Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.
Stat Methods Med Res
; 33(2): 256-272, 2024 Feb.
Article
in En
| MEDLINE
| ID: mdl-38196243
ABSTRACT
Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proportional Hazards Models
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Stat Methods Med Res
Year:
2024
Document type:
Article
Affiliation country:
Netherlands
Country of publication:
United kingdom