Speaker
Description
Title:
Evaluating Nonparametric Combination Methods for Aggregating N-of-1 Trials: A Simulation-Based Comparison with Meta-Analysis
Abstract:
Aggregating results from multiple N-of-1 trials has become increasingly relevant for evaluating personalized and digital health interventions, where inter-individual heterogeneity and complex temporal structures challenge traditional study designs. In earlier work, we compared the efficiency of three designs—parallel-group randomized controlled trials (RCTs), two-period crossover trials, and meta-analysis of multiple N-of-1 studies—and found that aggregating individual N-of-1 trials through random-effects meta-analysis can achieve comparable power with substantially smaller sample sizes. However, model-based meta-analytic estimators may be sensitive to violations of normality, time dependence, carryover, or incomplete sequences, which frequently arise in digital health applications.
In this study, we propose a general framework for combining evidence from N-of-1 trials based on the Nonparametric Combination (NPC) methodology. NPC offers a flexible, assumption-light approach that combines p-values from multiple permutation tests without requiring independence or distributional assumptions. We develop a two-level aggregation strategy: (1) at the within-subject level and (2) at the across-subject level.
To assess the methodological properties of NPC aggregation, we design an extensive simulation study reflecting realistic N-of-1 settings with varying intra- and inter-subject variability, AR(1) autocorrelation, carryover effects, non-Gaussian errors, and missingness. Scenarios are aligned with those used in our previous comparative work, enabling direct evaluation of meta-analysis versus NPC under matched conditions. Performance metrics include type I error control, power, bias and coverage for the overall effect estimate, robustness to misspecification, and computational cost.
Simulation studies are currently ongoing, and results will be presented at the conference. Preliminary investigations suggest that NPC may offer improved robustness in heterogeneous or highly autocorrelated settings, while retaining competitive power relative to random-effects meta-analysis.
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