Epidemiology 2200b: Randomized Trials Notes

Randomized Trials Overview

• Epidemiology 2200b by Dr. Joel Gagnier
• Focus Topic: Randomized Trials

Why Use Random Assignment?

• Ensures that treatment groups are equivalent in every aspect except for the treatment itself.
• Reduces bias in treatment assignment, minimizing the influence of factors that could affect the outcome.

Role of the Control Group

• Purpose: To compare the effect of an intervention (e.g., new drug) with a group that does not receive the intervention (control).
• Necessary for determining whether improvements in health are attributable to the intervention or other factors (e.g., natural recovery).

Types of Control Groups

1. Historical Control Group

   • Compares new intervention outcomes with past patient data.
   • Challenges: Changes in healthcare quality and treatment protocols over time; historical data may not be comparable.
   • Example: Insulin treatment for diabetes by Banting et al. in 1922.

2. Concurrent Control Group

   • 2.1 Non-randomized: Selection bias occurs (e.g., physicians choosing healthier patients).
   • 2.2 Alternate Allocation: Assigning subjects alternately can lead to biased treatment groups.
   • 2.3 Randomized Control Group: Ensures subjects are randomly assigned to either intervention or control, preventing bias.

Advantages of Randomization

• Prevents investigator bias in assigning treatments.
• Balances characteristics across control and experimental groups.
• Enables reliable comparison of outcomes.

Randomized Trial Design Issues

• Sample Size and Power: Important for ensuring the study can detect a significant effect if it exists.
• Treatment Effect and Significance Level: Statistical standards for determining the efficacy of treatment.
• Cluster Randomization: When entire groups or clusters are randomized rather than individuals (used when individual randomization is impractical).

Importance of Blinding

• Double Blinding: Both participants and evaluators unaware of group assignments; reduces assessment bias.
• Essential for subjective outcomes (e.g., self-reported pain).
• Less critical for objective outcomes (e.g., survival).

Phase I to Phase IV Drug Testing

1. Phase I: Determining safe dosage and toxicity in a small group.
2. Phase II: Estimating efficacy and further assessing safety in a larger patient group.
3. Phase III: Randomized controlled trials comparing new drugs against placebo or standard treatments.
4. Phase IV: Post-marketing surveillance and long-term effects monitoring.

Statistical Hypotheses in Randomized Trials

• Null Hypothesis (H0): There is no true difference in treatment effects.
• Alternative Hypothesis (Ha): There is a significant difference between treatment groups.
• Importance of maintaining a Type I error (alpha) of 0.05 when conducting tests.

Sample Size Estimation Process

1. Set allowable Type I error (e.g., α=0.05).
2. Anticipate difference in cure rates based on previous studies.
3. Use statistical tools (i.e., sample size tables) to determine necessary samples for the trial.

Challenges in Recruitment and Statistical Power

• Under-recruiting can lead to insufficient power to detect treatment differences.
• External validity may be compromised if sample does not represent the broader population.

Cluster Randomization Trials

• Groups (clusters) are used for random assignment instead of individuals (e.g., schools, communities).
• Outcomes within clusters tend to be correlated, requiring specific statistical methods for analysis.

Case Study: Influenza Vaccination in Hutterite Communities

• Objective: Assess whether vaccinating children prevents influenza in unvaccinated community members.
• Results indicated significant indirect benefits, demonstrating the importance of herd immunity.