53 - Clinical Trials in CVD

Question 1

Why are clinical trials considered best to study causation?

Answer 1

Active change of exposure status.

Tightly-controlled study environment

Question 2

Relative measures of intervention effect
1)
2)

Answer 2

1) Relative risks

2) Hazard ratios

Question 3

Absolute measures of intervention effect
1)
2)

Answer 3

1) Absolute risk/rate reduction

2) Number needed to treat

Question 4

Way to deal with confounders in a clinical trial

Answer 4

Randomisation

Question 5

Way to deal with information bias

Answer 5

Blinding (single- or double-blind).

Reduces overseer bias.

Question 6

Source of significant selection bias in clinical trials

Answer 6

Cross-over.
EG: sick patients drop out because they aren’t getting batter. Healthy patients stay in, so data appear to suggest greater effect of intervention.

Question 7

Intention-to-treat analysis

Answer 7

A method of analysis for randomised trials in which all patients randomly assigned to one of the treatments are analysed together, regardless of whether or not they completed or received that treatment.

A way to minimise impact of cross-over

Question 8

Difference between hazard ratio and relative risk

Answer 8

Hazard ratio applies to entire period of follow-up, is a weighted average of all measurements.
Relative risk is a risk at one point in time.

Question 9

Survival analysis
1)
2)
3)

Answer 9

1) During follow up, note when occurrence of interest happens. Measure of interest is ‘time to event.’
2) Construct a Kaplan-Meier curve, which plots hazard or survival (1-hazard) vs time.
3) ‘Survival’ is avoidance of event of interest, not death.

Question 10

Hazard

Answer 10

Continuously-updated ratio of event/no event

Question 11

Hazard ratio

Answer 11

Hazard intervention:hazard control ratio.
EG: HR=0.5 means that at any point in time in follow up probability of outcome in intervention group is half that of in the control group

Question 12

Risk/rate reduction

Answer 12

Change of outcome with exposure expressed in relative and absolute terms.

EG: Rate of outcome in control = 10/100
Rate of outcome in intervention = 7/100
Rate ratio = (7/100)/(10/100)=0.7
Absolute reduction = 10/100 - 7/100 = 3/100

Question 13

Number needed to treat

Answer 13

Number of people that need to undergo treatment to prevent outcome in one.

Calculated by 1/(absolute risk or rate reduction)
EG: if absolute risk reduction is 3/100 per year, then 1/(3/100)=33.3. So 33.3 need to be treater/year to avert one case of outcome.

Question 14

Big determinant of number needed to treat

Answer 14

How common condition is. If condition is rare, then a lot of people will need to be treated.

Question 15

Name for NNT when intervention increases risk of developing outcome

Answer 15

Number needed to harm