How is statistical power used in trial design, and what factors influence the required sample size?

Prepare for the Drug Discovery and Healthcare Systems Test. Access flashcards and multiple-choice questions with hints and explanations. Ace your exam with our comprehensive resources!

Power is the probability that the study will detect a true treatment effect if one exists, i.e., the chance of avoiding a Type II error. In trial design, you set a target power (commonly 80% or 90%) and a significance level, then determine the sample size needed to achieve that power for the assumed effect size and outcome variability. The required sample size increases when the expected effect is smaller, when outcomes are more variable, or when the test is conducted at a stricter alpha level or as a two-sided test rather than one-sided. Imbalanced allocation between groups generally requires more total subjects to attain the same power. If there are planned analyses or multiplicity adjustments, the effective per-test power drops, so more participants may be needed to maintain overall power. Anticipated dropout or non-compliance also reduces the effective sample, so recruitment is often inflated to preserve planned power. Conversely, designs that leverage within-subject measurements or repeated measures can boost power and reduce the needed sample size.