Penalized estimation for proportional hazards models with current status data

We provide a simple and practical, yet flexible, penalized estimation method for a Cox proportional hazards model with current status data. We approximate the baseline cumulative hazard function by monotone B‐splines and use a hybrid approach based on the Fisher‐scoring algorithm and the isotonic re...

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Bibliographic Details
Published in:Statistics in medicine 2017-12, Vol.36 (30), p.4893-4907
Main Authors: Lu, Minggen, Li, Chin‐Shang
Format: Article
Language:English
Subjects:
Algorithms
Animals
Biostatistics - methods
Calcinosis - diagnosis
Carcinogenicity Tests - statistics & numerical data
Computer Simulation
current status data
Economic models
efficient estimation
Estimating techniques
Female
goodness‐of‐fit
Humans
isotonic regression
Lenses, Intraocular - adverse effects
Likelihood Functions
Male
Mice
monotone B‐spline
Monte Carlo Method
penalized estimation
Postoperative Complications - diagnosis
Proportional Hazards Models
Regression Analysis
Sample Size
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Summary:We provide a simple and practical, yet flexible, penalized estimation method for a Cox proportional hazards model with current status data. We approximate the baseline cumulative hazard function by monotone B‐splines and use a hybrid approach based on the Fisher‐scoring algorithm and the isotonic regression to compute the penalized estimates. We show that the penalized estimator of the nonparametric component achieves the optimal rate of convergence under some smooth conditions and that the estimators of the regression parameters are asymptotically normal and efficient. Moreover, a simple variance estimation method is considered for inference on the regression parameters. We perform 2 extensive Monte Carlo studies to evaluate the finite‐sample performance of the penalized approach and compare it with the 3 competing R packages: C1.coxph, intcox, and ICsurv. A goodness‐of‐fit test and model diagnostics are also discussed. The methodology is illustrated with 2 real applications.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.7489