1. humor me by drawing this picture ……………………
you have just drawn the shape of every epidemiological study

2. GATE: Graphic Appraisal Tool for Epidemiology
Work in progress since 1991
1 picture, 2 formulas & 3 acronyms

3. GATE: Graphic Appraisal Tool for Epidemiology Graphic Architectural Tool for Epidemiology Graphic Approach To Epidemiology making epidemiology accessible
Work in progress since 1991

4. 4th year medical students 1991
My main inspiration for developing GATE was an alternative to traditional epi courses which didn’t excite my 4th year medical students
4th year medical students 1991

5. Jerry Morris numerator denominator epidemiology =
In 1976 I read this definition of epidemiology in Jerry Morris’ book (Uses of Epidemiology) and it turned me onto the simplicity of the epidemiological paradigm
numerator denominator
epidemiology =
In: Uses of Epidemiology 1957

6. I now teach simple epidemiology to over 1000 year 1 university students each year as well as to 4th year medical students and postgraduates students using the GATE frame

7. presentation outline GATE is a framework for:
study design
study analysis
study error
practicing EBM
1 picture, 2 formulas & 3 acronyms

8. 1

9. GATE: a framework for study design 1 picture
The shape of every epidemiological studies, so a useful design tool
every epidemiological study can be hung on the GATE frame
1 picture, 2 formulas & 3 acronyms

10. cohort / longitudinal / follow-up study
1 picture: GATE frame
cohort of British doctors
smoking status allocated by measurement (observation)
smokers
non-smokers
followed for 10 years
yes
lung cancer events counted
A cohort study starts with a cohort of people (the triangle) – in the example this is British drs – real study …all Br drs …. , the cohort is then allocated to different groups based on the factors being studied (the circle) …smokers and non smokers.
They are then followed over a period time – illustrated by the downward pointing arrow which identifies the study as longitudinal
And the Outcomes are counted – eg lung cancer
Useful study design for investigating risk factors for disease - both causes of disease and predictors of disease (including prognostic factors) no
cohort / longitudinal / follow-up study
1 picture, 2 formulas & 3 acronyms

11. randomised controlled trial
1st acronym: PECOT P
Participants
British doctors
randomly allocated to aspirin or placebo
Exposure E C
Comparison
aspirin
placebo
PECOT is an epidemiologist’s version of PICO – I’m embarrassed to say that I came up with it completely independently – more generic epi, includes time – timing is everything to an epidemiologist – I had missed the development of clinical epidemiology having been trained in public health epidemiology where clinical epidemiology was considered an oxymoron
this slide presents the same study as in the previous slide to introduce the first acroynym PECOT which identified the 5 main components of most epi studies.
Outcomes
yes O MI
Time no T
5 years
randomised controlled trial
1 picture, 2 formulas & 3 acronyms

12. cross-sectional (prevalence) study
middle-aged Americans
body mass index measured
overweight E C
‘normal’ weight
diabetes status measured in all participants
yes T O
The horizontal arrow identifies the study as cross-sectional no
cross-sectional (prevalence) study

13. diagnostic test (prediction) study
middle-aged American women P
receive mammogram screening test
mammogram positive E C
mammogram negative
yes
breast cancer O
Another study design you will come across is the diagnostic study. There are 2 versions of this design - here is the one that is the easiest to introduce you to and the other version is on the next slide
Note the arrow goes sideways T no
diagnostic test (prediction) study

14. diagnostic (test accuracy) study
middle-aged American women P
Gold Standard
breast cancer E C
no breast cancer
positive
mammogram
test O
Here is the other version. How you fill in the GATE frame depends on your question - in this case cancer yes/no is in the circle, not the square
negative T
diagnostic (test accuracy) study

15. (all nested in virtual cohort studies)P
smokers E C
non-smokers
smoking status measured
cases
yes O
lung cancer
Here I show the stucture of a case-control study which is basically a very efficient version of a cohort study in which the investigators wait until people in a virtual cohort have had the study outcome - these people with the study outcome are known as the cases and the investigators also typically recruit a group of non cases, ideally from the same virtual cohort as the controls..
As with randomised trials and cohort studies, there is a specific presentation on case control studies later in the course.
Useful study design for investigating risk factors for disease - both causes of disease and predictors of disease (including prognostic factors) no
controls T
case-control study
(all nested in virtual cohort studies)

