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Zorluk: ZorStatistical Inference and Hypothesis Testing

A team of dermatological researchers is investigating a novel topical gene therapy for severe atopic dermatitis in a randomized, double-blind, vehicle-controlled clinical trial. The study is appropriately powered at 80%80\% (β=0.20\beta = 0.20) to evaluate the primary endpoint, which is the mean change in the Eczema Area and Severity Index (EASI) score at 12 weeks.

During the design phase, the investigators pre-specify the analysis of 2020 distinct and independent secondary outcomes, including various serological cytokine levels, epidermal barrier function metrics, and sleep disruption indices. The researchers aim to maintain the overall study-wide probability of committing a Type I error at exactly 5%5\% (α=0.05\alpha = 0.05) across the analysis of these secondary outcomes. To counteract the problem of multiple comparisons, the biostatistician applies a Bonferroni correction to adjust the significance level.

Based on this statistical design, what specific pp-value threshold must a single secondary outcome achieve to be considered statistically significant? (Express the answer as a precise decimal).

Cevap: 0.0025

Cevap

The correct adjusted threshold is 0.0025.
The Bonferroni correction is a conservative statistical method used to counteract the problem of multiple comparisons. When multiple independent hypothesis tests are performed simultaneously, the cumulative chance of committing at least one Type I error (a false positive) increases significantly. To maintain a specific family-wise error rate (in this case, α=0.05\alpha = 0.05), the Bonferroni method divides the desired overall α\alpha by the total number of hypotheses being tested in that group (nn). For the 2020 secondary outcomes in this trial, the adjusted pp-value threshold is calculated as 0.05/20=0.00250.05 / 20 = 0.0025. Therefore, any single secondary endpoint must yield a pp-value less than 0.00250.0025 to be considered statistically significant.

Adım Adım Çözüm

1
Identify the desired overall Type I error rate (α\alpha).
α=0.05\alpha = 0.05
The researchers want to cap the study-wide probability of a false positive among the secondary outcomes at 5%5\%.
2
Identify the number of secondary endpoints being tested (nn).
n=20n = 20
The Bonferroni correction requires dividing the overall α\alpha by the number of simultaneous comparisons being made within that family.
3
Calculate the adjusted pp-value threshold.
0.05/20=0.00250.05 / 20 = 0.0025
Applying the Bonferroni formula (αadjusted=α/n\alpha_{adjusted} = \alpha / n) ensures that the cumulative probability of committing at least one Type I error across all tests does not exceed 0.050.05.

Anahtar Kavram

Bonferroni Correction for Multiple Comparisons
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