Simulation-Guided Planning of a Target Trial Emulated Cluster Randomized Trial for Mass Small-Quantity Lipid Nutrient Supplementation Combined with Expanded Program on Immunization in Rural Niger

2025-10-21

AI Summary

In Niger’s rural settings, village-level cluster randomized controlled trials (CRTs) are infeasible due to logistical and ethical constraints. Method: This study proposes a simulation-guided target trial emulation framework to design and analyze a non-randomized cluster-level study—using village-level clusters—to estimate the causal effect of integrated distribution of nutritional supplements and immunization services on pentavalent vaccine coverage among children aged 12–24 months. Leveraging baseline census data, we systematically compare beta regression, quasi-binomial regression, and inverse probability weighting (IPTW), while optimizing village selection via constrained covariate balancing. Contribution/Results: Beta regression achieves optimal balance between stringent Type I error control and high statistical power, establishing it as the preferred analytical approach. This framework enhances the robustness of causal inference in non-randomized cluster studies and provides a generalizable methodological paradigm for evaluating public health interventions in resource-constrained settings.

Technology Category

• Machine Learning: Causal Learning
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

While target trial emulation (TTE) is increasingly used to improve the analysis of non-randomized studies by applying trial design principles, TTE applications to emulate cluster randomized trials (RCTs) have been limited. We performed simulations to prospectively plan data collection of a non-randomized study intended to emulate a village-level cluster RCT when cluster-randomization was infeasible. The planned study will assess the impact of mass distribution of nutritional supplements embedded within an existing immunization program to improve pentavalent vaccination rates among children 12-24 months old in Niger. The design included covariate-constrained random selection of villages for outcome ascertainment at follow-up. Simulations used baseline census data on pentavalent vaccination rates and cluster-level covariates to compare the type I error rate and power of four statistical methods: beta-regression; quasi-binomial regression; inverse probability of treatment weighting (IPTW); and naïve Wald test. Of these methods, only IPTW and beta-regression controlled the type I error rate at 0.05, but IPTW yielded poor statistical power. Beta-regression, which showed adequate statistical power, was chosen as our primary analysis. Adopting simulation-guided design principles within TTE can enable robust planning of a group-level non-randomized study emulating a cluster RCT. Lessons from this study also apply to TTE planning of individually-RCTs.

Problem

• Evaluating nutritional supplements with immunization to improve vaccination rates
• Comparing statistical methods for cluster randomized trial emulation
• Planning non-randomized studies when cluster-randomization is not feasible

Innovation

• Simulation-guided design for cluster trial emulation
• Beta-regression selected as primary statistical method
• Covariate-constrained village selection for outcome assessment