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Pharmacy
Critical Appraisal Level 2
Evaluating studies using the
Critical Appraisal Skills
Programme (CASP) Tools
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Pharmacy
What is Critical Appraisal?
Critical appraisal is the process of
systematically examining research papers
before using the evidence to form a decision
Critical appraisal allows us to make sense of
research evidence and close the gap
between research and practice
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Why Critically Appraise?
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In evidence based medicine the clinician should
use the best available evidence to decide which
treatment option is best for their patient
To determine the best treatment option we must
have critical appraisal skills to assess the quality
of research
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Advantages of Critical Appraisal
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Provides a systematic way of assessing the
validity, results and usefulness of published
research
Allows us to improve healthcare quality
Encourages objective assessment of the
usefulness of all information/evidence
Critical appraisal skills are not difficult to develop –
mostly common sense!
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Disadvantages of Critical Appraisal
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Can be time consuming
It may highlight that current practice is in
fact ineffective
May highlight a lack of good evidence in an
area of interest
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Help with Critical Appraisal
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It is difficult to remember all the issues which must
be taken into account when reading research
papers
To help with this the Public Health Resource Unit
in Oxford have produced tools to evaluate most
types of study as part of the Critical Appraisal
Study Programme (CASP)
These are called CASP tools
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CASP Tools
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CASP Tools have been developed to help us
appraise:
Systematic Reviews
RCTs
Case-control studies
Cohort Studies
Tools for other types of study (e.g. qualitative
research, economic evaluations and diagnostic
studies) are also available
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CASP Tools (2)
The tools break critical appraisal of each study type
into 10-12 manageable questions
The questions in each tool will vary slighty as study
designs are different
They are available at:
http://www.sph.nhs.uk/what-we-do/public-healthworkforce/resources/critical-appraisals-skillsprogramme
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Randomised Controlled Trials
(RCTSs)
Pharmacy
RCTs are the best type of research study to determine if a
specific intervention produced the desired outcome in a specific
population
e.g. The HOPE study: Does ramipril (the intervention) prevent
myocardial infarction, stroke or death from cardiovascular
causes (the outcomes) in patients who were at high risk for
cardiovascular events but who did not have left ventricular
dysfunction or heart disease (the population)
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RCTs (continued)
RCTs should have a clearly defined research question in
relation to the population, outcome and intervention
RCTs can minimise bias and use the most appropriate study
design for investigating the effectiveness of a specific
intervention or treatment
RCTs are, however, not automatically of good quality and
should be appraised critically
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Appraising RCTs
Three broad issues should be considered when
examining a report of an RCT:
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Validity
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Results
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Relevance
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The CASP tool prompts us to ask questions
which will assess each of these issues in more
detail
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Screening Questions in CASP tool
Questions 1 and 2 in the RCT CASP tool are
screening questions to assess if:
The RCT asks a clearly focused study question (i.e.
is population, intervention and outcome being
investigated clearly stated?)
Whether an RCT is the most appropriate study
design to ask this question
If the answer to either of these is “no” then it is
probably not worth continuing with the rest of the
questions
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Validity
When assessing validity the methods used in the study are
appraised
If the research methods are flawed then this may invalidate the
results of the trial
To critically appraise the methodology of an RCT you will look at
sample size, randomisation and baseline characteristics,
blinding, follow-up, data collection and interventions. These
elements are dealt with by questions 3-7 in the CASP tool for
RCTs
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Randomisation and Baseline
Characteristics (Q3 in CASP Tool)
Randomisation reduces the possibility of bias
Method of randomisation should be robust and neither the
participants or clinicians should be aware which group patients
will be in before randomisation
Most robust methods are computer- generated numbers or
tables of random numbers (gives 50:50 chance of being in either
group)
Stratification can be used to ensure similar baseline
characteristics in both groups
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Randomisation and Baseline
Characteristics (Q3 in CASP Tool)
The make-up of treatment and control groups should be very
similar and the only difference should be the intervention under
investigation in the study
This is so that we can be confident that the outcome is due to
the intervention and not to any other confounding factors
Consider:
Is the sample large enough? (see also Q7)
Was the randomisation process robust?
Was there stratification?
