Lessons learned: avoiding bias via multi-state analysis of patients' trajectories in real-time.

ABSTRACT:

Objectives

Many studies have attempted to determine the disease severity and patterns of COVID-19. However, at the beginning of the pandemic, the complex patients' trajectories were only descriptively reported, and many analyses were worryingly prone to time-dependent-, selection-, and competing risk biases. Multi-state models avoid these biases by jointly analysing multiple clinical outcomes while taking into account their time dependency, including current cases, and modelling competing events. This paper uses a publicly available data set from the first wave in Israel as an example to demonstrate the benefits of analysing hospital data via multi-state methodology.

Methods

We compared the outcome of the data analysis using multi-state models with the outcome obtained when

SUBMITTER: Lucke E 

PROVIDER: S-EPMC11215151 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature