A parametric survival model with an interval‐censored covariate

Langohr, Klaus; Gómez, Guadalupe; Muga, Roberto

doi:10.1002/sim.1892

Cited by 22 publications

(18 citation statements)

References 22 publications

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“…Langohr et al (2004) and Sattar et al (2012) proposed fully-parametric survival models with a single interval-censored predictor. Lee et al (2003) also considered survival models with a single covariate subject to DLs, though they proposed using semiparametric Cox proportional hazards models in which the relative risk function for the censored covariates is replaced by a nonparametric estimate of its expected value.…”

Section: Introductionmentioning

confidence: 99%

Flexible modeling of survival data with covariates subject to detection limits via multiple imputation

Bernhardt

Wang

Zhang

2014

Computational Statistics & Data Analysis

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Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.

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Section: Introductionmentioning

confidence: 99%

Flexible modeling of survival data with covariates subject to detection limits via multiple imputation

Bernhardt

Wang

Zhang

2014

Computational Statistics & Data Analysis

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“…If the distributional assumption is satisfied then this modeling approach is more efficient than its counterpart nonparametric and semi-parametric hazard models. Langor et al [11] studied doubly censored survival data with an interval-censored covariate in a parametric survival model framework. They have considered a censored discrete covariate.…”

Section: Introductionmentioning

confidence: 99%

A Parametric Survival Model When a Covariate is Subject to Left-Censoring

Sattar

Sinha²,

Morris³

2013

J Biomet Biostat

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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.

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“…As the extension of this approach, a statistical model of interval censored covariate at a right censored failure time was proposed. A parametric survival distribution was assumed and a corresponding likelihood was derived by Langohr et al (2004). They constructed a joint likelihood of both failure time and an interval censored continuous covariate.…”

Section: Discussionmentioning

confidence: 99%

A modified estimating equation for a binary time varying covariate with an interval censored changing time

Kim

2016

CSAM

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Interval censored failure time data often occurs in an observational study where a subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are made available. Several methods have been suggested to analyze interval censored failure time data (Sun, 2006). In this article, we are concerned with a binary time-varying covariate whose changing time is interval censored. A modified estimating equation is proposed by extending the approach suggested in the presence of a missing covariate. Based on simulation results, the proposed method shows a better performance than other simple imputation methods. ACTG 181 dataset were analyzed as a real example.

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A parametric survival model with an interval‐censored covariate

Cited by 22 publications

References 22 publications

Flexible modeling of survival data with covariates subject to detection limits via multiple imputation

Flexible modeling of survival data with covariates subject to detection limits via multiple imputation

A Parametric Survival Model When a Covariate is Subject to Left-Censoring

A modified estimating equation for a binary time varying covariate with an interval censored changing time

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