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Blinding (Q4 in CASP Tool)
If the people involved in the study are not aware who is in the
treatment or control group this reduces the possibility of bias i.e.
ideally a study should be double-blind
Single-blind and open-label studies would be expected to have
higher chance of producing biased results
To blind patients and clinicians the intervention and placebo
must appear to be identical – this is not always possible
Consider – if every effort was made to achieve blinding
-if it matters in the study being reviewed (i.e. could there be
observer bias in the results that are used?)
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Follow-up (Q5 in CASP tool)
If a large number of participants withdraw from a trial before its
conclusion their loss may distort the results
Crossover between groups can also distort the results as the
effects of randomisation can be lost
In most cases intention-to-treat analysis should be used – i.e.
the final results should be analysed according to the original
randomisation
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Interventions (Q6 in CASP tool)
It is important that, aside from the intervention under
investigation, the groups are treated equally as this means the
outcome of the study can be attributed to the intervention
Consider:
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Were groups reviewed at the same time intervals?
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Were any other treatments allowed in either group?
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Interventions (Q6 in CASP tool)
Also consider:
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If tests or measurement s were used were they conducted
by appropriate personnel and established tests were used?
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Were assessments frequent enough to show a pattern of
response?
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Is the duration of the study sufficient?
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Sample Size (Q7 in CASP tool)
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A study should include enough participants so that the
researchers can be reasonably sure that there is a high
chance of detecting a beneficial effect
A power calculation should be carried out before the
participants are enrolled to estimate how many people
need to be recruited to achieve a certain level of certainty
(usually aim for 80-90%)
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Results
The results of a RCT should be scrutinised in a similar way to
the method
Broad considerations are:
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What are the results?
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How are the results presented?
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How precise are the results?
This is dealt with by questions 8 and 9 in the CASP RCT tool
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Presentation of Results
(Q8 in CASP tool)
Results can be expressed as:
• Relative Risk – if we hope that the intervention will lead to
LESS outcomes (e.g. MI, stroke, death) we want a hazard
ratio of less than 1
• Absolute Risk – proportion of people experiencing an event
in each group
Results may be reported as either of the above or as both
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Presentation of Results (2)
The results presented should relate to the objectives set out in
the original description of the study method
Relative or absolute measures may have little meaning in
relation to clinical practice
Numbers needed to treat (NNT) may make measures more
understandable
NNT is the number of people who must be treated to produce
one additional successful outcome
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Precision of Results
(Q9 in CASP tool)
Statistical tests are used to establish whether the results of a trial
are real or whether they occurred purely by chance
There will always be some doubt as the trial only looks at a
sample of the population
Confidence intervals and p-values indicate the level of certainty
around the results
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Confidence Intervals (CI)
Inferences based on random samples are uncertain because
different results would be obtained each time a study was
repeated
CIs indicate the range of doubt around the results and represent
the range of values within which the true value lies
Therefore, the narrower the range, the more convincing the
results
If CIs overlap this indicates that the study has failed to
demonstrate a difference
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P-values
P-values describe the probability that a result has
happened by chance
P<0.05 is usually described as statistically significant
and means that the results are unlikely to be due to
chance
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Statistics Used
There should be adequate statistical analysis of the
relevant results and the statistical analysis used
should be appropriate to trial size and design
Statistical tests used should be adequately described
and referenced
Consider whether a statistician is listed as one of the
authors
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Clinical vs. Statistical
Significance
A statistically significant difference in favour of a drug does not
always indicate a clinically significant difference
e.g. in a study of two antihypertensives a difference in BP of 12mmHg may be statistically significant but is this likely to confer
significant clinical benefit?
Also consider surrogate markers versus clinical endpoints (e.g.
an increase in bone mineral density versus a decrease in
fracture rate)
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Relevance
If the methodology and results are acceptable then the
applicability of the results to the local population should be
considered
Some broad issues include:
• Relevance to the local population
• Were the outcomes considered clinically important?
