Clinical Trials in CVD

Question 1

What is the power of clinical trials?

Answer 1

ability to control bias and confounding

Question 2

What are clinical trials?

Answer 2

• longitudinal
• designed to assess if an intervention (removal of exposure) changes the incidence of an outcome
  
  • most are expected to decrease incidence
• most involve a control group for comparison
• prospective follow-up to capture outcomes

Question 3

What does evidence from clinical trials provide?

Answer 3

• the ‘gold standard’ for causality by:
  
  • actively changing the exposure status to see if incidence of disease or outcome of interest is reduced relative to a comparator
  • in a tightly controled study environment to minimize confounding and bias
• they provide most of the evidence for EBP

Question 4

What relative measures of intervention effect are used in CTs?

Answer 4

• relative risk
  
  • comparison of the incidence measures between the intervention and control groups
• hazard ratios
  
  • interpreted similarly to relative risk

Question 5

What absolute measures of intervention effect are used in CTs?

Answer 5

• absolute risk/rate reduction
• number needed to treat

Question 6

What are the key outcomes of CTs?

Answer 6

• relative:
  
  • relative risk
  • hazard ratio
• absolute:
  
  • absolute risk/rate reduction
  • number needed to treat
• survival analysis (explicit consideration of time-to-event)

Question 7

Randomization deals with

Answer 7

confounding

Question 8

Confounding is

Answer 8

a mechanism through which distortion of truth can occur

Question 9

Masking or blinding deals with

Answer 9

information bias

Question 10

How is information bias dealt with?

Answer 10

• blinding/masking subjects and/or investigators
• blinded/masked committee who decides if an outcome has occured based on pre-described, objective criteria to reduce subjectivity

Question 11

What is intention-to-treat analysis?

Answer 11

• deals with selection bias
  
  • those who drop out are almost always systematically different from those who don’t
• ignores drop-out and crossover when analyzing subject data
  
  • ie treating subject data as they were intended to be treated, not the group they crossed over to
• underestimates treatment effect (conservative estimate) because the groups have become more similar

Question 12

What is survival analysis?

Answer 12

• during follow-up, explicit capture of outcomes and their time of occurence
• measured as time to event
• plotted on a Kaplan-Meier curve, a plot of hazard or survival (1-hazard) over time
• survival = avoidance of event (not necessarily death)
• can note from KM curves whether incidence of a disease is different in treatment vs control, and when that difference occurs for differen tdiseases (circles)

Question 13

What is hazard ratio?

Answer 13

• H_intervention:H_control
• conceptually similar to relative risk, interpreted similarly
  
  • but relative risk applies only at a specific time period
• outcome of survival analysis
• applies to the whole period of follow up
  
  • ​a weighted average of the whole period of follow up
• example:
  
  • if HR = 0.5, then at any given point in time within the period of follow-up, probability of an outcome in the intervention group is half that of the control group

Question 14

What is the interpretation of the HR for CHD and stroke?

Answer 14

• HR for CHD is 1.29
  
  • HRT compared to placebo leads to a 29% increased risk (1.29x the probability of CHD over the period of follow up)
• HR for stroke is 1.41
  
  • HRT compared to placebo leads to a 41% increased risk (1.41x the probability of stroke over the period of follow up)

Question 15

How is relative risk calculated?

Answer 15

• rate of outcome (/py) in intervention divided by rate of outcome in control e.g.:
  
  • if control is 10/100py and intervention is 7/100py
    
    • RR = 7/10 = 0.7
  • this equates to a 30% relative reduction in likelihood of the outcome conferred by the intervention

Question 16

How is absolute risk/rate reduction calculated?

Answer 16

rate of outcome in control arm e.g. 10/100py minus rate of outcome in intervention arm e.g. 7/100py

ARR = 10/100py - 7/100py = 3/100py

Question 17

How is number needed to treat calculated?

Answer 17

NNT = 1/ARR (absolute risk/rate reduction)

e.g. if ARR = 3/100py, NNT = 100/3 = 33.3 people per year

must be reported with time reference

Question 18

What influences the number needed to treat?

Answer 18

• relative effect (often constant)
• underlying likelihood of the outcome
  
  • e.g. if likelihood of outcome is 10% less, NNT needs to be 10x more:
    
    • ARR = 3/100py, NNT = 33.3
    • ARR = 3/1000py, NNT = 333

Question 19

What is the number needed to harm?

Answer 19

• measure of what interventions increase risk/rate of outcome
• number of people who would need to undergo an intervention for one of them to be harmed from it
• e.g. if rate of outcome in control is 10/100py, and rate of outcome in intervention is 14/100py (ie higher):
  
  • RR = 14/100py / 10/100py = 1.4 (same as 0.7 benefit)
  • ARR = 10/100py - 14/100py = -4/100py
  • tf NNT = -25; negative implies NNH
  • tf NNH = 25