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Exposure assessment for Cox proportional hazards cure models with intervalācensored survival data
- Source :
- Biom J
- Publication Year :
- 2021
- Publisher :
- Wiley, 2021.
-
Abstract
- Mixture cure models have been developed as an effective tool to analyze failure time data with a cure fraction. Used in conjunction with the logistic regression model, this model allows covariate-adjusted inference of an exposure effect on the cured probability and on the hazard of failure for the uncured subjects. However, the covariate-adjusted inference for the overall exposure effect is not directly provided. In this paper, we describe a Cox proportional hazards cure model to analyze interval-censored survival data in the presence of a cured fraction and then apply a post-estimation approach by using model-predicted estimates difference to assess the overall exposure effect on the restricted mean survival time scale. For baseline hazard/survival function estimation, simple parametric models as fractional polynomials or restricted cubic splines are utilized to approximate the baseline logarithm cumulative hazard function, or alternatively, the full likelihood is specified through a piecewise linear approximation for the cumulative baseline hazard function. Simulation studies were conducted to demonstrate the unbiasedness of both estimation methods for the overall exposure effect estimates over various baseline hazard distribution shapes. The methods are applied to analyze the interval-censored relapse time data from a smoking cessation study.
- Subjects :
- Statistics and Probability
Hazard (logic)
Models, Statistical
Logarithm
Proportional hazards model
General Medicine
Logistic regression
Survival Analysis
Article
Logistic Models
Survival function
Parametric model
Statistics
Humans
Computer Simulation
Fraction (mathematics)
Statistics, Probability and Uncertainty
Algorithms
Proportional Hazards Models
Mathematics
Exposure assessment
Subjects
Details
- ISSN :
- 15214036 and 03233847
- Volume :
- 64
- Database :
- OpenAIRE
- Journal :
- Biometrical Journal
- Accession number :
- edsair.doi.dedup.....f221c5ec14c57d6adfd9a58b03b508de