A0812
Title: Optimal designs for matching adjusted indirect comparison
Authors: Saumen Mandal - University of Manitoba (Canada) [presenting]
Xiang Zheng - University of Manitoba (Canada)
Abstract: As part of a clinical trial, a new treatment is compared with a competitor treatment to determine its effect on the patient. Ideally, the new treatment can be directly compared with the competitor treatment in randomized controlled trials. However, it is difficult to directly compare due to various factors, such as time, price, regulation, and patents. A matching-adjusted indirect comparison method leverages all available data by adjusting average patient characteristics in trials with individual patient data (IPD) to match those reported in the aggregate trials data. As IPD is used to match the pre-defined baseline characteristics, optimal design theory is used, and this is converted into a constrained optimization problem. A Lagrangian method is used to determine the optimal design subject to satisfying the constraints of baseline characteristics. The new methodology is quite flexible and can be applied to different types of constraints. The methodology can be applied to situations where there is a lack of direct comparison. It will also reduce the time and cost of running experiments.
