Assessing the numerical integration of dynamic prediction formulas using the exact expressions under the joint frailty-copula model

Kawakami, Ryo; Michimae, Hirofumi; Lin, Yuan-Hsin

doi:10.1007/s42081-021-00133-z

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…In our data example of gastric cancer patients (Section 5.1), bivariate correlated endpoints are of great interest [55]. Correlations between responses should be employed to improve the efficiency and reduce the bias of univariate analyses [56][57][58][59][60][61][62][63][64][65] and to predict a primary outcome by the secondary outcome [66][67][68][69]. Multivariate shrinkage estimators of multivariate normal means, such as [70,71], can be considered for this extension.…”

Section: Conclusion and Future Extensionsmentioning

confidence: 99%

A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

Taketomi

Konno

Chang

et al. 2021

Axioms

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Meta-analyses combine the estimators of individual means to estimate the common mean of a population. However, the common mean could be undefined or uninformative in some scenarios where individual means are “ordered” or “sparse”. Hence, assessments of individual means become relevant, rather than the common mean. In this article, we propose simultaneous estimation of individual means using the James–Stein shrinkage estimators, which improve upon individual studies’ estimators. We also propose isotonic regression estimators for ordered means, and pretest estimators for sparse means. We provide theoretical explanations and simulation results demonstrating the superiority of the proposed estimators over the individual studies’ estimators. The proposed methods are illustrated by two datasets: one comes from gastric cancer patients and the other from COVID-19 patients.

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Section: Conclusion and Future Extensionsmentioning

confidence: 99%

A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

Taketomi

Konno

Chang

et al. 2021

Axioms

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“…A series of works by Fournier et al [ 63 ], Asar et al [ 64 ], and Lenain et al [ 65 ] made a Shiny-based web application for the dynamic prediction of long-term kidney graft failure. Similarly, the dynamic prediction method of Emura et al [ 21 ] (see also the updated works of [ 66 ]) was transferred to web applications [ 67 ]. Clearly, this type of survival prognostic prediction tool can promote the development of personalized medicine, allowing clinicians to utilize powerful prognostic prediction models.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

2023

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Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package “compound.Cox”. The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.

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“…However, it is not trivial to extend this prediction method under competing risks. This risk prediction scheme is equivalent to dynamic prediction [48][49][50][51][52][53]. Moreover, the accelerated life test plans may be considered including the case of one-shot devices with competing risks [54,55].…”

Section: Discussionmentioning

confidence: 99%

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Michimae

Emura

2022

Mathematics

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Survival and reliability analyses deal with incomplete failure time data, such as censored and truncated data. Recently, the classical left-truncation scheme was generalized to analyze “field data”, defined as samples collected within a fixed period. However, existing competing risks models dealing with left-truncated field data are not flexible enough. We propose copula-based competing risks models for latent failure times, permitting a flexible parametric form. We formulate maximum likelihood estimation methods under the Weibull, lognormal, and gamma distributions for the latent failure times. We conduct simulations to check the performance of the proposed methods. We finally give a real data example. We provide the R code to reproduce the simulations and data analysis results.

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Assessing the numerical integration of dynamic prediction formulas using the exact expressions under the joint frailty-copula model

Cited by 10 publications

References 33 publications

A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

A Meta-Analysis for Simultaneously Estimating Individual Means with Shrinkage, Isotonic Regression and Pretests

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

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