Pseudo-partial likelihood for proportional hazards models with biased-sampling data

Tsai, Wei Yann

doi:10.1093/biomet/asp026

Cited by 67 publications

(69 citation statements)

References 27 publications

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“…Thus, a consistent estimator of the regression parameters can be easily derived by maximizing the pseudo-profile likelihood. Unlike other bias-adjusted risk-set methods, including Ghosh (2008), Tsai (2009) and Qin & Shen (2010), the proposed estimation procedure does not involve estimation of the censoring distribution, so it is expected to be more stable when the censoring proportion is high.…”

Section: Introductionmentioning

confidence: 99%

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

Huang¹,

Qin²,

Follmann³

2012

Biometrika

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SUMMARYThis paper considers semiparametric estimation of the Cox proportional hazards model for right-censored and length-biased data arising from prevalent sampling. To exploit the special structure of length-biased sampling, we propose a maximum pseudo-profile likelihood estimator, which can handle time-dependent covariates and is consistent under covariate-dependent censoring. Simulation studies show that the proposed estimator is more efficient than its competitors. A data analysis illustrates the methods and theory.

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Section: Introductionmentioning

confidence: 99%

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

Huang¹,

Qin²,

Follmann³

2012

Biometrika

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“…In fact, this setting coincides with the first type of length-biased sampling considered in Tsai (2009). Adopting the notation used in Tsai (2009), we let h 2 (a, t, δ | Z ) be the conditional probability density function of the observed data (A, T, ), where A denotes the truncation time.…”

Section: Estimation Proceduresmentioning

confidence: 99%

“…It also corresponds to the second type of censoring under the lengthbias setting studied in Tsai (2009). This configuration involves censoring of the residual lifetime after the data are sampled with bias.…”

Section: Real Examplesmentioning

confidence: 99%

“…Under the scope of semiparametric inference, Tsai (2009) carefully studied two intrinsically different types of length-biased sampling, depending on the assumption made on the recruitment/censoring procedure under the Cox proportional hazards model. Shen and others (2012) proposed two likelihood approaches for the estimation and assessment of the difference between two survival distributions under length-biased sampling.…”

Section: Introductionmentioning

confidence: 99%

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Accelerated failure time model under general biased sampling scheme: Table 1.

Kim

Sit

Ying

2016

Biostat

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SUMMARYRight-censored time-to-event data are sometimes observed from a (sub)cohort of patients whose survival times can be subject to outcome-dependent sampling schemes. In this paper, we propose a unified estimation method for semiparametric accelerated failure time models under general biased estimating schemes. The proposed estimator of the regression covariates is developed upon a bias-offsetting weighting scheme and is proved to be consistent and asymptotically normally distributed. Large sample properties for the estimator are also derived. Using rank-based monotone estimating functions for the regression parameters, we find that the estimating equations can be easily solved via convex optimization. The methods are confirmed through simulations and illustrated by application to real datasets on various sampling schemes including length-bias sampling, the case-cohort design and its variants.

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“…Wang [18] proposed pseudo-likelihood for length-biased failure times under the Cox proportional hazards model, but this method cannot be applied to right-censored failure times. Luo and Tsai [19] and Tsai [20] derived pseudo-partial-likelihood estimators for right-censored lengthbiased data which have closed-form and retain high efficiency. Shen et al [21] considered modeling covariate effects for length-biased data under time transform and accelerated failure time models.…”

Section: Introductionmentioning

confidence: 99%

Measurement Error for Age of Onset in Prevalent Cohort Studies

Zhong¹,

Cook²

2014

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Prevalent cohort studies involve screening a sample of individuals from a population for disease, recruiting affected individuals, and prospectively following the cohort of individuals to record the occurrence of disease-related complications or death. This design features a response-biased sampling scheme since individuals living a long time with the disease are preferentially sampled, so naive analysis of the time from disease onset to death will over-estimate survival probabilities. Unconditional and conditional analyses of the resulting data can yield consistent estimates of the survival distribution subject to the validity of their respective model assumptions. The time of disease onset is retrospectively reported by sampled individuals, however, this is often associated with measurement error. In this article we present a framework for studying the effect of measurement error in disease onset times in prevalent cohort studies, report on empirical studies of the effect in each framework of analysis, and describe likelihood-based methods to address such a measurement error.

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Pseudo-partial likelihood for proportional hazards models with biased-sampling data

Cited by 67 publications

References 27 publications

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

Accelerated failure time model under general biased sampling scheme: Table 1.

Measurement Error for Age of Onset in Prevalent Cohort Studies

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