A clinical research steering committee is reviewing proposed modifications to the design of an ongoing randomized controlled trial evaluating a new medical therapy for heart failure. Match each proposed trial design modification with its primary impact on the study's statistical power or error rates.
- Converting the primary endpoint from a continuous variable (e.g., ejection fraction percentage) to a dichotomous variable (e.g., normal vs. abnormal ejection fraction)Decreases statistical power due to a loss of information and data granularity
- Performing multiple unadjusted interim data analyses for efficacy before the end of the clinical trialInflates the overall probability of rejecting a true null hypothesis (Type I error)
- Targeting a smaller relative risk reduction without increasing the planned target enrollmentIncreases the probability of failing to reject a false null hypothesis (Type II error)
- Modifying inclusion criteria to enroll patients with more advanced disease to increase the baseline event rateEnhances statistical power by increasing the total number of expected primary endpoints
Answer
Dichotomizing endpoints decreases power; unadjusted interim analyses inflate Type I error; targeting a smaller effect size without increasing sample size increases Type II error; and enrolling a higher-risk population increases power.
Each proposed modification alters the mathematical framework of the trial. Dichotomizing continuous data wastes information, lowering power. Repeated testing inflates the chance of a random false positive (Type I error). Attempting to detect a smaller effect without adding subjects reduces power (increasing Type II error). Selecting patients more likely to have the event increases the overall event count, which directly drives statistical power.
Step-by-Step Solution
Key Concept
Understanding how trial design modifications (endpoint selection, interim analyses, effect size estimation, and patient selection) impact Type I error, Type II error, and statistical power.