# Learn-As-you-GO (LAGO): An innovative adaptive design for multi-component intervention studies in cardiology and public health

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $642,594

## Abstract

The use of complex, multi-component interventions (CMCIs) is an increasingly important aspect of
cardiovascular disease prevention, screening, and treatment. For example, this application’s co-Investigator
Longenecker is the principal investigator of a CMCI trial nearing completion aimed at improving blood pressure
control, EXTRA-CVD (XCVD), consisting of 4 components: 1. Nurse-led care coordination, 2. Nurse-managed
medication protocols and adherence counseling 3. Home blood pressure (BP) monitoring, and 4. Electronic
health record (EHR) support tools, to be compared with generic prevention education. Each component has a
“dose”, e.g. number of days/week home BP should be recorded and reported, and number and duration of
adherence counseling sessions. Implementation scientists, such as those conducting this trial, discuss the
tension between fidelity to the original intervention protocol, and the need for tailoring, tweaking and
adaptation, perhaps contextually driven, i.e. varying by facility size, composition of the provider workforce, or
health status of the patient population served. Under our innovative Learn-As-you-GO (LAGO) design, as the
trial evolves, the doses of these intervention components are adapted at pre-specified stages to maximize
cost-effectiveness and reduce the ultimate risk of trial failure by achieving pre-specified statistical power, while
at the same time preserving the nominal size of the hypothesis test for the overall intervention package effect
and attaining a pre-planned study outcome goal, such as attainment of 80% of patients under blood pressure
control. Because each intervention component is associated with different costs and effectiveness, it is difficult
to specify the optimal intervention package, that is, the optimal intervention component “doses” along with the
components themselves, before launching a trial, as standard methods require. LAGO designs for continuous
outcomes, such as changes in blood pressure (mmHg) or cholesterol levels (mg/dL) as in XCVD; for repeated
binary outcomes, such as per visit hypertension control; and which account for clustering of outcome rates
within centers are not available. In this project, following on our 2021 Annals of Statistics publication
establishing fundaments for logistic regression analysis of a single binary outcome in non-clustered data, we
will derive the mathematical theory for LAGO designs for clustered binary and continuous repeated measures
data, and compare the LAGO design to its standard non-adaptive factorial and two-armed alternatives where
the intervention is fixed before the study commences. Once a LAGO study concludes, LAGO provides a
means to design interventions for new centers, scaling up and out, subject to the same class of goals, perhaps
contextually dependent. Methods will be applied to XCVD and PULESA-Uganda, another active CMCI trial led
by co-Investigator Longenecker for which contact PI Spiegelman serves as study statistician, allowing f...

## Key facts

- **NIH application ID:** 10803283
- **Project number:** 1R01HL167936-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Judith Jacqueline Lok
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $642,594
- **Award type:** 1
- **Project period:** 2024-02-15 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803283

## Citation

> US National Institutes of Health, RePORTER application 10803283, Learn-As-you-GO (LAGO): An innovative adaptive design for multi-component intervention studies in cardiology and public health (1R01HL167936-01A1). Retrieved via AI Analytics 2026-06-25 from https://api.ai-analytics.org/grant/nih/10803283. Licensed CC0.

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