A comparison of statistical methods to predict the residual lifetime risk

Lifetime risk measures the cumulative risk for developing a disease over one’s lifespan. Modeling the lifetime risk must account for left truncation, the competing risk of death, and inference at a fixed age. In addition, statistical methods to predict the lifetime risk should account for covariate-...

Full description

Saved in:
Bibliographic Details
Published in:European journal of epidemiology Vol. 37; no. 2; pp. 173 - 194
Main Authors: Conner, Sarah C., Beiser, Alexa, Benjamin, Emelia J., LaValley, Michael P., Larson, Martin G., Trinquart, Ludovic
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.02.2022
Springer Nature B.V
Subjects:
Age
Atrial Fibrillation - epidemiology
Bias
Cardiology
Computer Simulation
Epidemiology
Fibrillation
Generalized linear models
Humans
Incidence
Infectious Diseases
Life span
Medicine
Medicine & Public Health
Methods
Models, Statistical
Oncology
Proportional Hazards Models
Public Health
Risk
Statistical inference
Statistical methods
Statistical models
Statistics
Survival Analysis
Cumulative incidence
Left truncation
Survival analysis
Competing risks
Time-to-event data
Lifetime risk
ISSN:0393-2990, 1573-7284, 1573-7284
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Lifetime risk measures the cumulative risk for developing a disease over one’s lifespan. Modeling the lifetime risk must account for left truncation, the competing risk of death, and inference at a fixed age. In addition, statistical methods to predict the lifetime risk should account for covariate-outcome associations that change with age. In this paper, we review and compare statistical methods to predict the lifetime risk. We first consider a generalized linear model for the lifetime risk using pseudo-observations of the Aalen-Johansen estimator at a fixed age, allowing for left truncation. We also consider modeling the subdistribution hazard with Fine-Gray and Royston-Parmar flexible parametric models in left truncated data with time-covariate interactions, and using these models to predict lifetime risk. In simulation studies, we found the pseudo-observation approach had the least bias, particularly in settings with crossing or converging cumulative incidence curves. We illustrate our method by modeling the lifetime risk of atrial fibrillation in the Framingham Heart Study. We provide technical guidance to replicate all analyses in R.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:0393-2990
1573-7284
1573-7284
DOI:10.1007/s10654-021-00815-8