1. …it’s really interestingPP
…it’s really interesting

2. Things to try and cover Vital statistics Confidence Intervals
Rates and Ratios
OR vs RR
Specificity, sensitivity, PPV, NPV
Extracting info out of flow charts to make calculations
Apply to some relevant questions
Other things worth having a look at

3. Vital statistics P value: probability of event occurring by chance
Null hypothesis – no significant difference
Chi-sq: Hypothesis test for categorical data (O/E)
Type I error ~ False positive ~ alpha value (p value)
↑p value will ↑false positive
Type II error ~ False negative ~ beta value
Statistical power = 1- Beta
Sample size is key
Power detecting what you want to detect
↑sample size = ↑Power

4. Confidence intervals
Confidence Interval: a range which is likely to contain the popn mean
Usually 95% CI quoted i.e. 95% of time the mean will be in this range
Does not denote significance unlike a p value
Related to sample size: ↑sample size equates to narrow CI

5. Options:
a) % confidence interval
b) statistical measure of dispersion of continuous data
c) hypothesis test for categorical data
d) statistical power- ability of study to find what you want to find
e) type I error
f) type II error
g) statistical significance level
This is:
the failure to detect a true difference
the Chi-squared test
3. the statistical assessment of a statement formulated in the negative, such as “there is no association between osteoarthritis and nutrition”
4. illustrated by looking at the difference between the 5th and the 95th centile of a set of blood pressure measurements in millimetres of mercury
a measure of the precision of an estimated proportion of patients on an orthopaedic waiting list who are likely to be over 65 years old f) c) c) b) a)

6. Rates and raw numbers Learn these- one of the favoured questions
Most on the hand out
Fertility and death rates
Will always be over a specified time period
Often specified per 1000 population
Rates will be over time / mention proportion
Raw numbers if asking for things to make a rate i.e. if ask for the numerator / denominator
Almost certainly ask what represents numerator or denominator
Remember: Numerator
Denominator

7. Options:
For a given geographic area:
a) number of women at a specified time
b) number of livebirths in specified time period
c) number of stillbirths in specified time period
d) number of deaths in the first month of life in specified time period
e) average number of women aged years in specified time period (mid-year estimate)
f) number of deaths in the first year of life in specified time period
g) total number of births (live and still) in specified time period
This is the:
denominator for general fertility rate
Things you know:
It’s something to do with birth i.e. not death! Exclude d) f)
It’s general (all births regardless of outcome). Exclude b) c)
It’s the denominator so should be lower portion of equation
It’s regarding fertility (always women aged years). Exclude a)
What’s left? E) & G)
Does G fit the definition? No as it’s the numerator
Does E fit the definition? Yes
General fertility rate = no. live and still births in a time period
number of women 15-44yrs in spec time

8. Options:
For a given geographic area:
a) number of women at a specified time
b) number of livebirths in specified time period
c) number of stillbirths in specified time period
d) number of deaths in the first month of life in specified time period
e) average number of women aged years in specified time period (mid-year estimate)
f) number of deaths in the first year of life in specified time period
g) total number of births (live and still) in specified time period
This is the:
denominator for infant mortality rate
Things you know:
It’s to do with infants. Exclude all options with women i.e. a) e)
Infant deaths: first year of life. Exclude c) d)
It’s a denominator so can’t be what’s defined in the question i.e. number of infant deaths. Exclude F)
What’s left? B) & G)
Does G fit the definition? No
Does B fit the definition? Yes(must know the definition here)
Get to a 1in2 chance rather than 1in7 by using these approaches

9. Options:
For a given geographic area:
a) number of women at a specified time
b) number of livebirths in specified time period
c) number of stillbirths in specified time period
d) number of deaths in the first month of life in specified time period
e) average number of women aged years in specified time period (mid-year estimate)
f) number of deaths in the first year of life in specified time period
g) total number of births (live and still) in specified time period
This is the:
numerator for infant mortality rate
Use previous workings and apply to this
Previously got to B), F) and G)
It’s a numerator so will be what’s defined in the question i.e. number of infant deaths. Select F)