16. £100
If you can find an epidemiological study that doesn’t fit the GATE frame I’ll give you £100

17. 2

18. GATE: a framework for study analysis: 1st formula: occurrence = outcomes ÷ population
the numbers in epidemiological studies can be hung on the GATE frame
1 picture, 2 formulas & 3 acronyms

19. 1st formula: occurrence of outcomes =
number of outcomes ÷ number in population/group
British doctors P
Participant Population
smoking status measured
Exposure Group
Comparison Group EG CG
smokers
non-smokers
The first formula is shown at the top of the slide and goes like this: To illustrate this formula I need to introduce a little additional jargon. 1st, instead of the E and C in PECOT, the first formula uses EG and CG. These are the populations used in the 1st formula and are the numbers of people with the study exposure and the number of people with the study comparison exposure.. Also the top quadrants of the Outcomes square - the number of people who develop the main study Outcome are labelled a and b
Epidemiology is the study of the occurrence of dis-ease in populations and in different groups within populations. The 1st formula defines the goal of every epidemiological study
Occurrence is a generic term that covers risk, probability, incidence, prevalence, likelihood etc PPV, NPV, sensitivity, specificity
Outcomes a b
yes O
Time
Lung cancer no T
10 years

20. Exposure Group Occurrence (EGO) = a÷EG
Population P
British doctors
smoking status measured
Exposure Group EG CG
Comparison Group
smokers
non-smokers
Outcomes a b
yes O
Time
The most important GATE jargon/abbreviations you need to learn to understand the analysis of epidemiological studies are shown in the next 2 slides.
In this slide I want to introduce you to EGO - the exposed group occurrence or a/EG which is the first formula applied to the exposure group = which in this example would be the number of smokers who developed lung cancer divided by the total number of smokers in the study. This is the occurrence of lung cancer in smokers
Lung cancer no T
10 years
Exposure Group Occurrence (EGO) = a÷EG
= number of outcomes (a) ÷ number in exposed population (EG)

21. Comparison Group Occurrence (CGO) = b÷CG
Population
British doctors
randomly allocated
Exposure Group EG CG
Comparison Group
aspirin
placebo
Outcomes a b
yes O
Time
and In this slide I want to introduce you to CGO - the comparison group occurrence or b/EG which is the first formula applied to the comparison group = which in this example would be the number of non-smokers who developed lung cancer divided by the total number of non smokers in the study. This is also the occurrence (or risk or incidence)of lung cancer in non- smokers MI no T
5 years
Comparison Group Occurrence (CGO) = b÷CG
= number of outcomes (b) ÷ number in comparison population (CG)

22. Epidemiology = Numerator ÷ Denominator
middle-aged American women P
Participant Population
receive mammogram screening test D
Exposure Group EG
Comparison Group
mammogram negative
mammogram positive
Jerry Morris slide: Every study fits the GATE frame because epidemiology = N/D N
Time
Outcomes a T
yes O
breast cancer no

23. the goal of all epidemiological studies is to calculate EGO and CGO
British doctors
smoking status measured
smokers EG CG
non-smokers
The downward pointing arrow identifies the study as longitudinal
10 years
CGO:
Occurrence of cancer in non-smokers
EGO:
Occurrence (risk) of cancer in smokers
yes a b O T no
Lung cancer

24. Average blood glucose in EG Average blood glucose in CGP
Middle-aged Americans
Body Mass Index (BMI) measured
High BMI EG CG
Low BMI
high
Here is an example of calculating EGO and CGO when the outcome is a numerical variable rather than a categorical variable.
EG and CG are categorical variables - that is high BMI and low BMI, but in this example the Outcome - blood glucose - is presented as a numerical variable - the blood glucose measures in every person in EG and in CG.
You can see on this slide that when the Outcome is a numerical variable the calculation of EGO and CGO produce an average. The 1st formula is given at the bottom of the slide .....
as an aside, the Exposure/Comparison groups in this example actually started as a numerical variable - that is BMI - but you can convert numerical variables into a categorical variables. I could also have categorised the blood glucose values into 2 or more categories. O
EGO:
Average blood glucose in EG
CGO:
Average blood glucose in CG
low

