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Difficulty: MediumHypothesis Testing and Statistical Power

Investigators at an academic medical center are designing a randomized, double-blind, placebo-controlled trial to evaluate a novel Janus kinase (JAK) inhibitor for the treatment of moderate-to-severe plaque psoriasis. The primary endpoint is the absolute change in the Psoriasis Area and Severity Index (PASI) score from baseline to 16 weeks. Preliminary power calculations indicate that the current study design only yields 65% statistical power (β=0.35\beta = 0.35), which is below the conventional 80% threshold. The research team meets to discuss protocol revisions to improve the study's ability to detect a true difference between the treatment and placebo groups.

Which of the following modifications would increase the statistical power of this trial? (Select all that apply)

  1. Expanding the target patient enrollment from 250 to 500 individualsAnswer
  2. Utilizing highly precise digital planimetry rather than subjective physician visual estimates to assess skin lesionsAnswer
  3. C
    Changing the acceptable probability of a Type I error (α\alpha) from 0.05 to 0.01
  4. D
    Redesigning the trial to detect a smaller minimum clinically important difference between the two groups
  5. E
    Selecting a non-parametric statistical test instead of a parametric test to analyze the normally distributed primary outcome data

Answer

The modifications that increase statistical power are expanding the target patient enrollment and utilizing highly precise digital planimetry to assess skin lesions.
Statistical power (1−β1 - \beta) is the probability of detecting a true effect when one exists. It is mathematically increased by expanding the sample size (which reduces standard error) and by decreasing the variance of the measurements (which reduces background noise). Therefore, enrolling more patients and using a more precise measurement tool (digital planimetry over visual estimation) are both correct strategies to increase power.

Step-by-Step Solution

1
Identify the goal of the question
The objective is to select all study design modifications that will increase statistical power (the probability of correctly rejecting a false null hypothesis).
Understanding the components that mathematically derive statistical power is necessary to evaluate each option.
2
Evaluate modifications related to sample size and variance
Increasing sample size and decreasing measurement variance both increase the signal-to-noise ratio in statistical testing, thereby increasing power.
Power is directly proportional to sample size and inversely proportional to the variance of the underlying population.
3
Evaluate modifications related to significance levels, effect size, and test selection
Decreasing alpha, detecting a smaller effect size, and using non-parametric tests on normal data all decrease statistical power.
These changes either raise the threshold for significance, look for harder-to-find differences, or use less efficient mathematical models.

Key Concept

Statistical power (1−β1 - \beta) is increased by larger sample sizes, larger expected effect sizes, higher Type I error tolerance (larger α\alpha), and decreased data variance.
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Topics

Hypothesis Testing and Statistical Power

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