10. Study design (observation to association to causality)
Descriptive (hypothesis generating)
Case report (single event / single patient
Case series (collection of patients)
Cross-sectional (survey / population studies)
Correlation (aka ecological)
Analytical (Observational looking at hypothesis)
Case-control (Disease / no disease with exposures)
Cohort (follow up over time)
Meta-analysis: Combine results of many studies
Experimental (test hypothesis)
RCT (Gold standard vs new intervention)

11. Relate to Bradford-Hill criteria (causality)
Temporal relationship – Does the cause precede the effect
Plausibility – Is the association consistent with other knowledge
Consistency – Have similar results been shown in other studies
Strength – What is the strength of the association between the cause and the effect (relative risk)
Dose-response relationship – Is increased exposure to the possible cause associated with increased effect
Reversibility - Does the removal of a possible cause lead to reduction of disease risk
Study design – Is the evidence based on a strong study design
Judging the evidence – How many lines of evidence lead to the conclusion

12. Options:
a) undertaking record linkage: bringing two pieces of information together
b) population screening: opportunistic / targeting high risk pops
c) Surveillance: regular / continuous
d) undertaking a case-control study
e) matching: e.g. making sure that two groups in case control are as similar apart form disease
f) undertaking a service evaluation study
g) reporting a case study
This would be illustrated by:
estimating point prevalence regularly for disease control purposes
publishing about an episode of adverse reaction to a new oral contraceptive
checking regularly the weight of pregnant women
offering a test for Down’s syndrome to all older pregnant women
checking childhood immunization uptake using a continuous system
  checking the blood pressure of all women of child-bearing age whenever they consult a general practitioner c) g) c) b) c) b)

13. Approaches for minimising bias
Confounder: factor related to both exposure and outcome- not an approach but important to identify so that you can adjust
Adjustment: statistical approach to minimise population differences
Matching: try and make cases and controls as similar by demographics e.g. age, sex, ethnicity Stratification: sub-sections of populations (homogenous groups)
Randomisation: Used in clinical trials
Standardisation: used commonly to account for confounding effect of age

14. Standardisation

15. Direct Standardisation
Standard population structure
Direct method of standardisation - calculation of the number of expected deaths for countries A and B applied to a standard population.

16. Indirect Standardisation
Indirect method of standardisation is used to calculate how many deaths would be expected in Country B if it had the same age-specific mortality rates as Country A.

17. Direct Vs Indirect Standardisation
Break down age spec groups in both pops into mortality rates
Apply rate to standard population numbers given
Standard mortality rate
Number of deaths / 1000 if were in standardised population structure
DIES
Death rate
Index population
Exposure
Sample populations
Comparative Mortality Ratio =Pop A
Pop B
Can compare age-adjusted rates (but not crude rate)
Pop A has x% mortality > Pop B
Used when age spec mortality rate unavailable (more realistic)
Set of age spec rate from standard population to unkn population
Get a estimated death / morbidity rate for each age group
Add altogether to have total estimated deaths
Compare this to what observed
Observed number of deaths
Expected number of deaths
Standardised Mortality Ratio (SMR)
SMR = 100 No deviation
SMR < 100 Less comparative deaths
SMR > 100 More comparative deaths

18. OR Vs RR Odds is number of times event happens vs not
Odds ratio = Odds of exposure in cases
Odds of exposure in controls
OR used in case control as retrospective selection based on outcome not exposure
In a way you can calculate the probability of exposure given outcome / status
Risk is probability an event will occur
Absolute risk: probability that an event will occur in spec time
Attributable risk is the excess risk due to the risk factor
Relative risk (RR) = Risk in exposed group
Risk in unexposed group
RR used in cohort as prospective selection and outcome observed with regards to exposures
Can estimate probability of outcome from exposure

