Abstract
INTRODUCTION: With the advent of Alzheimer's disease (AD)-modifying and symptomatic treatments of demonstrated efficacy, enrolling participants as concurrent placebo controls in trials can become increasingly difficult. Synthetic controls have been proposed as a viable alternative to concurrent control groups, but their feasibility and reliability remain untested in AD studies. METHODS: I-CONECT trial, which evaluates conversational interactions on cognition, was used to test synthetic control methods. Data from the National Alzheimer's Coordinating Center-Uniform Data Set was used to create synthetic-controls for I-CONECT participants using two methods: 1) case mapping and 2) case modeling. Efficacy estimates were compared between original versus synthetic-controlled trials. RESULTS: In parallel-group designs, treatment effect sizes for the primary outcome were closely aligned between the original trial (β = 1.67) and synthetic control analyses (β = 1.40-1.65). For n-of-1 designs, the two methods showed high agreement in identifying treatment responders (Kappa = 0.75-0.82). DISCUSSION: Synthetic control methods are feasible and reliable to create alternative controls in AD studies. CLINICAL TRIAL REGISTRATION: NCT02871921. HIGHLIGHTS: Synthetic control methods are feasible and suitable for evaluating treatment effects in various trial designs such as n-of-1, single-arm, and parallel groups. Synthetic control methods can help replicate early-phase Alzheimer's trials, informing go/no-go decisions for larger-scale studies. The choice of similarity algorithms is critical as it affects the quality of historical case mapping. The National Alzheimer's Coordinating Center-Uniform Data Set (NACC-UDS) provided an ideal pool for identifying historical cases with similar demographic, biological, and social characteristics to participants in trials, enabling the creation of synthetic control groups for Alzheimer's clinical research.