Calculating Follow-Up Completeness: a comparison of multiple methods under different simulated scenarios and a use-case.

Abstract

Background: Completeness of follow-up is a crucial aspect of data quality in cohort studies and clinical trials. This study aims to provide an overview of different methods to calculate follow-up completeness. Additionally, the performance of these methods is tested in several scenarios using simulated datasets and a use-case, with the aim of guiding researchers in selecting the most appropriate method for their data.

Methods: The literature was searched for methods of quantification of follow-up completeness. These methods were investigated in simulated datasets, in which the true completeness of follow-up was known. A total of 27 different scenarios were investigated, based on different survival distributions, total proportions of drop-out of participants and different time points of drop-out. The methods were also investigated using real-world mortality data from the population-based Rotterdam Study cohort. Kaplan-Meier curves were used in order to depict observed survival, and completeness of follow-up was calculated in percentages using a freely available GitHub package developed by our research group.

Results: In total, six methods were found in the literature for quantification of follow-up completeness. Overall, two methods (the Simplified Person-Time Method and the modified Clark's Completeness Index C*) were closest to the true follow-up completeness in the 27 scenarios.

Conclusions: Researchers should make attempts to report follow-up completeness. This simulation study may assist researchers in selecting the most appropriate method to calculate follow-up completeness in different scenarios.