Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling

Piao, Jin; Ning, Jing; Shen, Yu

doi:10.1111/rssb.12308

Cited by 5 publications

(8 citation statements)

References 56 publications

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“…However, it is apparent that this assumption may not hold in some applications, and thus the generalizing of the proposed method to the situation of informative censoring deserves further investigation. In some applications, one may also encounter bivariate or multivariate failure time data [ 35 ], and it would be helpful to generalize the proposed method to deal with such data. Also the extensions of the proposed method to other regression models such as the transformation or additive hazards models can be useful.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring

Shao

et al. 2023

BMC Med Res Methodol

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Background Failure time data frequently occur in many medical studies and often accompany with various types of censoring. In some applications, left truncation may occur and can induce biased sampling, which makes the practical data analysis become more complicated. The existing analysis methods for left-truncated data have some limitations in that they either focus only on a special type of censored data or fail to flexibly utilize the distribution information of the truncation times for inference. Therefore, it is essential to develop a reliable and efficient method for the analysis of left-truncated failure time data with various types of censoring. Method This paper concerns regression analysis of left-truncated failure time data with the proportional hazards model under various types of censoring mechanisms, including right censoring, interval censoring and a mixture of them. The proposed pairwise pseudo-likelihood estimation method is essentially built on a combination of the conditional likelihood and the pairwise likelihood that eliminates the nuisance truncation distribution function or avoids its estimation. To implement the presented method, a flexible EM algorithm is developed by utilizing the idea of self-consistent estimating equation. A main feature of the algorithm is that it involves closed-form estimators of the large-dimensional nuisance parameters and is thus computationally stable and reliable. In addition, an R package is developed. Results The numerical results obtained from extensive simulation studies suggest that the proposed pairwise pseudo-likelihood method performs reasonably well in practical situations and is obviously more efficient than the conditional likelihood approach as expected. The analysis results of the MHCPS data with the proposed pairwise pseudo-likelihood method indicate that males have significantly higher risk of losing active life than females. In contrast, the conditional likelihood method recognizes this effect as non-significant, which is because the conditional likelihood method often loses some estimation efficiency compared with the proposed method. Conclusions The proposed method provides a general and helpful tool to conduct the Cox’s regression analysis of left-truncated failure time data under various types of censoring.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring

Shao

et al. 2023

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…However, it is apparent that this assumption may not hold in some applications, and thus the generalizing of the proposed method to the situation of informative censoring deserves further investigation. In some applications, one may also encounter bivariate or multivariate failure time data [33], and it would be helpful to generalize the proposed method to deal with such data. Also the extensions of the proposed method to other regression models such as the transformation or additive hazards models can be useful.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

A Pairwise Pseudo-Likelihood Approach for Regression Analysis of Left-Truncated Failure Time Data with Various Types of Censoring

Shao

et al. 2022

Preprint

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Background: Failure time data occur frequently in many medical studies and often accompany with various types of censoring. In some applications, left truncation may occur and can induce biased sampling, which makes the practical data analysis become more complicated. The existing analysis methods for left-truncated data has some limitations in that they either focus only on a special type of censored data or fail to flexibly utilize the distribution information of the truncation times for inference. Therefore, it is essential to develop an reliable and efficient method for the analysis left-truncated failure time data with various types of censoring. Method: This paper concerns regression analysis of left-truncated failure time data with the proportional hazards model under various types of censoring mechanism, including right censoring, interval censoring and a mixture of them. The proposed pairwise pseudo-likelihood estimation method is essentially built on a combination of the conditional likelihood and the pairwise likelihood that eliminates the nuisance truncation distribution function or avoids its estimation. To implement the presented method, a flexible EM algorithm is developed by utilizing the idea of self-consistent estimating equation. A main feature of the algorithm is that it involves closed-form estimators of the large-dimensional nuisance parameters and is thus computationally stable and reliable. In addition, an R package LTsurv is developed. Results: The numerical results obtained from extensive simulation studies suggest that the proposed pairwise pseudo-likelihood method performs reasonably well in practical situations and is obviously more efficient than the conditional likelihood approach as expected. The analysis results of the MHCPS data with the proposed pairwise pseudo-likelihood method indicate that males have significantly higher risk of losing active life than females. In contrast, the conditional likelihood method recognizes this effect as non-significant, which is because the conditional likelihood method often loses some estimation efficiency compared with the proposed method. Conclusions: The proposed method provides a general and helpful tool to conduct the Cox’s regression analysis of left-truncated failure time data under various types of censoring.

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“…Several recent papers also addressed the problem of joint estimation of two correlated event time distributions. Assuming length-biased sampling, Piao et al (2019) parameterized the two marginal survival functions using Cox proportional hazards models and allowed for correlations via a copula model. Under the same length-biased sampling set-up, Rabhi and Bouezmarni (2020) obtained non-parametric estimates of the bivariate distribution, copula function, and its density, when both variables are left-truncated and one of them is subject to informative censoring.…”

Section: Discussionmentioning

confidence: 99%

Non-Parametric Estimation of the Age-at-Onset Distribution from a Cross-Sectional Sample

2022

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We propose and study a simple and innovative non‐parametric approach to estimate the age‐of‐onset distribution for a disease from a cross‐sectional sample of the population that includes individuals with prevalent disease. First, we estimate the joint distribution of two event times, the age of disease onset and the survival time after disease onset. We accommodate that individuals had to be alive at the time of the study by conditioning on their survival until the age at sampling. We propose a computationally efficient expectation–maximization (EM) algorithm and derive the asymptotic properties of the resulting estimates. From these joint probabilities we then obtain non‐parametric estimates of the age‐at‐onset distribution by marginalizing over the survival time after disease onset to death. The method accommodates categorical covariates and can be used to obtain unbiased estimates of the covariate distribution in the source population. We show in simulations that our method performs well in finite samples even under large amounts of truncation for prevalent cases. We apply the proposed method to data from female participants in the Washington Ashkenazi Study to estimate the age‐at‐onset distribution of breast cancer associated with carrying BRCA1 or BRCA2 mutations.

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Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling

Cited by 5 publications

References 56 publications

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring

A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring

A Pairwise Pseudo-Likelihood Approach for Regression Analysis of Left-Truncated Failure Time Data with Various Types of Censoring

Non-Parametric Estimation of the Age-at-Onset Distribution from a Cross-Sectional Sample

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