help you make sense of a Cohort Study

CASP Checklist: 12 questions to help you make sense of a Cohort Study
How to use this appraisal tool: Three broad issues need to be considered when appraising a
cohort study:
Are the results of the study valid? (Section A)
What are the results? (Section B)
Will the results help locally? (Section C)
The 12 questions on the following pages are designed to help you think about these issues
systematically. The first two questions are screening questions and can be answered quickly.
If the answer to both is “yes”, it is worth proceeding with the remaining questions. There is
some degree of overlap between the questions, you are asked to record a “yes”, “no” or
“can’t tell” to most of the questions. A number of italicised prompts are given after each
question. These are designed to remind you why the question is important. Record your
reasons for your answers in the spaces provided.
About: These checklists were designed to be used as educational pedagogic tools, as part of a
workshop setting, therefore we do not suggest a scoring system. The core CASP checklists
(randomised controlled trial & systematic review) were based on JAMA ‘Users’ guides to the
medical literature 1994 (adapted from Guyatt GH, Sackett DL, and Cook DJ), and piloted with
health care practitioners.
For each new checklist, a group of experts were assembled to develop and pilot the checklist
and the workshop format with which it would be used. Over the years overall adjustments
have been made to the format, but a recent survey of checklist users reiterated that the basic
format continues to be useful and appropriate.
Referencing: we recommend using the Harvard style citation, i.e.: Critical Appraisal Skills
Programme (2018). CASP (insert name of checklist i.e. Cohort Study) Checklist. [online]
Available at: URL. Accessed: Date Accessed.
©CASP this work is licensed under the Creative Commons Attribution – Non-CommercialShare A like. To view a copy of this license, visit http://creativecommons.org/licenses/by-ncsa/3.0/ www.casp-uk.net
Critical Appraisal Skills Programme (CASP) part of Oxford Centre for Triple Value Healthcare Ltd www.casp-uk.net
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Section A: Are the results of the study valid?
1. Did the study address a clearly
focused issue?
Yes HINT: A question can be ‘focused’
in terms of
• the population studied
• the risk factors studied
• is it clear whether the study tried to
detect a beneficial or harmful effect
• the outcomes considered
Can’t Tell
No
Comments:
2. Was the cohort recruited in
an acceptable way?
Yes HINT: Look for selection bias which might
compromise the generalisability of the
findings:
• was the cohort representative of a
defined population
• was there something special about the
cohort
• was everybody included who should
have been
Can’t Tell
No
Comments:
Is it worth continuing?
Paper for appraisal and reference:……………………………………………………………………………………………
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3. Was the exposure accurately
measured to minimise bias?
Yes HINT: Look for measurement or
classification bias:
• did they use subjective or objective
measurements
• do the measurements truly reflect what
you want them to (have they been
validated)
• were all the subjects classified
into exposure groups using the
same procedure
Can’t Tell
No
Comments:
4. Was the outcome accurately
measured to minimise bias?
Yes HINT: Look for measurement or
classification bias:
• did they use subjective or objective
measurements
• do the measurements truly reflect what
you want them to (have they been
validated)
• has a reliable system been
established for detecting all the cases (for
measuring disease occurrence)
• were the measurement
methods similar in the different groups
• were the subjects and/or
the outcome assessor blinded to
exposure (does this matter)
Can’t Tell
No
Comments:
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5. (a) Have the authors identified
all important confounding
factors?
Yes HINT:
• list the ones you think might be
Can’t Tell important, and ones the author missed
No
Comments:
5. (b) Have they taken account of
the confounding factors in the
design and/or analysis?
Yes HINT:
• look for restriction in design, and
techniques e.g. modelling, stratified-,
regression-, or sensitivity analysis to
correct, control or adjust for confounding
factors
Can’t Tell
No
Comments:
6. (a) Was the follow up of
subjects complete enough?
Yes HINT: Consider
• the good or bad effects should have
had long enough to reveal
themselves
• the persons that are lost to follow-up
may have different outcomes than
those available for assessment
• in an open or dynamic cohort, was
there anything special about the
outcome of the people leaving, or the
exposure of the people entering the
cohort
Can’t Tell
No
6. (b) Was the follow up of
subjects long enough?
Yes
Can’t Tell
No
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Comments:
Section B: What are the results?
7. What are the results of this study? HINT: Consider
• what are the bottom line
results
• have they reported the rate or
the proportion between the
exposed/unexposed, the
ratio/rate difference
• how strong is the association
between exposure and
outcome (RR)
• what is the absolute risk
reduction (ARR)
Comments:
8. How precise are the results? HINT:
• look for the range of the confidence
intervals, if given
Comments:
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9. Do you believe the results? Yes HINT: Consider
• big effect is hard to ignore
• can it be due to bias, chance or
confounding
• are the design and methods of this
study sufficiently flawed to make the
results unreliable
• Bradford Hills criteria (e.g. time
sequence, dose-response gradient,
biological plausibility, consistency)
Can’t Tell
No
Comments:
Section C: Will the results help locally?

• your local setting is likely to differ
much from that of the study
• you can quantify the local benefits and
harms
Comments:
11. Do the results of this study fit
with other available
evidence?
Yes
Can’t Tell
No
Comments:
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12. What are the implications of
this study for practice?
Yes HINT: Consider
• one observational study rarely
provides sufficiently robust
evidence to recommend changes
to clinical practice or within health
policy decision making
• for certain questions,
observational studies provide the
only evidence
• recommendations from
observational studies are always
stronger when supported by other
evidence
Can’t Tell
No
Comments: