75
Q

Effective Reproduction Number

A

is the average number of secondary infections produced by a typical infective agent; if this number is greater than 1 then it is impossible to eradicate an infection.

76
Q

Cost Effectiveness Analysis (CEA):

A

Used when the effect (outcome) of the two interventions is expected to vary
The outcome is measured in natural units e.g. BP, cholesterol level, mortality, live years saved
The outcome is one dimensional - addresses quantity or quality, not both
Costs & outcomes are combined into a single measure to allow comparison

77
Q

Cost Utility Analysis (CUA):

A

Used when the effect (outcome) of the interventions on health status has two or more dimensions
Measures outcome in terms of quantity and quality. Combines these into a single measure e.g. the QALY = Quality Adjusted Life Year
A measure which tries to combine a quantitative measure (months gained, years gained) with a qualitative measure of the quality of that measure
Can be used to compare interventions with a disease or condition or across different diseases or treatment options

78
Q

QALY

A

Based on number of years of life that would be added by intervention
Each year of perfect health = 1, to a value of 0 for death
If extra years are not lived in full health (eg loss of limb) = between 0 and 1

79
Q

Cost Benefit Analysis (CBA):

A

Places a monetary value on benefits or outcomes
Generally based on individuals’ observed or stated preferences and values for something
Most common approach is “willingness to pay”
most comprehensive but rarely undertaken.

80
Q

Cost Minimisation Analysis (CMA):

A

Used when the effect (outcome) of both interventions is identical (or assumed to be identical)
No outcome measurement
Only costs are accounted for
cannot provide answers where effectiveness is different between competing alternatives

81
Q

Cost Consequence:

A

Measures costs, measures consequences

82
Q

Sensitivity Analysis:

A

‘Vary key assumptions and see if it has an impact’

83
Q

Incremental cost effectiveness ratio

A

to work out cost effectiveness

This is the ratio of the change in costs of therapeutic interventions (compared with alternatives/no treatment) to the change in effects of the intervention ( measured as clinical outcome of QALY)

ICER=(Cost A- Cost B) / (QALYs b -QALYsA)

84
Q

Why - Combining data in a meta analysis

A

Increases the number of patients being analysed ( increases size of study sample)
Improves precision and reduces the width of the confidence intervals
Can demonstrate a statistically significant result when none of the trials could do this individually

(only poss with homogenous data eg mortality)

85
Q

Regression to the mean

A

People often get better or worse regardless of intervention = not the intervention causing change

86
Q

Equipoise:

A

Equipoise refers to the situation where the researchers have no preference between the treatments being studied in a trial
When it is lost: when those designing the trial or recruiting patients into it do have a preference for one treatment over another, it can be considered unethical to recruit patients into the trial as you are offering some patients what you believe to be a “worse option”

Medicine (72 decks)

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