Multi-objective optimization formulation for Alzheimer's disease trial patient selection

Clinical trial recruitment faces critical challenges with screen failure rates exceeding 80% in Alzheimer's disease (AD) trials. Traditional patient selection relies on expert consensus without systematic evaluation of trade-offs between statistical power, recruitment feasibility, safety, and cost. We developed a multi-objective optimization framework to systematically identify optimal eligibility criteria configurations that balance competing objectives in AD clinical trial design.

We implemented the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize patient selection criteria across three objectives: patient identification accuracy (F1 score), recruitment balance, and economic efficiency. The framework utilized National Alzheimer's Coordinating Center data comprising 2,743 participants with comprehensive clinical assessments and cerebrospinal fluid biomarker measurements. We optimized 14 eligibility parameters including age boundaries, cognitive thresholds, biomarker criteria, and comorbidity management policies. Statistical validation employed Monte Carlo simulation with 10,000 iterations, bootstrap analysis, and SHAP interpretability analysis.

Optimization identified 11 Pareto-optimal solutions spanning F1 scores from 0.979 to 0.995 and eligible patient pools from 108 to 327. Compared to standard criteria selecting 101 participants, optimized approaches identified 102 participants with no significant demographic or clinical differences after multiple comparison correction. Monte Carlo simulation revealed mean cost savings of $1,048 per patient (95% CI: -$1,251 to $3,492), with 80.7% probability of positive savings but 19.3% risk of cost increases (SD = $1,208). Cross-validation demonstrated high precision (95.1%) with strategic selectivity (9.4% recall). SHAP analysis identified biomarker requirements as the dominant cost driver. Optimization algorithms converged toward solutions similar to expert-designed criteria, validating both computational and clinical approaches.

Multi-objective optimization provides meaningful but incremental value through systematic validation and probabilistic efficiency enhancement rather than revolutionary transformation. The convergence toward established practice demonstrates that computational approaches serve as sophisticated validation tools that identify concrete yet uncertain efficiency improvements within existing frameworks. The substantial variability in projected outcomes establishes realistic expectations and highlights the importance of site-specific evaluation, particularly regarding recruitment infrastructure quality as the dominant determinant of success. This establishes a mature paradigm for evidence-based trial design optimization that enhances rather than replaces clinical expertise.