A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

Othus, Megan; Li, Yang; Tiwari, Ram C.

doi:10.1198/jasa.2009.tm08033

Cited by 25 publications

(18 citation statements)

References 58 publications

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“…To account for such dependent censoring, one may assume certain dependence structures between the failure time and the censoring time (e.g., see Tanaka and Rao (2005) and Li, Tiwari, and Guha (2007)). Alternatively, we can adopt the technique proposed by Othus, Li, and Tiwari (2009) through the use of the conditional crude hazard function of the censoring time given the covariates. Future research along this direction is warranted.…”

Section: Discussionmentioning

confidence: 99%

Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study

Yuan

Diao

2014

The International Journal of Biostatistics

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The proportional odds model is commonly used in the analysis of failure time data. The assumption of constant odds ratios over time in the proportional odds model, however, can be violated in some applications. Motivated by a genetic study with breast cancer patients, we propose a novel semiparametric odds rate model for the analysis of right-censored survival data. The proposed model incorporates the short-term and long-term covariate effects on the failure time data and includes the proportional odds model as a nested model. We develop efficient likelihood-based inference procedures and establish the large sample properties of the proposed non-parametric maximum likelihood estimators. Simulation studies demonstrate that the proposed methods perform well in practical settings. An application to the motivating example is provided.

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Section: Discussionmentioning

confidence: 99%

Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study

Yuan

Diao

2014

The International Journal of Biostatistics

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“…Othus et al. () provided a unified framework for a class of transformation cure models that allow for dependent censoring. They utilized an inverse censoring probability formula to develop unbiased EEs.…”

Section: Conclusion Remarksmentioning

confidence: 99%

“…In this case, the EE approach can be a choice because it does not require a full specification of the joint distribution. Othus et al (2009) provided a unified framework for a class of transformation cure models that allow for dependent censoring. They utilized an inverse censoring probability formula to develop unbiased EEs.…”

Section: Conclusion Remarksmentioning

confidence: 99%

A semiparametric mixture cure survival model for left‐truncated and right‐censored data

Chen¹,

Shen

Wei

et al. 2016

Biometrical J

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In follow-up studies, the disease event time can be subject to left truncation and right censoring. Furthermore, medical advancements have made it possible for patients to be cured of certain types of diseases. In this article, we consider a semiparametric mixture cure model for the regression analysis of left-truncated and right-censored data. The model combines a logistic regression for the probability of event occurrence with the class of transformation models for the time of occurrence. We investigate two techniques for estimating model parameters. The first approach is based on martingale estimating equations (EEs). The second approach is based on the conditional likelihood function given truncation variables. The asymptotic properties of both proposed estimators are established. Simulation studies indicate that the conditional maximum-likelihood estimator (cMLE) performs well while the estimator based on EEs is very unstable even though it is shown to be consistent. This is a special and intriguing phenomenon for the EE approach under cure model. We provide insights into this issue and find that the EE approach can be improved significantly by assigning appropriate weights to the censored observations in the EEs. This finding is useful in overcoming the instability of the EE approach in some more complicated situations, where the likelihood approach is not feasible. We illustrate the proposed estimation procedures by analyzing the age at onset of the occiput-wall distance event for patients with ankylosing spondylitis.

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“…One category is the 2-component mixture cure models, where the distribution of T is assumed to be a mixture of 2 components and have a survival function S pop (t|x) = (x)S(t|x) + 1 − (x), with (x) and S(t|x) being, respectively, the proportion and the survival function of susceptible subjects. Examples can be found in some recent papers [1][2][3][4][5] and the references within.…”

Section: Introductionmentioning

confidence: 99%

Promotion time cure rate model with nonparametric form of covariate effects

Chen¹,

2018

Statistics in Medicine

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Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study.

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A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

Cited by 25 publications

References 58 publications

Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study

Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study

A semiparametric mixture cure survival model for left‐truncated and right‐censored data

Promotion time cure rate model with nonparametric form of covariate effects

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