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Difficulty: MediumHypothesis Testing and Statistical Power

A multidisciplinary research team at a regional veterans hospital is investigating a novel care-transition protocol for patients discharged with congestive heart failure. The primary outcome is the change in the Kansas City Cardiomyopathy Questionnaire (KCCQ) clinical summary score at 90 days. During the planning phase, a biostatistician determines that the current study protocol only has a 60%60\% probability of detecting a true difference between the intervention and usual care groups. The team needs to modify the study design to reach at least an 80%80\% probability of detecting this difference.

Which of the following modifications would independently help achieve this goal? (Select all that apply)

  1. Recruiting additional study sites to increase the total number of enrolled participants.Answer
  2. Using an automated electronic scoring system that significantly reduces inter-rater measurement variability.Answer
  3. C
    Lowering the accepted Type I error threshold (α\alpha) from 0.050.05 to 0.010.01.
  4. D
    Redesigning the trial to detect a 5-point difference in KCCQ scores rather than the originally planned 10-point difference.
  5. E
    Expanding the enrollment criteria to include patients with any severity of heart failure, thereby increasing the baseline heterogeneity of the cohort.

Answer

Recruiting additional study sites to increase the total number of enrolled participants AND Using an automated electronic scoring system that significantly reduces inter-rater measurement variability.
Statistical power is the probability of correctly rejecting a false null hypothesis. The power of a study is directly proportional to the sample size (nn), the true effect size (Δ\Delta), and the chosen significance level (α\alpha), and it is inversely proportional to the variance in the underlying population or measurement (σ2\sigma^2). Recruiting additional participants increases the sample size, which narrows the standard error and increases power. Similarly, using an automated scoring system reduces measurement error, which decreases overall variance and also increases power.

Step-by-Step Solution

1
Identify the statistical concept described in the clinical vignette.
The "probability of detecting a true difference" represents the statistical power of a study (1−β1 - \beta).
Understanding that the question requires identifying ways to increase statistical power from 60%60\% to 80%80\% is necessary to evaluate the given design modifications.
2
Recall the mathematical determinants of statistical power.
Statistical power increases as sample size (nn) increases, effect size (Δ\Delta) increases, significance level (α\alpha) increases, and population variance (σ2\sigma^2) decreases.
These variables mathematically interact to determine the likelihood of correctly rejecting a false null hypothesis.
3
Evaluate each answer choice against these statistical determinants.
Increasing the number of participants increases nn. Reducing inter-rater measurement variability decreases σ2\sigma^2. Both of these independent modifications will successfully increase the statistical power of the trial.
Matching the proposed clinical modifications to the correct directional changes in the power calculation confirms the correct answers.

Key Concept

Determinants of Statistical Power
Estimated Time:1m 15s
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Topics

Hypothesis Testing and Statistical Power

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