Examining the Effect of Missing Data and Unmeasured Confounding on External Comparator Studies: Case Studies and Simulations.

ABSTRACT:

Background and objective

Missing data and unmeasured confounding are key challenges for external comparator studies. This work evaluates bias and other performance characteristics depending on missingness and unmeasured confounding by means of two case studies and simulations.

Methods

Two case studies were constructed by taking the treatment arms from two randomised controlled trials and an external real-world data source that exhibited substantial missingness. The indications of the randomised controlled trials were multiple myeloma and metastatic hormone-sensitive prostate cancer. Overall survival was taken as the main endpoint. The effects of missing data and unmeasured confounding were assessed for the case studies by reporting estimated external comparator versus random

SUBMITTER: Rippin G 

PROVIDER: S-EPMC11554740 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature