Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model

Ning, Jing; Qin, Jing; Shen, Yu

doi:10.1080/01621459.2014.946034

Cited by 22 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the sequel, we assume that R c is independent of (Y, X, T ). This assumption is common in right-censored left-truncated data setting, and is reasonable in most practical situations [see Bergeron et al (2008) and Ning et al (2014)…”

Section: Copula and Bivariate Distributions 21 Data Setting And Notamentioning

confidence: 99%

Nonparametric Inference for Copulas and Measures of Dependence Under Length-Biased Sampling and Informative Censoring

Rabhi

Bouezmarni

2019

Journal of the American Statistical Association

View full text Add to dashboard Cite

Length-biased data are often encountered in cross-sectional surveys and prevalent-cohort studies on disease durations. Under length-biased sampling subjects with longer disease durations have greater chance to be observed. As a result, covariate values linked to the longer survivors are favoured by the sampling mechanism. When the sampled durations are also subject to right censoring, the censoring is informative. Modelling dependence structure without adjusting for these issues leads to biased results. In this paper, we consider copulas for modelling dependence when the collected data are lengthbiased and account for both informative censoring and covariate bias that are naturally linked to length-biased sampling. We address nonparametric estimation of the bivariate distribution, copula function and its density, and Kendall and Spearman measures for rightcensored length-biased data. The proposed estimator for the bivariate cdf is a Hadamarddifferentiable functional of two MLEs (Kaplan-Meier and empirical cdf) and inherits their efficiency. Based on this estimator, we devise two estimators for copula function and a local-polynomial estimator for copula density that accounts for boundary bias. The limiting processes of the estimators are established by deriving their i.i.d. representations. As a by-product, we establish the oscillation behavior of the bivariate cdf estimator. In addition, we introduce estimators for Kendall and Spearman measures and study their weak convergence. The proposed method is applied to analyze a set of right-censored length-biased data on survival with dementia, collected as part of a nationwide study in Canada.

show abstract

Section: Copula and Bivariate Distributions 21 Data Setting And Notamentioning

confidence: 99%

Nonparametric Inference for Copulas and Measures of Dependence Under Length-Biased Sampling and Informative Censoring

Rabhi

Bouezmarni

2019

Journal of the American Statistical Association

View full text Add to dashboard Cite

show abstract

“…random variables without covariate effects. Using this unique feature, Ning et al (2014a) proposed a class of estimating equations based on the score functions for the transformed i.i.d. data, which are derived from the full likelihood function under commonly used semiparametric models such as the proportional hazards or proportional odds model.…”

Section: Semiparametric Accelerated Failure Time Model: Estimation mentioning

confidence: 99%

Nonparametric and semiparametric regression estimation for length-biased survival data

2016

Self Cite

View full text Add to dashboard Cite

For the past several decades, nonparametric and semiparametric modeling for conventional right-censored survival data has been investigated intensively under a noninformative censoring mechanism. However, these methods may not be applicable for analyzing right-censored survival data that arise from prevalent cohorts when the failure times are subject to length-biased sampling. This review article is intended to provide a summary of some newly developed methods as well as established methods for analyzing length-biased data.

show abstract

“…Determining the best way to adjust for the sampling bias from the prevalent cohort has been a long-standing statistical problem. Statistical methods for analysing prevalent sampling survival data have been widely studied by Wang et al (1986), Asgharian et al (2002), Asgharian and Wolfson (2005), Shen et al (2009), Tsai (2009), Qin et al (2011), Kim et al (2013), Ning et al (2014), Liu et al (2016) and others. Most of the methods that were used in those references dealt with a special type of prevalent data under the stationarity assumption that the incidence of disease is constant over time (stable disease model).…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling

Piao

Ning

Shen

2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

Self Cite

View full text Add to dashboard Cite

Summary To understand better the relationship between patient characteristics and their residual survival after an intermediate event such as the local recurrence of cancer, it is of interest to identify patients with the intermediate event and then to analyse their residual survival data. One challenge in analysing such data is that the observed residual survival times tend to be longer than those in the target population, since patients who die before experiencing the intermediate event are excluded from the cohort identified. We propose to model jointly the ordered bivariate survival data by using a copula model and appropriately adjusting for the sampling bias. We develop an estimating procedure to estimate simultaneously the parameters for the marginal survival functions and the association parameter in the copula model, and we use a two‐stage expectation–maximization algorithm. Using empirical process theory, we prove that the estimators have strong consistency and asymptotic normality. We conduct simulation studies to evaluate the finite sample performance of the method proposed. We apply the method to two cohort studies to evaluate the association between patient characteristics and residual survival.

show abstract

Score Estimating Equations from Embedded Likelihood Functions Under Accelerated Failure Time Model

Cited by 22 publications

References 42 publications

Nonparametric Inference for Copulas and Measures of Dependence Under Length-Biased Sampling and Informative Censoring

Nonparametric Inference for Copulas and Measures of Dependence Under Length-Biased Sampling and Informative Censoring

Nonparametric and semiparametric regression estimation for length-biased survival data

Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling

Contact Info

Product

Resources

About