Model diagnostics for the proportional hazards model with length-biased data

Lee, Chi Hyun; Ning, Jing; Shen, Yu

doi:10.1007/s10985-018-9422-y

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…Cox regression analysis was used for univariate and multivariate analyses, and the hazard ratio (HR) with 95% CI was calculated. The PH assumption was checked for the Cox regression models by constructing test statistics based on asymptotically mean-zero processes 66 . If the global P < 0.05, the PH assumption was violated; otherwise, the assumption was valid 66 .…”

Section: Methodsmentioning

confidence: 99%

“…The PH assumption was checked for the Cox regression models by constructing test statistics based on asymptotically mean-zero processes 66 . If the global P < 0.05, the PH assumption was violated; otherwise, the assumption was valid 66 . The relative importance of each variable in the multivariable model to predict the prognosis was assessed by using the χ 2 statistic minus the corresponding degree of freedom 67 .…”

Section: Methodsmentioning

confidence: 99%

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Prognostic and predictive value of a pathomics signature in gastric cancer

Chen

Chi

et al. 2022

Nat Commun

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The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PSGC) that is derived from multiple pathomics features of haematoxylin and eosin-stained slides. We find that the PSGC is an independent predictor of prognosis. A nomogram incorporating the PSGC and TNM staging system shows significantly improved accuracy in predicting the prognosis compared to the TNM staging system alone. Moreover, in stage II and III GC patients with a low PSGC (but not in those with a high PSGC), satisfactory chemotherapy benefits are observed. Therefore, the PSGC could serve as a prognostic predictor in patients with GC and might be a potential predictive indicator for decision-making regarding adjuvant chemotherapy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prognostic and predictive value of a pathomics signature in gastric cancer

Chen

Chi

et al. 2022

Nat Commun

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“…The null distribution of the general form (3) under Model (1) has been studied in Lee et al (2019b) to derive the critical values for test statistics T j 1 , T j 2 , and T 2 . We can approximate the null distribution by adopting the resampling technique used in Lin et al (1993).…”

Section: Checking the Cox Model Assumptionsmentioning

confidence: 99%

“…In this paper, we introduce a new package, CoxPhLb (Lee et al, 2019a), in R that provides tools to analyze lengthbiased data under the Cox model. The package includes functions that fit the Cox model using the estimation method proposed by Qin and Shen (2010), check the proportional hazards model assumptions based on methods developed by Lee et al (2019b) and check the underlying stationarity assumption. CoxPhLb is available from the Comprehensive R Archive Network (CRAN) at http: //CRAN.R-project.org/package=CoxPhLb.…”

Section: Introductionmentioning

confidence: 99%

CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model

Lee¹,

Zhou²,

Ning³

et al. 2020

The R Journal

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Data subject to length-biased sampling are frequently encountered in various applications including prevalent cohort studies and are considered as a special case of left-truncated data under the stationarity assumption. Many semiparametric regression methods have been proposed for lengthbiased data to model the association between covariates and the survival outcome of interest. In this paper, we present a brief review of the statistical methodologies established for the analysis of length-biased data under the Cox model, which is the most commonly adopted semiparametric model, and introduce an R package CoxPhLb that implements these methods. Specifically, the package includes features such as fitting the Cox model to explore covariate effects on survival times and checking the proportional hazards model assumptions and the stationarity assumption. We illustrate usage of the package with a simulated data example and a real dataset, the Channing House data, which are publicly available.

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“…Lu, Liu and Chen 15 and Borgan and Zhang 16 studied to check for the proportional hazards assumption with nested casecontrol data. Lee, Ning and Shen 17 proposed a diagnostic tool for testing the proportional hazards assumption with length-biased data.…”

Section: Introductionmentioning

confidence: 99%

A class of goodness‐of‐fit test for the additive hazards model with case‐cohort data

Cao

2020

Pharmaceutical Statistics

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The case-cohort design is commonly used in epidemiological studies due to its cost-effectiveness. The additive hazards model is widely used in survival analysis when the hazards difference is constant. In this article, we propose a class of goodness-of-fit test statistics for the assumption of the additive hazards model with case-cohort data through a class of asymptotically mean-zero multiparameter stochastic processes. We also establish the asymptotic theory of the proposed test statistics and a resampling scheme is adopted to approximate its asymptotic distribution. The performance of the proposed test statistics is evaluated through simulation studies and a real dataset is analyzed to illustrate the proposed method.

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Model diagnostics for the proportional hazards model with length-biased data

Cited by 8 publications

References 24 publications

Prognostic and predictive value of a pathomics signature in gastric cancer

Prognostic and predictive value of a pathomics signature in gastric cancer

CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model

A class of goodness‐of‐fit test for the additive hazards model with case‐cohort data

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