PittsburghV1, Istex, Corpus, bibRecord, 001684

Constrained parametric model for simultaneous inference of two cumulative incidence functions

Identifieur interne : 001684 ( Istex/Corpus ); précédent : 001683; suivant : 001685

Constrained parametric model for simultaneous inference of two cumulative incidence functions

Auteurs : Haiwen Shi ; Yu Cheng ; Jong-Hyeon Jeong

Source :

Biometrical Journal [ 0323-3847 ] ; 2013-01.

RBID : ISTEX:638A3C39E0259BAFBDFDCD2B69DA267D264D3D41

English descriptors

KwdEn :
- Additivity, Additivity constraint, Asymptote, Baseline, Baseline cifs, Biometrical, Breast cancer recurrence, Breast cancer study, Censoring, Censoring times, Cheng, Cifs, Cifs regression, Complementary link function, Complementary transformation, Conditional distribution, Constraint, Covariate, Covariate effects, Covariates, Coverage rate, Coverage rates, Cumulative incidence function, Estimator, Event time, Event times, Exible, Generalized model, Generalized transformation, Gmbh, Gompertz, Gompertz function, Gompertz model, Gray method, Gray model, Jeong, Kgaa, Likelihood function, Likelihood theory, Logistic, Logistic baseline, Logistic function, Logistic model, Modeling, Other events, Package timereg, Parametric, Parametric model, Parametric models, Parametric regression model, Primary cause, Proportional hazards assumption, Proportional subdistribution hazards assumption, Proportional subdistribution hazards model, Recurrence, Regression, Regression model, Regression models, Risks data, Scenario, Scheike, Semiparametric, Semiparametric model, Semiparametric models, Simulation, Simulation results, Simulation studies, Standard errors, Subdistribution, Tamoxifen, Test process, Time interaction, Tumor size, Verlag, Verlag gmbh, Weinheim, Weinheim biometrical journal.
Teeft :
- Additivity, Additivity constraint, Asymptote, Baseline, Baseline cifs, Biometrical, Breast cancer recurrence, Breast cancer study, Censoring, Censoring times, Cheng, Cifs, Cifs regression, Complementary link function, Complementary transformation, Conditional distribution, Constraint, Covariate, Covariate effects, Covariates, Coverage rate, Coverage rates, Cumulative incidence function, Estimator, Event time, Event times, Exible, Generalized model, Generalized transformation, Gmbh, Gompertz, Gompertz function, Gompertz model, Gray method, Gray model, Jeong, Kgaa, Likelihood function, Likelihood theory, Logistic, Logistic baseline, Logistic function, Logistic model, Modeling, Other events, Package timereg, Parametric, Parametric model, Parametric models, Parametric regression model, Primary cause, Proportional hazards assumption, Proportional subdistribution hazards assumption, Proportional subdistribution hazards model, Recurrence, Regression, Regression model, Regression models, Risks data, Scenario, Scheike, Semiparametric, Semiparametric model, Semiparametric models, Simulation, Simulation results, Simulation studies, Standard errors, Subdistribution, Tamoxifen, Test process, Time interaction, Tumor size, Verlag, Verlag gmbh, Weinheim, Weinheim biometrical journal.

Abstract

We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds‐rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds‐rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite‐sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.

Url:

https://api.istex.fr/document/638A3C39E0259BAFBDFDCD2B69DA267D264D3D41/fulltext/pdf

DOI: 10.1002/bimj.201200011

Links to Exploration step

ISTEX:638A3C39E0259BAFBDFDCD2B69DA267D264D3D41

Le document en format XML

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<front><div type="abstract">We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds‐rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds‐rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite‐sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.</div>
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<profileDesc><abstract style="main"><p>We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds‐rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds‐rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite‐sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.</p>
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 Simulation results on censoring time following a proportional odds model; the data were simulated from the modified logistic or Gompertz base with a proportional subdistribution hazard transformation (LOG+PSH and GOM+PSH) or with a generalized odds‐rate transformation (LOG + GOR and GOM + GOR); referring to Table 1 for the definition of AVE, MSE, ESE, and Cov.</p>
<p><b>Table S2</b>
 Simulation results where the data were simulated from our proposed modified logistic (panel LOG + PSH) or Gompertz model (panel GOM + PSH) with complimentary log–log transformation or with generalized odds‐rate transformation (panels LOG + GOR and GOM + GOR) with sample size n=500 and 40% censoring rate, where AVE is the average of the estimates, MSE is the average of the model‐based standard errors, ESE is the empirical standard error, and Cov is the coverage rates of the 95% Wald confidence intervals.</p>
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<note type="content"> Disclaimer: Supplementary materials have been peer‐reviewed but not copyedited.Supporting Info Item: Table S1 Simulation results on censoring time following a proportional odds model; the data were simulated from the modified logistic or Gompertz base with a proportional subdistribution hazard transformation (LOG+PSH and GOM+PSH) or with a generalized odds‐rate transformation (LOG + GOR and GOM + GOR); referring to Table 1 for the definition of AVE, MSE, ESE, and Cov. Table S2 Simulation results where the data were simulated from our proposed modified logistic (panel LOG + PSH) or Gompertz model (panel GOM + PSH) with complimentary log–log transformation or with generalized odds‐rate transformation (panels LOG + GOR and GOM + GOR) with sample size n=500 and 40% censoring rate, where AVE is the average of the estimates, MSE is the average of the model‐based standard errors, ESE is the empirical standard error, and Cov is the coverage rates of the 95% Wald confidence intervals. - Supporting Information - </note>
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Constrained parametric model for simultaneous inference of two cumulative incidence functions

Constrained parametric model for simultaneous inference of two cumulative incidence functions

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