Stacked survival models for residual lifetime data

BMC Medical Research Methodology

James H. McVittie¹  David B. Wolfson¹  Zhaoheng Li²  Vittorio Addona² 

[1] Department of Mathematics and Statistics, McGill University, Montreal, Canada;Department of Mathematics, Statistics and Computer Science, Macalester College, St.Paul, USA;

关键词: Survival analysis;    Residual lifetime data;    Nonparametric estimation;    Stacking;   

DOI  :  10.1186/s12874-021-01496-3

来源: Springer

【 摘 要 】

When modelling the survival distribution of a disease for which the symptomatic progression of the associated condition is insidious, it is not always clear how to measure the failure/censoring times from some true date of disease onset. In a prevalent cohort study with follow-up, one approach for removing any potential influence from the uncertainty in the measurement of the true onset dates is through the utilization of only the residual lifetimes. As the residual lifetimes are measured from a well-defined screening date (prevalence day) to failure/censoring, these observed time durations are essentially error free. Using residual lifetime data, the nonparametric maximum likelihood estimator (NPMLE) may be used to estimate the underlying survival function. However, the resulting estimator can yield exceptionally wide confidence intervals. Alternatively, while parametric maximum likelihood estimation can yield narrower confidence intervals, it may not be robust to model misspecification. Using only right-censored residual lifetime data, we propose a stacking procedure to overcome the non-robustness of model misspecification; our proposed estimator comprises a linear combination of individual nonparametric/parametric survival function estimators, with optimal stacking weights obtained by minimizing a Brier Score loss function.

【 授权许可】

CC BY