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A master protocol of an adaptive platform trial to assess effectiveness of multi-component interventions for linear growth of sub-Saharan African children during complementary feeding period Park, Jay JH

Abstract

Randomized clinical trials (RCT) are an important tool that has led to important reduction of global childhood mortality. Low and middle-income countries (LMICs) still face important challenge in stunting (low height-for-age) that can produce detrimental effects on child’s long-term development. Facing important challenges in stunting Rwanda has adopted stunting prevention with a particular focus on complementary feeding period ([CFP]: 6-24 months) as their national strategic plan. Rapid Pro, a community health workers program that provides routine health and monitoring services from pregnancy to five years of age using SMS, is a unique health system in Rwanda that can be used to improve linear growth for their children. A single overarching master protocol for an adaptive platform trial (APT) that could be embedded into Rapid Pro to determine comparative effectiveness of multi-component interventions on linear growth during CFP was developed. APTs are a new RCT design that allows for evaluation of multiple interventions against a common control using interim evaluation and flexibilities of allowing new interventions to be added during the trial. To inform the trial design, a landscape analysis of master protocols and APTs was done through a systematic literature review (SLR). This showed 83 master protocols, 16 of which were platform trials, that have been mostly conducted in the US (n=44/83) for pharmaceutical development (n=82/83). This was followed by an SLR with network meta-analysis (NMA) of LMIC-based RCTs studying interventions under the domains of micronutrients and food supplements, deworming, maternal education, and water, sanitation, and hygiene aimed to improve linear growth for children during CFP. An NMA of 79 RCTs involving 81,786 children showed largely equivocal results highlighting the need for more investigation with interventions being combined and tested as packages. The results of these findings were then presented to the governmental stakeholders to determine intervention packages to be tested and to inform the APT design. Simulations were performed to design Bayesian early stopping rules that could reduce the expected sample size while keeping type I error rates under 2.5%. The findings support the use of APTs for child health and other key areas in global health research.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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