• Risk versus benefit of treatment
Q10 in the CASP tool prompts us to consider the issues around
the relevance of our study to our local population
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Relevance to Local Population
It is important to consider if there are any differences between
the participants in the trial and the local population that would
make it impossible to apply the results locally. Think about:
Inclusion/exclusion criteria – e.g. age, ethnicity, co-morbidities,
concomitant medication
Local healthcare provision – the setting may have been in a
different healthcare system and it may not be possible to provide
similar care locally
Control group – is the standard treatment used in the study
better or worse than local standard? Are realistic comparative
doses used?
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Importance of outcomes
Were all clinically important outcomes considered? A single RCT
is unlikely to address all the clinically important outcomes but
consider if the original question has been answered and if any
other important outcomes have been missed out.
Think about outcomes form the point of view of:
• The patient and their family/carers
• The clinician using the treatment
• Policymakers
• The wider community
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Risks versus Benefits
Risks:
Safety – the risk of serious side effects may outweigh
the benefits of treatment (can calculate NNT vs. NNH)
Tolerability – what was the drop-out rate in the study
compared to withdrawal rates on the current standard
treatment?
Is the benefit of the treatment large enough to
outweigh these risks?
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Other Considerations
Cost implications – financial information is not normally included
in a trial. Economic evaluations may be available
Simplicity of use – patient factors such as compliance, method of
administration or complicated devices may limit the usefulness of
a treatment in clinical practice
Quality of life data – gives more information from the patient’s
perspective
Sponsorship – sponsorship of the research or author affiliations to
drug to drug companies may affect how the results are presented
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Look out for……..
A lot of work and funding goes into drug development and
clinical trials and drug companies and researchers will want a
positive result from their hard work
When apprasing RCTs beware of the negative aspects of the
study being glossed over to make it appear positive. This can
include:
1. Use of sub-group analysis
2. Use of composite end points
3. Analysis of secondary outcomes
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Sub-group analysis
Sub-group analysis is when results are broken down into patient
sub-groups e.g. elderly, patients with a history of stroke/diabetes
If the result of the overall trial is negative it may be positive in a
specific sub-group
Trials may not be powered to detect differences in sub-groups
containing small numbers of patients
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Use of Composite Endpoints
Sometimes the desired outcome of a clinical trial is
relatively rare e.g. fatal MI
To show a difference between intervention and control huge
numbers of participants in very long term trials would be
required
Composite end-points may be used instead e.g. risk of MI,
stroke, death or hospital admission due to cardiac causes
Not all the individual components are always of equal
importance to all patients
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Analysis of Secondary Outcomes
The primary outcome is the most important outcome of an
RCT
If no statistically significant increase in efficacy is observed
with the new treatment then secondary outcomes which
are significant may be quoted in the results
The number of patient enrolled in a trial is based on the
primary outcome
It is poor practice to disregard the primary outcome and
quote a secondary outcome as the main result
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Remember!
Healthcare decisions are not usually made on
the basis of one trial. Other factors and other
evidence may also have to be considered
when making a decision
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Appraising other publications
In addition to RCTs other publications may be used to
contribute to evidence-based decision making
In order of importance the usual hierarchy is:
 Systematic reviews
 RCTs
 Observational studies e.g. cohort, case-control or
cross-sectional studies
 Case reports, case studies
 Expert consensus
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Systematic Reviews
Systematic reviews seek to bring the same level of rigour
to reviewing research evidence as should be used in
producing research evidence. They:
• Identify relevant published and non-published evidence
• Select studies for inclusion and assess the quality of
each
• Present a summary of the findings with due
consideration
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Systematic Reviews (2)
Meta-analysis is a statistical technique for combining the
results of independent studies and is the technique
normally used to combine the results of selected studies
for systematic review.
Validity of meta-analysis depends on the quality of the
systematic review on which it is based.
Like RCTs systematic reviews should not be considered to
automatically be of good quality and should be critically
appraised.
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Observational Studies
Includes cohort studies, case-control studies and crosssectional studies
These studies lack the controlled design of RCTs and the
evidence which comes from this type of study is therefore
considered less robust
In some cases observational studies provide the only
evidence available (e.g. emerging safety issues) and
therefore it is also important that we are able to critically
appraise these
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Over to you……
Now use what you have learned from this
presentation and the tools available online to
critically appraise a randomised controlled trial
and a cohort study
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