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Block 2 RCT Wrap-Up - Power (9/2020)

RCT Wrap-up (Block 2) - Power

In this block, we looked at critically appraising RCTs with a special focus on bias & confounding, power, and clinical vs statistical significance. Residents used a variety of well-known trials (ACCORD-BP, SPRINT, ANDROMEDA, and HYVET) to put these concepts to practice. Below is a summary of concepts related to power. 

• The statistical power of an RCT is the ability of the study to detect a difference between the groups when such a difference exists.
  • The power of a study is determined by several factors, including the frequency of the outcome being studied, the magnitude of the effect, the study design, and the sample size.
  • You can think of it as the probability of NOT making a type II error.
• Probability of making a type II error is designated Beta (β). Therefore power is 1 - β. It can also be looked at as the false positives. 
  • Type 1 error - false positives. Represented by alpha (α), which is the p-value below which you reject the null hypothesis. A p-value of 0.05 indicates you're willing to accept a 5% chance that you are wrong when you reject the null hypothesis. 
  • Type 2 error - false negatives. Represented by Beta β
• Power is affected by sample sizes.
  • A large sample size increases statistical power and reduces chance of type II error (False negative)

    • Increase power = decrease in type II error

4 types of comparisons in RCTs that can affect power levels:

1. Superiority trials
   1. To verify that a new treatment is more effective than a standard treatment from a statistical point of view or from a clinical point of view, its corresponding null hypothesis is that: The new treatment is not more efficacious than the control treatment by a statistically/clinically relevant amount. Based on the nature of relevant amount, superiority design contains statistical superiority trials and clinical superiority trials.
2. Equivalence trials
   1. The objective of this design is to ascertain that the new treatment and standard treatment are equally effective. The null hypothesis of that is: Both two treatments differ by a clinically relevant amount.
3. Non-inferiority trials
   1. Non-inferiority trials are conducted to show that the new treatment is as effective but need not superior when compared to the standard treatment. The corresponding null hypothesis is: The new treatment is inferior to the control treatment by a clinically relevant amount.
4. One-sided test is performed in both superiority and non-inferiority trials, and two-sided test is used in equivalence trials. The hypothesis testing of different design is summarized in Table 1.