25. cross-sectional study with numerical measuresP
Middle-aged Americans
Body Mass Index (BMI) measured
High BMI E C
Low BMI T
high
blood glucose O
Exposure and outcomes can be continuous
low
cross-sectional study with numerical measures

26. O T P E C EGO: likelihood of a positive mammogram if breast cancer
Middle-aged American women
Gold Standard
Breast cancer E C
no Breast cancer
positive O
mammogram
Here is EGO and CGO for diagnostic test accuracy studies T
negative
EGO:
likelihood of a positive mammogram if breast cancer
CGO:
likelihood of a positive mammogram if no breast cancer

27. occurrence = outcomes ÷ population
1st formula:
occurrence = outcomes ÷ population
its all about EGO and CGO
EGO ÷ CGO = Relative Risk (RR)
EGO – CGO = Risk Difference (RD)
measures of occurrence: risk; rate; likelihood; probability; average; incidence; prevalence

28. 3

29. GATE: framework for nonrandom error 2nd acronym: RAMBOMAN
Recruitment
Allocation
Maintenance
Blind
We use the acronym RAMboMAN to help readers identify the major errors in epi studies. Professor Paul Glasziou from Bond University in Australia, one of the guru’s of evidence based medicine, came up with the RABMO acronym
Objective
Measurements
ANalyses
1 picture, 2 formulas & 3 acronyms

30. Recruitment of participants
Study setting
RAMBOMAN
Eligible population
recruitment process P P
The R is for recruitment and one of the first question one should ask when appraising a study is:
were the participants recruited for the study relevant to the study objectives? You would hope they are but lets say you were wanting to determine if a rather strict and bland diet was effective in helping obese people lose weight. You may start by trying to recruit typical obese people in your community but the people who finally agree to take part may be a very small proportion of the people you invited and may be highly motivated. You carry out the study and determine that the diet is very effective. But you are left asking - just who are the findings applicable to? Perhaps only highly motivated volunteers are likely to stick to such a strict bland diet but most of the obese people you deal with, dont fit this description.
Generalisability of findings will be influenced by how well recruitment is described - not always relevant
Recruitment of participants
‘who are the findings applicable to?’

31. RAMBOMAN: ‘were participants well Allocated to exposure & comparison groups?’
was Allocation to EG & CG successful?
RCT: allocated by randomisation (e.g to drugs)
Cohort: allocated by measurement (e.g. smoking)
EG & CG similar at baseline?
E & C measures accurate?
EG CG
EG CG
In RCTs the goal is to allocate participants to two (or more) groups that are very similar prior to the EG & CG interventions are initiated – they would have the same occurrence of outcomes if there were treated the same
In non-randomised studies the goal is to allocate participants to two ore more groups so that they are accurately categorised by their exposure/comparison status and to also measure other factors relevant to the study outcome that may differ between the groupss O O T T

32. RAMBOMAN completeness of follow-up compliance contamination
‘were Participants well Maintained in the groups they were allocated to?’
EG CG
completeness of follow-up
compliance
contamination
co-interventions
The first M in Ramboman stands for maintenance.
If the study has done a good job of allocated participants to EG and CG at the beginning of the study, you want to determine if most of the participants remained in their allocated groups. In other words were they maintained in the groups to which they were initially allocated throughout the study?
This can certainly be a problem in studies that have a long follow-up period. For example many of the British doctors who were smokers in the 1950s when the study begain, stopped smoking during the study. Also in randomised trials that expect people to take study drugs or maintain a diet every day for a number of years, it can be hard work to keep participants motivated to take all their drugs or maintain their diet.
To help answer this question I have provided a list of topics to consider. I'll let you read through this list yourself, as we will come back to the specific topics in subsequent presentations. If you have problems with any of the definitions, there is an excellent glossary on the Cochrane Collaboration site - just google Cochrane Collaboration glossary if you want a definition of contamination, co- intervention etc.
finally one the best way to minimise differences in the level of compliance/contamination/co-interventions/follow-up is to keep participants and study staff blind to whether the participants are in EG or CG. O T

