Enhancing Study Design and Analysis of MR Imaging Markers Through Measurement Error Modeling

Measurement error in imaging reduces statistical power and potentially biases parameter estimation, compromising study reliability.

To introduce a dual data collection design (reliability and main datasets) to quantify measurement error and apply regression calibration to correct error-prone imaging markers, thereby improving biomarker-outcome estimation, statistical power, and sample size planning.

Prospective (reliability) and retrospective (regression calibration).

65 healthy volunteers (mean age: 23.2), 60 age and sex matched with 34 epilepsy patients (mean age: 28.7).

3.0 T, MR fingerprinting (MRF) and T1-weighted (T1w) MPRAGE.

Three-dimensional brain scan-rescan data were acquired in 5 volunteers (6 identical acquisitions per volunteer across 3 scanners) to estimate reliability coefficients ( $\lambda$ ) for MRF T1 and T1w signal intensity (SI) mean and standard deviation (SD). These coefficients were applied in regression calibration to correct imaging markers in the epilepsy cohort. Effect sizes for distinguishing lesional from control were compared before and after correction. Simulations evaluated the impact of additive and proportional bias on sample size, statistical power, and association estimates under single and multi-scanner scenarios.

Reliability coefficient, Cohen's d, regression calibration, generalized estimation equations.

MRF T1 markers exhibited higher reliability ( $\lambda$ = 0.887-0.941) than T1w SI markers with site effects ( $\lambda$ = 0.246-0.554). Regression calibration increased effect size more for T1w SI mean (333.22% increase) than for MRF T1 mean (12.57% increase). In multi-site simulations, regression calibration alone achieved unbiased estimate under small site effects (additive and proportional SD ≤ 0.2), whereas under larger site effects (additive SD ≥ 0.5) only the combined regression calibration and Combat produced near-zero bias (-0.024), outperforming naïve analysis (-0.423).

The dual data acquisition design with regression calibration restores attenuated imaging biomarker associations, improves statistical power, and informs sampling requirements, thus enhancing reliability and generalizability in multi-site imaging studies.

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