19. Odds ratio: It’s comparing outcome in exposure groups
Exposed to risk factor
(e.g. Morbid obesity)
BMI>40
BMI<40
Total
Disease status
T2DM A 15 B 6
A+B 21
Control C 4 D 18
C+D 22
A+C 19
B+D 24
Odds ratio: odds of T2DM if morbid obese (exposure) / odds of T2DM if not (non exposure)
Odds of T2DM if morbidly obese = A/C
Odds of T2DM if not morbidly obese = B/D
Odds ratio = (A/C)/(B/D) = AD/BC
OR=1: no difference
OR>1: T2DM more likely in exposed group
OR<1: T2DM less likely in exposed group

20. Confidence intervals and ORs
If 95%CI intersects 1 then it is not significant
If 95%CI of two groups overlap they are not significant

21. Relative risk: Risk of disease given exposure
Exposed to risk factor
(e.g. Morbid obesity)
BMI>40
BMI<40
Total
Disease status
T2DM A 15 B 6
A+B 21
Control C 4 D 18
C+D 22
A+C 19
B+D 24
RR: Risk of T2DM in BMI>40 (exposure) / Risk of T2DM in BMI<40 (non exposure)
Risk of T2DM in BMI>40 (exposure) = A/A+C
Risk of T2DM in BMI<40 (non exposure) = B/B+D
RR = (A/A+C)/(B/B+D)
RR=1: no difference
RR>1: Obesity increases T2DM risk
RR<1: Obesity reduces T2DM risk

22. OR != RR Both measure likelihood of event between groups
Very similar in low prevalence but as it increases they are not
A 2% mortality Vs B 1% mortality
RR of A = 2 (what most people would deduce)
OR = 2.02 i.e. A (2/98)/(1/99)
Most common diseases reflect this
A 50% mortality Vs B 25% mortality
RR of A= 2 (what most people would deduce)
OR = 3 because its always chance of getting versus not in each group i.e. A (50/50)/(25/75)=1/0.333=3
A 90% mortality Vs B 10% mortality
RR of A = 9 (what most people would deduce)
OR = 81 because its always chance of getting versus not in each group i.e. A (90/10)/(10/90)

23. Absolute risk reduction and NNT
Absolute risk reduction (ARR) = risk(non-exposure) – risk(exposure)
Number needed to treat (NNT)
average number of patients who need to be treated to prevent one additional bad outcome
effectiveness of a health-care intervention
NNT = 1 / ARR
*Remember dealing with patient numbers so round up
NNT=1 everyone treated recovers
↑NNT = ↑poor intervention i.e need to treat lots of people to gain one recovery

24. ARR & NNT
ARR = 14-8 = 5 (0.05)
Therefore NNT = 1/0.05 = 20

25. a) sample size calculation
b) calculation of odds
c) calculation of a relative risk
d) surveillance
e) calculation of an odds ratio
f) minimizing bias in a cohort study
g) matching
This is:
illustrated by comparing incidence between exposed and non-exposed groups
illustrated by a school health service implementing an ongoing system of hearing tests at school entry
illustrated by focusing on ‘loss to follow-up’
a way of adjusting for confounders
crucial for deciding whether a study has the power to detect a particular difference c) d) f) g) a)

26. Sensitivity, specificity, PPV, NPV
True positive: Correctly identified as +ve
False positive: Identified as +ve but actually -ve
True negatives: Correctly identified as -ve
False negatives: Identified as -ve but actually are +ve
Sensitivity: correctly identifying true positives
Specificity: correctly identifying true negatives
Relevant for population screening etc
Don’t want a test that either does not pick up disease or gives people reason to worry for no reason

27. Sensitivity, specificity, PPV, NPV
Disease / Outcome
DVT
No DVT
Total
Test
+ve
True Positive A
False Positive B
PPV
A/A+B
-ve
False Negative C
True Negative D
NPV
D/C+D
Sensitivity A/A+C
Specificity B/B+D

