A two-stage approach for dynamic prediction of time-to-event distributions

Xuelin Huang, Fangrong Yan, Jing Ning, Ziding Feng, Sangbum Choi, Jorge Cortes

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Dynamic prediction uses longitudinal biomarkers for real-time prediction of an individual patient's prognosis. This is critical for patients with an incurable disease such as cancer. Biomarker trajectories are usually not linear, nor even monotone, and vary greatly across individuals. Therefore, it is difficult to fit them with parametric models. With this consideration, we propose an approach for dynamic prediction that does not need to model the biomarker trajectories. Instead, as a trade-off, we assume that the biomarker effects on the risk of disease recurrence are smooth functions over time. This approach turns out to be computationally easier. Simulation studies show that the proposed approach achieves stable estimation of biomarker effects over time, has good predictive performance, and is robust against model misspecification. It is a good compromise between two major approaches, namely, (i) joint modeling of longitudinal and survival data and (ii) landmark analysis. The proposed method is applied to patients with chronic myeloid leukemia. At any time following their treatment with tyrosine kinase inhibitors, longitudinally measured BCR-ABL gene expression levels are used to predict the risk of disease progression.

Original languageEnglish (US)
Pages (from-to)2167-2182
Number of pages16
JournalStatistics in Medicine
Issue number13
StatePublished - Jun 15 2016
Externally publishedYes


  • Biomarker
  • Dynamic prediction
  • Landmark analysis
  • Longitudinal data
  • Survival analysis
  • Time-dependent covariate

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


Dive into the research topics of 'A two-stage approach for dynamic prediction of time-to-event distributions'. Together they form a unique fingerprint.

Cite this