Using Item Response Theory (IRT) to Measure The Primary Clinical Outcome Can Reduce Required Sample Size by Over 50%

Item Response Theory (IRT) enhances clinical outcome measurement by increasing precision and reducing measurement error, thereby significantly lowering required sample sizes compared to Classical Test Theory (CTT). This study employs a Markov simulation analysis to quantify the expected efficiency gains when transitioning from CTT-based assessments to IRT-based adaptive measures.Empirical evidence suggests that IRT reduces standard errors (SE) in clinical outcomes by 20%–40%, translating into proportional reductions in sample size. Our simulation, using a baseline CTT-based sample of N = 500, demonstrates that IRT can achieve sample size reductions of up to 64% in cross-sectional studies and 50% in longitudinal trials while maintaining equivalent statistical power (α = 0.05, power = 0.80). Furthermore, computerized adaptive testing (CAT)—which dynamically selects test items based on individual responses—further enhances efficiency, reducing the number of required test items by 30%–50%.The findings have major implications for clinical trial design, offering cost savings through lower participant recruitment and data collection needs. Additionally, IRT reduces patient burden by shortening assessments while improving sensitivity in detecting meaningful clinical change. These advantages make IRT-based methodologies particularly valuable for longitudinal studies, precision medicine, and real-world evidence generation.