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Early detection of functional decline in amyotrophic lateral sclerosis (ALS) is critical for timely intervention and efficient clinical trial design. We evaluated speech-derived biomarkers to detect ALS-related functional decline events earlier than traditional clinical measures. Using longitudinal patient data, we applied Kaplan-Meier analysis to estimate time-to-event and derived hazard rates for each measure. Our results demonstrate that speech-based biomarkers identify functional decline faster than conventional ALS Functional Rating Scale - Revised (ALSFRS-R) subscores. Hazard rate modelling enabled the estimation of sample sizes and trial durations required to detect treatment effects. These findings suggest that speech-based measures can accelerate ALS clinical trials by providing sensitive and objective endpoints, supporting more efficient study design and patient stratification.