What scientific inferences can be made with randomized implementation rollout trials.

Area of Science:

• Implementation Science
• Health Services Research
• Clinical Trial Design

Background:

• Randomized rollout trial designs are vital for evaluating evidence-based interventions in real-world settings.
• Diverse research questions necessitate varied trial designs, assignment principles, and statistical models for implementation research.

Purpose of the Study:

• To propose a framework for selecting appropriate randomized rollout trial designs.
• To identify suitable statistical models for analyzing implementation trial data.
• To guide the design and analysis of trials testing one to three implementation strategies.

Main Methods:

• Categorized research questions into three types: single strategy, two-strategy comparison, and three-strategy comparison.
• Identified mixed-effects models suitable for various randomized implementation rollout trial scenarios.
• Discussed design considerations, including assignment methods and statistical modeling for up to three strategies.

Main Results:

• Presented specific designs: Fixed-Length Staggered Rollout Trial Design for sustainment, Stepped Wedge for existing vs. new strategy, and Head-to-Head Rollout for comparing new strategies or testing synergy.
• Introduced a Three-Phase Sequential Rollout Implementation trial design for testing an existing strategy against a new one with a sustainment period.
• Detailed modeling choices, including random effects for site and clustering, and discussed superiority vs. non-inferiority testing.

Conclusions:

• Randomized rollout trial designs offer extensive opportunities for implementation scientists.
• Balancing cohort assignment is crucial before randomizing transition times.
• Mixed-effects models are recommended for testing hypotheses, accounting for site variation and clustering.