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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.
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Paper for appraisal and reference:.........................................................................................................
Section A: Are the results of the study valid?
1. Did the study address a clearly Yes HINT: A question can be ‘focused’ focused issue?in terms of
Can’t Tell • the population studied
•
No the risk factors studied
• is it clear whether the study tried to detect a beneficial or harmful effect
• the outcomes considered
Comments:
2. Was the cohort recruited in Yes HINT: Look for selection bias which might an acceptable way?compromise the generalisability of the
Can’t Tell findings:
• was the cohort representative of a
No defined population
• was there something special about the cohort
• was everybody included who should have been
Comments:
Is it worth continuing?
3. Was the exposure accurately Yes HINT: Look for measurement or measured to minimise bias? classification bias:
Can’t Tell • did they use subjective or objective measurements
No • 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
Comments:
4. Was the outcome accurately Yes HINT: Look for measurement or measured to minimise bias? classification bias:
Can’t Tell • did they use subjective or objective measurements
No • 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)
Comments:
5. (a) Have the authors identified all important confounding factors?
Comments:
5. (b) Have they taken account of the confounding factors in the design and/or analysis?
Comments:
6. (a) Was the follow up of subjects complete enough?
6. (b) Was the follow up of
subjects long enough?
Yes HINT:
• list the ones you think might be
Can’t Tell important, and ones the author missed No
Yes HINT:
• look for restriction in design, and
Can’t Tell techniques e.g. modelling, stratified-, regression-, or sensitivity analysis to
No correct, control or adjust for confounding
factors
Yes HINT: Consider
• the good or bad effects should have had long enough to reveal
Can’t Tell themselves
• the persons that are lost to follow-up
No 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 Yes
Can’t Tell
No
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:
9. Do you believe the results? Yes HINT: Consider
• big effect is hard to ignore
Can’t Tell • can it be due to bias, chance or confounding
No • 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)
Comments:
Section C: Will the results help locally?
HINT: Consider whether
• a cohort study was the appropriate method to answer this question • the subjects covered in this study could be sufficiently different from your population to cause concern
10. Can the results be applied to Yes the local population?
Can’t Tell
No
• 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 Yes with other available
evidence? Can’t Tell
No
Comments:
12. What are the implications of Yes HINT: Consider this study for practice? • one observational study rarely
provides sufficiently robust
Can’t Tell
evidence to recommend changes to clinical practice or within health
No policy decision making
• for certain questions, observational studies provide the only evidence
• recommendations from
observational studies are always stronger when supported by other evidence
Comments: