Evidence Based Medicine and the Role of Chance

Question 1

Explain what is meant by hypothesis testing in stats

Answer 1

• Used to answer a research questions
  
  • E.g. “Does additionally treating patients admitted to hospital with suspected MI with clopidogrel improve long term outcomes?”
• Scenario created that you don’t necessarily believe and you try to disprove it (Hypothetico-deductive method)
  
  • Null hypothesis (H0) = No effect of clopidogrel on outcomes
• If enough evidence is gathered to reject H0, we have grounds to favour our alternative hypothesis (often the research question)
  
  • Alternative (H1) = Clopidogrel improves outcomes (1-sided)
  • Alternative (H1) = Clopidogrel has an effect on outcomes (2-sided)

Question 2

What is the p value?

Answer 2

The probability, given that the null hypothesis is true, of obtaining data as extreme or more extreme than that observed

It is the result of a statistical test e.g. chi-square, t-test

The lower the p-value, the greater the statistical significance of the observed difference, meaning you reject the null hypothesis

Question 3

Describe type 1 and type 2 errors

Answer 3

Type 1 error - Both null hypothesis true and reject the null hypothesis are concluded

Type 2 - Both null hypothesis false and fail to reject null hypothesis are concluded

Question 4

How can P-values be interpreted?

Answer 4

• 95% CI contains 1 - not enough evidence to reject H0, if doesn’t contain 1, evidence to reject H0
• 95% CI contain 0 - not enough evidence to reject H0, if doesn’t contain 0, evidence to reject H0

If p≥ 0.05 - Not enough evidence to reject H0

If P < 0.05 - Statistically significant

Question 5

Describe independent vs. paired data

Answer 5

Independent - 2 independent groups, interested in b/w group differences, no worry about variation b/w individuals in diff groups

Paired - Measured on same individuals, can provide more precise estimates of treatment effects

Question 6

Describe statistical correlation

Answer 6

• Statistical measure of (linear) relationship b/w 2 variables
  
  • Lies b/w -1 to 1
  • 0 implies no correlation
• Paired data often shows strong positive correlation

Question 7

What’s the difference between parallel and crossover clinical trials?

Answer 7

Parallel - One group receives treatment, other is control (receive placebo) and then the outcomes are measured

Crossover - Both groups will receive the treatment and placebo, and then the outcomes are measured. This focuses on short-term outcomes that are measurable in each period. Outcomes can then be compared within individuals

Question 8

Describe why crossover trials may be used

Answer 8

• Only suitable when treatment effects are short-lived and reversible
  
  • Requires washout periods b/w treatment periods help prevent ‘carry-over’ effects
  • Usually longer in duration vs. parallel
• Has some advantages over parallel
  
  • Smaller number subjects needed
  • Removes biological and methodological variations (as subjects act as their own control)
• Can be more than 2 treatments + 1 ‘crossover’