Baseline Adjustment and Normalisation in Neurite Outgrowth Assays: A Simulation-Based Comparison of Analytical Methods.

Abstract

Background: When baseline neurite lengths differ between experimental groups, analytical methods may detect, miss or overstate treatment effects. We assessed how commonly used methods recover a baseline-adjusted treatment effect.

New method: We developed a simulation framework comparing endpoint comparison, change score, percentage change, percent-of-control normalisation and ANCOVA. A known 8 μm treatment effect was simulated under varying baseline imbalances and baseline-growth relationships, with additional analyses of sample size and baseline measurement error.

Results: In the worked example, endpoint comparison and percent-of-control normalisation did not recover the true adjusted effect, whereas change score overestimated it by 55%. Across 20 parameter combinations, ANCOVA maintained nominal Type I error (3.8-5.8%) and remained essentially unbiased (mean absolute bias: 0.02 μm) when baseline was measured without error. Small-sample analyses showed that systematic bias persisted while error rates varied with sample size. Substantial baseline measurement error reduced ANCOVA's bias advantage and could inflate its Type I error.

Comparison with existing methods: Endpoint and percent-of-control approaches estimate unadjusted endpoint differences. Change scores can be distorted when baseline predicts subsequent growth, while percentage change additionally introduces denominator-related distortion when baselines differ. ANCOVA directly estimates the treatment effect at a common baseline under its standard assumptions.

Conclusions: When the target is a baseline-adjusted effect and baseline is measured reliably, ANCOVA provides the most appropriate analysis under the conditions examined. Baseline values, the target estimand and assumption checks should be reported explicitly.