33. RAMBOMAN O P ‘were outcomes well Measured?’ EG CG T
were they measured Blind to whether participant was in EG or CG ?
EG CG
As you can see, the b in Ramboman gets used twice. In the previous slide I discussed the benefits of blinding participants in relation to Maintenance and here I want comment on the importance of blinding in relation to the second M in RamboMan which stands for Measurement ofoutcomes. Unfortunately many outcome measurements are not clear cut or black and white and one of the ways we can reduce differences in the way outcomes are measured between different groups within the study is to keep the outcome assessors blind to the exp/comp status of the participants.
Let me give you an example of where this may be important., if a study was investigating the effect of a new drug (the E) compared to an old drug (the C) on the outcome depression. Depression is not a clear cut diagnosis and outcome assessors normal score participants on a scale. Now if the outcome assessors are aware that particpants are on the new antidepressent, they may subconsciouly score the particpants lower on the depresession scale because they think the new drug is likely to be more effective and vice versa for the older drug. If this happened then there would be an error favouriing the new drug, keeping the outcome assessors blind to the participants exposure status is a useful way to reduce outcome measurement errors O T

34. RAMBOMAN O P ‘were outcomes well Measured?’ EG CG T
were they measured Objectively?
EG CG O T

35. ‘were the ANalyses done well?’
RAMBOMAN P
‘were the ANalyses done well?’
EGA CGA
If RCT were Intention To Treat (ITT) analyses done?
EGC CGC
This slide covers two other analytical question that are important to consider when critcally apraising a study.
The first question simply asks whether the numbers you would need to calculate EGO and CGO are given in the paper and if so, whether you get a similar answer. In this course we provide you with a excel-based calculator that you can use to do these calculations.
The second question asks whether all the participants were analysed in the groups to which they were initially allocated. I'm going to discuss this further under the topic 'intention-to-treat' analysis in a subequent presentation on appraising randomised controlled trials. a b O T

36. ‘were the ANalyses done well?’
RAMBOMAN P
‘were the ANalyses done well?’
EG CG
adjustment for baseline differences / confounding?
The AN at the end of RamboMAN stands for analyses and there are several analysis questions that are covered in the final 3 slides of this presentation.
The first relates to how the investigators addressed any important differences between the EG and the CG at the beginning /during the study that could have influenced the number of outcomes over and above the specific exposure and comparison factors being investigated.
Differences between EG and CG are far more likely to be present in non randomised studies than in randomised trials. As I mentioned the whole point of randomisation is to start a study with two groups that are as similar as possible, but in non ramdomised studies where participants are allocated by measurement of a specific exposure factor, there are almost certainly going to be other differences between the EG and CG that could influence the outcome.
For example if we did a study of smoking and cancer today we would find that the smokers are also more likely to be exposed to a number of other risk taking behaviours than non smokers because everyone now knows that smoking is a risky behaviour and smokers are more likely to be risk takers (in regards to their health) than non smokers. So if the investigators find that smokers are at increased risk of cancer, they wont know how much is due to the smoking and how much is due to the associated risk taking behaviours which are called confounders if they also cause cancer.
There are a number of ways investigators can address this very important source of error in non randomised epidemiological studies, in particular using multivariate ananalytical tecniques that mathematically attempt to make the EG and CG similar. O T

37. sample from a population
GATE: random error: 2nd formula: random error = 95% confidence interval
sample from a population
In this final slide on the second acronym - Ramboman - I want to introduce the 2nd formula. It is an ANalytical issue but it relates to a different type of error than all the other errors I have discussed so far, and so it has its own formula. I'm pushing the boundaries of the definition of a formula but the second formula is as follows: ' random error equals the 95% confidence interval.' There is a separate presentation in this course about random error and all I want to say at this stage is that every measurement and calculation done in epidemiological studies will be associated with a degree of random error. Increasingly, the standard way of describing the amount of random error in epidemiological measures is using the 95% CI, and every measure of EGO and CGO has a 95% CI, as does every relative risk or risk difference or number needed to treat. I've given a definition of a 95% CI in this slide and will come back to this in a subsequent presentation.
So this concludes my overview of the major sources of error in epidemiological studies that need to be considered when appraising a study
EGO ± 95% CI
CGO ± 95% CI
There is about a 95% chance that the true value in the underlying population lies within the 95% CI (assuming no non-random error)
1 picture, 2 formulas & 3 acronyms

