Transcript abstracts - how to assess

How to assess an
abstract
Objectives
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Understand the principle differences
between qualitative and quantitative
research
Understand the basic statistics
employed in research
Be able to assess a piece a research
with confidence!
Qualitative research
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Which type of questions does it
answer?
What methodologies are employed?
Improving their validity
Assessing a qualitative paper
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Is the qualitative approach
appropriate?
Methodology
Data analysis
Results and conclusion
Quantitative
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Types of quantitative research
RCT – design features, advantages &
disadvantages
Cohort Studies
Case control studies
Cross section surveys
BIAS
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Selection bias
Observer bias
Participant bias
Withdrawal or drop out bias
Recall bias
Measurement bias
Publication bias
Assessing quantitive research
Commonly used statistics
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P values
Relative Risk Reduction
Absolute Risk Reduction
Numbers Need to Treat
Sensitivity
Specificity
Positive Predictive Value
Negative Predictive Value
P values & CI
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p value = the probability of the
outcome being due to chance
p = 1 in 20 (0.05).
> 1 in 20 (0.051) = not significant
< 1 in 20 (0.049) = statistically significant
CONFIDENCE INTERVALS
This defines the range of values between which we
could be 95% certain that this result would lie if
this intervention was applied to the general
population
RR, AR, ARR & RRR
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What are they?
How do you calculate them?
Warfarin & AF study
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The annual rate of stroke was 4.5% for the
control group
Absolute Risk (Control group) = 0.045
1.4% for the warfarin group
Absolute Risk (experimental group) = 0.014
Absolute Risk Reduction = 0.045 – 0.014 =
0.031
NNT = 32
Relative Risk = 0.014/0.045 = 0.31 = 31%
Relative Risk Reduction = 0.045 –
0.014/0.045 = 0.68 = 68%
NNT
How many people you need to treat
with the study intervention to stop
the study event from happening
once.
1/ARR = Number Needed to Treat.
NNT EXAMPLES
Streptokinase + aspririn v.
placebo (ISIS 2)
tPA v. streptokinase
(GUSTO trial)
Simvastatin v. placebo in IHD
(4S study)
Treating hypertension in the
over-60s
Aspirin v. placebo in healthy
adults
prevent 1 death
at 5 weeks
save 1 life with
tPA usage
prevent 1
event in 5y
prevent 1 event
in 5y
prevent MI or
death in 1 year
20
100
15
18
500
Screening tests – assessing
their performance
Sensitivity
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The test’s ability to correctly identify those
people with disease.
If Sensitivity is <100% Disease is missed.
So =
True Positives
True Positives + False negatives
i.e. all those who truly Have the disease
Specificity
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The test’s ability to correctly exclude those
people without disease
If Specificity <100% then healthy people
are told they may have disease
=
True Negatives
True Negatives + False Positives
i.e. all those who truly don’t have the
disease
Positive predictive value
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If the test is positive, what is the
chance of the person having the
disease = positive predictive value.
True Positives
True positives + False Positives
Negative Predictive Value
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If the test is negative, what chance
is there that the person doesn’t have
the disease = negative predictive
value.
True negative
True negative + False negative
Accuracy
True positive + True negative
True negative +true positive+ false negative + false positive
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Urine dipstick to screen for
Diabetes
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Example- urine dip test vs GTT (the gold
standard)
Diabetes +ve
(n=27)
Diabetes –ve
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Result of urine test
(n=973)
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Glucose present (13)
True +ve 6
False +ve 7
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Glucose absent (987)
False –ve 21
True -ve 966