Dynamic prediction of recurrent events data by landmarking with application to a follow-up study of patients after kidney transplant

• Published in: Statistical methods in medical research Vol. 27; no. 3; p. 832
• Main Authors: Musoro, J Z, Struijk, G H, Geskus, R B, Ten Berge, Ijm, Zwinderman, A H
• Format: Journal Article
• Language: English
• Published: England 01.03.2018
• Subjects: Biostatistics - methods; Computer Simulation; Follow-Up Studies; Humans; Kidney Transplantation - adverse effects; Kidney Transplantation - statistics & numerical data; Likelihood Functions; Models, Statistical; Opportunistic Infections - epidemiology; Opportunistic Infections - etiology; Postoperative Complications - epidemiology; Postoperative Complications - etiology; Prognosis; Recurrence; Risk Factors; recurrent events; landmark; multiple markers; frailty models; Dynamic prediction
• ISSN: 1477-0334
• DOI: 10.1177/0962280216643563

This paper extends dynamic prediction by landmarking to recurrent event data. The motivating data comprised post-kidney transplantation records of repeated infections and repeated measurements of multiple markers. At each landmark time point t , a Cox proportional hazards model with a frailty term was fitted using data of individuals who were at risk at landmark s. This model included the time-updated marker values at t as time-fixed covariates. Based on a stacked data set that merged all landmark data sets, we considered supermodels that allow parameters to depend on the landmarks in a smooth fashion. We described and evaluated four ways to parameterize the supermodels for recurrent event data. With both the study data and simulated data sets, we compared supermodels that were fitted on stacked data sets that consisted of either overlapping or non-overlapping landmark periods. We observed that for recurrent event data, the supermodels may yield biased estimates when overlapping landmark periods are used for stacking. Using the best supermodel amongst the ones considered, we dynamically estimated the probability to remain infection free between t and a prediction horizon t , conditional on the information available at t .