Epidemiology 2200b by Dr. Joel Gagnier
Focus Topic: Randomized Trials
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.
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).
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.
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.
Prevents investigator bias in assigning treatments.
Balances characteristics across control and experimental groups.
Enables reliable comparison of outcomes.
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).
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: Determining safe dosage and toxicity in a small group.
Phase II: Estimating efficacy and further assessing safety in a larger patient group.
Phase III: Randomized controlled trials comparing new drugs against placebo or standard treatments.
Phase IV: Post-marketing surveillance and long-term effects monitoring.
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.
Set allowable Type I error (e.g., α=0.05).
Anticipate difference in cure rates based on previous studies.
Use statistical tools (i.e., sample size tables) to determine necessary samples for the trial.
Under-recruiting can lead to insufficient power to detect treatment differences.
External validity may be compromised if sample does not represent the broader population.
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.
Objective: Assess whether vaccinating children prevents influenza in unvaccinated community members.
Results indicated significant indirect benefits, demonstrating the importance of herd immunity.
Epidemiology 2200b: Randomized Trials Notes
Historical Control Group
Concurrent Control Group