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Updated: Mar 14, 2026
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Including an infrequently measured time-varying error-prone covariate in survival analyses: a simulation-based
Viviane Philipps1, Laurence Freedman2, Veronika Deffner3
1Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, F-33000 Bordeaux, France.
American Journal of Epidemiology
|March 13, 2026
Summary
Accurate analysis of time-varying exposures requires accounting for measurement error and discrete updates. Multiple imputation (MI) and joint modeling (JM) are recommended, while classical regression-calibration (RC) should be avoided.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Epidemiologic studies frequently assess exposure-event risk associations.
- Time-varying exposures present challenges in survival models due to discrete measurements and continuous time.
- Exposures are often measured with error and truncated at the event time.
Purpose of the Study:
- To quantify the bias in Cox regression associations from intermittent, error-prone exposure measurements.
- To compare the performance of different statistical methods in handling time-varying, error-prone exposures.
Main Methods:
- Simulations were conducted under various scenarios.
- Five methods were compared: last observation carried-forward (LOCF), classical regression-calibration (RC), post-event information regression-calibration (PE-RC), multiple imputation (MI), and joint modeling (JM).
- Bias was evaluated in Cox regression models.
Main Results:
- Last observation carried-forward (LOCF) and classical regression-calibration (RC) exhibited substantial bias across most scenarios.
- Regression-calibration bias was mitigated by incorporating post-event information (PE-RC).
- Multiple imputation (MI) and joint modeling (JM) demonstrated relatively good performance with minimal bias.
Conclusions:
- Accounting for measurement error and discrete updates is crucial for time-varying exposures.
- Multiple imputation (MI) and joint modeling (JM) are suitable techniques for handling these complexities.
- Classical regression-calibration (RC) is not recommended due to informative truncation bias.
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