28. Sensitivity aka True positive rate
Disease / Outcome
DVT
No DVT
Total
test
+ve
True positive A
-ve
False Negative C
Sensitivity A/A+C
aka True positive rate
Measures those correctly identified as positive
e.g., the percentage of sick people
who are correctly identified as having the condition
Quantifies avoiding false negatives
Sensitivity 100%: all DVTs identified as DVTs
A negative result in a test with high sensitivity is useful for ruling out disease (SnOUT)
A positive result in a test with high sensitivity is not useful for ruling in disease
Overdiagnosis

29. Specificity aka True negative rate
Disease / Outcome
DVT
No DVT
Total
Test
+ve
False Positive B
-ve
True Negative D
Specificity B/B+D
Specificity
aka True negative rate
Measures those correctly identified as negative
e.g., the percentage of healthy people who are correctly identified as having the condition
Quantifies avoiding false positives
Specificity 100% - all non-DVTs are identified
A positive result in a test with high specificity is useful for ruling in disease (SpIN)
A negative result in a test with high specificity is not useful for ruling out disease
Underdiagnosis
Does not take into account false negatives

30. PPV and NPV PPV & NPV dependent on prevalence
describe performance of diagnostic test
Sensitivity and specificity are not
PPV = TP / (TP + FP)
will increase proportionally with prevalence
If we test in a high prevalence setting, it is more likely positive test truly have disease vs low prevalence population
NPV = TN / (FN + TN)
will decrease with increased prevalence
↑prevalence ~ ↑PPV
↑prevalence ~ ↓NPV

32. Disease / Outcome
DVT
No DVT
Test
+ve 67
(52+25)
-ve
True +ve: VTE predicted by tests and VTE found= 67
25 (D-dimer +ve) + 52 (Wells score >4)

33. Disease / Outcome
DVT
No DVT
Test
+ve 67
(52+25)
-ve 4
False –ve: VTE not predicted by test but VTE found = 4
4 subjects with VTE but had Wells score ≤4 & D-dimer –ve

34. Disease / Outcome
DVT
No DVT
Test
+ve 67
(52+25)
-ve 4
268
(272-4)
True –ve: VTE not predicted and not found = 268
272 (Wells score ≤4 & D-dimer –ve ) – 4 (VTE found)

35. Disease / Outcome
DVT
No DVT
Test
+ve 67
(52+25)
249
( )
-ve 4
268
(272-4)
False +ve: VTE predicted but VTE not found = 249
124 (Wells score >4 but no VTE) = 176 (Wells score >4) – 52 (VTE found)
125 (D-dimer +ve but no VTE) = 150 (D-dimer +ve) – 25 (VTE found)
Total =

36. Sensitivity = TP / All with DVT (TP+FN) = 67/71 (94%)
Disease / Outcome
DVT
No DVT
Total
Test
+ve
67 (TP) A
249 (FP) B
PPV
A/A+B
-ve
4 (FN) C
268 (TN) D
NPV
D/C+D
Sensitivity A/A+C
Specificity B/B+D
Sensitivity = TP / All with DVT (TP+FN) = 67/ (94%)
Specificity = TN / All without DVT (TN+FP)= 268/ (52%)
Positive predictive value = TP / All that were positive (TP+FP) = 67/316
Negative predictive value = TN / All that were positive (TN+FN) = 268/272

37. Other things to cover / consider
Look up options of answers on vital – very similar questions often used
Definitions / explanations on health knowledge website
Watch out for double negative questions (especially in null hypothesis based questions)
Have a look at immunisation schedule (make sure updated version)
Normal distribution, mean, median mode, range
Intention-to-treat (include in analysis of clinical trial)
3Es 3As
Look up different types of bias
Favourites are lead-time / length-time
Primary, secondary and tertiary prevention
Screening is secondary (as they already have it – picking up disease already there)
Screening diabetics for retinopathy is tertiary (they already have DM and minimising impact of complications)
Health economics (on hand out)

38. Thanks hlrsmit4@liv.ac.uk