Stats

Question 1

General structure for critical appraisal

Answer 1

Question + relevance

Population - characteristics, how many, how were they recruited
Intervention
Control - what is the comparison - usual care? placebo?
Outcome - primary vs secondary outcomes?

Validity - internal and external
Ethics - approval, Helsinki, four pillars?
Funding - who funded it?

Conclude - question, strengths, weaknesses, key outcomes, implications

Question 2

Answer to does this change practice?

Answer 2

Likely no - this is one single study, thinking about hierarchy of evidence you need systematic review/meta analysis

Question 3

What’s research equipoise?

Answer 3

Genuine uncertainty about the therapeutic merits of each arm

Question 4

What is the p value

Answer 4

Probability that an outcome could have happened by chance - p<0.05 is significant, p<0.01 is highly significant

Question 5

Confidence interval definition?

Answer 5

There is an XX% chance that true value lies within the interval

Question 6

Types of bias

Answer 6

Selection bias - how patients were chosen
Performance bias - if patients’ performance was influenced/could influence results
Observational bias - if researcher’s observation was influence/could influence results
Attrittion bias - patients leaving the trial unequally
Confounding - other factors

Question 7

Ways to mitigate bias

Answer 7

Randomisation, multi-centre
Blinding
Intention to treat vs per protocol analysis
Confounding - randomisation or matching for equal distribution of confounders, stratify by confounders, multivariate analysis

Question 8

Intention to treat vs per protocol

Answer 8

ITT - maintains effect of randomisation, reduces risk of selection bias, more representative of real life
Per protocol - shows whether the intervention was effective in those who fully adhered

Question 9

How to calculate 95% CI

Answer 9

= sample mean +- 1.96 x standard error

Question 10

incidence vs prevalence

Answer 10

Incidence - new cases in specified time period, prevalence - proportion of population who have illness

Question 11

Absolute risk

Answer 11

Number of events in group/number of people in that group

Question 12

Relative risk

Answer 12

Incidence in treatment / incidence in control

Question 13

Absolute risk reduction

Answer 13

Risk in control - risk in treat

Question 14

Number needed to treat

Answer 14

1/absolute risk reduction

Question 15

Odds ratio (used in case control)

Answer 15

Odds of the exposure amongst cases / odds of the exposure amongst controls

Question 16

Hazard ratio

Answer 16

Risk of outcome in exposed group/ risk of outcome in non-exposed group

Question 17

Type 1 error vs Type 2 error

Answer 17

Type 1 - false positive ; type 2 - false negative

Question 18

How to avoid a type 1 error

Answer 18

Increase sample size. Reduce p value to 0.01 and significance level (CI)

Question 19

How to avoid a type 2 error

Answer 19

Increase sample size. Increase p value and significance level

Question 20

What does a bigger box on a forest plot represent

Answer 20

Study with more weight in the meta analysis - could be because more statistical power, larger sample size

Question 21

what does a diamond represent on a forest plot

Answer 21

the combined results of the trial

Question 22

Sensitivity

Answer 22

How many of those who actually have the disease will test positive

Question 23

Specificity

Answer 23

How many of those who don’t have the disease will test negative

Question 24

T-test

Answer 24

Comparing differences between 2 groups

Question 25

Chi squared

Answer 25

Measure of the difference between observed and expected frequencies

Question 26

How many observations will be within 1 SD

Answer 26

68%

Question 27

How many observations will be within 2 SDs

Answer 27

95%

Question 28

Mann Whitney

Answer 28

Like t-test - testing for differences between groups but when we can’t assume normal distribution

Question 29

Positive predictive value

Answer 29

probability that following a positive test result, that individual will truly have that specific disease.

Question 30

Negative predictive value

Answer 30

probability that following a negative test result, that individual will truly not have that specific disease.

Question 31

what does spearman’s rank tell you

Answer 31

Measure of correlation between 2 variables +ve or -ve

Question 32

What does a Kaplan-Meier show

Answer 32

Cumulative survival probabilities - steeper slope=higher death rate ie. worse survival prognosis