EVALUATING THE IMPACT OF WEIGHTED SAMPLE SIZE ON MATCHING ADJUSTED INDIRECT TREATMENT COMPARISONS BETWEEN TRIALS WITH TIME-TO-EVENT OUTCOME: A SIMULATION STUDY

Matching-adjusted indirect comparisons (MAICs) are a popular method of population-adjusted indirect treatment comparison used to support health technology assessment submissions. MAICs rely on a propensity score approach that rescales the weight of patients in the index trial to a target population. We sought to explore the impact of approaches used to rescale weights on the results of MAICs.