Dynamic prediction of repeated events data based on landmarking model: application to colorectal liver metastases data

BMC Medical Research Methodology

Isao Yokota¹  Yutaka Matsuyama² 

[1] Department of Biostatistics, Graduate School of Medicine, Hokkaido University;Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo;

关键词: Dynamic prediction;    Landmarking;    Pseudo-observations;    Repeated events;    Terminal event;   

DOI  :  10.1186/s12874-019-0677-0

来源: DOAJ

【 摘 要 】

Abstract Background In some clinical situations, patients experience repeated events of the same type. Among these, cancer recurrences can result in terminal events such as death. Therefore, here we dynamically predicted the risks of repeated and terminal events given longitudinal histories observed before prediction time using dynamic pseudo-observations (DPOs) in a landmarking model. Methods The proposed DPOs were calculated using Aalen–Johansen estimator for the event processes described in the multi-state model. Furthermore, in the absence of a terminal event, a more convenient approach without matrix operation was described using the ordering of repeated events. Finally, generalized estimating equations were used to calculate probabilities of repeated and terminal events, which were treated as multinomial outcomes. Results Simulation studies were conducted to assess bias and investigate the efficiency of the proposed DPOs in a finite sample. Little bias was detected in DPOs even under relatively heavy censoring, and the method was applied to data from patients with colorectal liver metastases. Conclusions The proposed method enabled intuitive interpretations of terminal event settings.

【 授权许可】

Unknown