38. 1 picture, 2 formulas & 3 acronyms
GATE: a framework for error in systematic reviews & meta-analyses: 3rd acronym: FAITH
These points are not found in narrative reviews
•Comprehensive list of all studies identified
•Clear presentation of characteristics of each included study
•Clear analysis of methodological quality of each included study
•Comprehensive list of all studies excluded and justification for exclusion
•Clear analysis of results using meta-analysis if appropriate and possible
•Sensitivity analysis of synthesised data
•Structured report stating aims, describing methods and materials
and reporting results
1 picture, 2 formulas & 3 acronyms

39. systematic review: a study of studies
study sources
studies screened
studies appraised & allocated:
included
excluded
studies summarised
& pooled if homogeneous

40. 1 picture, 2 formulas & 3 acronyms
critical appraisal of SR: FAITH
study sources
Find
studies screened
Appraise
studies appraised & allocated:
Include
included
excluded
Total
studies summarised
& pooled if homogeneous
Heterogeneity?
1 picture, 2 formulas & 3 acronyms

41. 4

42. GATE: framework for the 4 steps of EBP
The GATE frame can also be used as a framework for the steps of EBM (aka EBP)

43. Ask Acquire 3. Appraise 4. Apply & Act
the steps of EBP:
Ask
Acquire
3. Appraise
4. Apply & Act
The final step of EBP is what QI is all about

44. EBP Step 1: ASK - turn your question into a focused 5-part PECOT question
1. Participants
2. Exposure
3. Comparison E C
this slide presents the same study as in the previous slide to introduce the first acroynym PECOT which identified the 5 main components of most epi studies.
4. Outcomes
yes O
5. Time no T

45. EBP Step 2: ACQUIRE the evidence – use PECOT to help choose search terms
Participants
Exposure E C
Comparison
Once you have written down your 5-part question - and please dont spend any time trying to make it grammatically correct - you can then use the relevant parts of the question to identify search terms.
You will usually find the most important search terms under participants, exposure and outcome, althpough sometimes the comparison (for example placebo) and the time frame can provide search terms.
There is a separate presentation on searching the literature in this course and I encourage you to view it.
Outcomes
yes O
Time no T

46. EBP Step 3: APPRAISE the evidence – with the picture, acronyms & formulas
Recruitment
Allocation
Maintenance
blind
objective
Measurements
ANalyses E C O
Mention RAMMbo T
Occurrence = outcomes ÷ population
Random error = 95% Confidence Interval

47. APPLY the evidence by AMALGAMATING the relevant information & making an evidence-based decision:’ the X-factor

48. The inspiration for the X was a student who told me ‘all you need now is an ’X’ to go with your triangle, circle and square!”

49. epidemiological evidence patient’s clinical circumstances
X-factor: making evidence-based decisions
epidemiological evidence
economic
person
values & preferences
system features
family
legal
community
political
patient’s clinical circumstances
practitioner
So here is the X which provides a great framework for the 4 components of EBP decisionmaking that I discussed in my introduction to EBP presentation. I call this the X-factor because decisionmakers who can put it all together well are good decisionmaker so have the x-factor and I guess that being able to put all these issues together is a way of defining what expertise is all about
See excellent article: Clinical expertise in the era of evidence-based medicine and patient choice. EBM 2002;736-8 (March/April)
social
physical health
psychological
Practitioner eXpertise: ‘putting it all together’ - the art of practice
Clinical expertise in the era of evidence-based medicine and patient choice. EBM 2002;736-8 (March/April)

50. GATE critically appraised topic (CATs) forms find these at: www. epiq
GATE critically appraised topic (CATs) forms find these at:

51. GATE CAT – 4-sheet workbook (in Excel) sheet 1: GATE-Ask & Acquire

52. GATE CAT – 3-sheet workbook (in Excel)
GATE CAT – 3-sheet workbook (in Excel) sheet 2: GATE-Appraise (with calculator)

53. GATE CAT – 3-sheet workbook (in Excel) sheet 3: GATE-Apply