A Parametric Survival Model When a Covariate is Subject to Left-Censoring
|Abdus Sattar1*, Sanjoy K. Sinha2 and Nathan J. Morris1|
|1Department of Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, OH, USA|
|2School of Mathematics and Statistics, Carleton University, Ottawa, Ontario K1S 5B6, Canada|
|Corresponding Author :||Abdus Sattar
Department of Epidemiology and Biostatistics
School of Medicine
Case Western Reserve University
10900 Euclid Avenue, BRB, G-19
Cleveland, OH 44106-4945, USA
E-mail: [email protected]
|Received July 05, 2012; Accepted August 20, 2012; Published August 25, 2012|
|Citation: Sattar A, Sinha SK, Morris NJ (2012) A Parametric Survival Model When a Covariate is Subject to Left-Censoring. J Biomet Biostat S3:002. doi:10.4172/2155-6180.S3-002|
|Copyright: © 2012 Sattar A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
Problem statement: Modeling survival data with a set of covariates usually assumes that the values of the covariates are fully observed. However, in a variety of applications, some values of a covariate may be left-censored due to inadequate instrument sensitivity to quantify the biospecimen. When data are left-censored, the true values are missing but are known to be smaller than the detection limit. The most commonly used ad-hoc method to deal with nondetect values is to substitute the nondetect values by the detection limit. Such ad-hoc analysis of survival data with an explanatory variable subject to left-censoring may provide biased and inefficient estimators of hazard ratios and survivor functions.
Method: We consider a parametric proportional hazards model to analyze time-to-event data. We propose a likelihood method for the estimation and inference of model parameters. In this likelihood approach, instead of replacing the nondetect values by the detection limit, we adopt a numerical integration technique to evaluate the observed data likelihood in the presence of a left-censored covariate. Monte Carlo simulations were used to demonstrate various properties of the proposed regression estimators including the consistency and efficiency.
Results: The simulation study shows that the proposed likelihood approach provides approximately unbiased estimators of the model parameters. The proposed method also provides estimators that are more efficient than those obtained under the ad-hoc method. Also, unlike the ad-hoc estimators, the coverage probabilities of the proposed estimators are at their nominal level. Analysis of a large cohort study, genetic and inflammatory marker of sepsis study, shows discernibly different results based on the proposed method.
Conclusion: Naive use of detection limit in a parametric survival model may provide biased and inefficient estimators of hazard ratios and survivor functions. The proposed likelihood approach provides approximately unbiased and efficient estimators of hazard ratios and survivor functions.