Learn-As-you-GO (LAGO) to adapt the intervention in an ongoing trial in the presence of center effects

Oregon Convention Center 

Learn-As-you-GO (LAGO) trials optimize the intervention component composition of a multi-component intervention while the trial is ongoing, and the final analysis uses trial data from all stages. The primary purpose of LAGO adaptations is to avoid failed trials, which is especially important if pre-trial expectations are not met. The second purpose is to optimize the intervention, so that it achieves a pre-specified goal, often while minimizing cost. In LAGO trials, the observations from different trial stages are not independent, because the interventions in later stages depend on previous stages' outcomes. Hence, standard statistical methods cannot be used to prove consistency of the intervention effect estimators. Therefore, in LAGO trials learning is based on summary measures. I will show that with fixed center effects, estimators based on LAGO trial data are consistent and asymptotically normal, and the null hypothesis of no effect of any of the intervention components can be tested using LAGO trial data.I will illustrate LAGO with PULESA, a clinical trial in Uganda aiming to improve blood pressure management in HIV infected patients.