Model Selection and Semiparametric Inference for Bivariate Failure-Time Data

Wang, Weijing; Wells, Martin T.

doi:10.2307/2669523

Cited by 88 publications

(100 citation statements)

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“…In practice, one would evaluate a number of copulas (e.g., Clayton, Hougaard, Plackett) for a given dataset, through preliminary (not necessarily meta-analytic) goodness-of-fit examinations. Such copula selection methods have been widely discussed elsewhere (see, e.g., Wang and Wells (2000) or Genest, Rmillard, and Beaudoin (2009) for an overview); here, we focus attention on the Clayton copula previously utilized and published for these data, and provide a re-analysis to include both traditional (survival-based) and alternative proposed (CDF-based) approaches.…”

Section: Example: Evaluation Of Dfs As Surrogate For Os In Accent Tmentioning

confidence: 99%

Impact of Copula Directional Specification on Multi-Trial Evaluation of Surrogate End Points

Renfro

Shang

Sargent

2014

Journal of Biopharmaceutical Statistics

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Evaluation of surrogate endpoints using patient-level data from multiple trials is the gold standard, where multi-trial copula models are used to quantify both patient-level and trial-level surrogacy. While limited consideration has been given in the literature to copula choice (e.g., Clayton), no prior consideration has been given to direction of implementation (via survival versus distribution functions). We demonstrate that evenwith the “correct” copula family, directional misspecification leads to biased estimates of patient-level and trial-level surrogacy. We illustrate with a simulation study and a re-analysis of disease-free survival as a surrogate for overall survival in early stage colon cancer.

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Section: Example: Evaluation Of Dfs As Surrogate For Os In Accent Tmentioning

confidence: 99%

Impact of Copula Directional Specification on Multi-Trial Evaluation of Surrogate End Points

Renfro

Shang

Sargent

2014

Journal of Biopharmaceutical Statistics

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“…We are not able to recommend a choice of copula, but a graphical method discussed in Wang and Wells may help the reader in this respect [19]. One advantage of using parametric copula is that they are flexible tools and can describe the dependence between two endpoints by a single parameter.…”

Section: Discussionmentioning

confidence: 99%

Adjustment on the Type I Error Rate for a Clinical Trial Monitoring for both Intermediate and Primary Endpoints

Halabi¹

2013

J Biomet Biostat

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In many clinical trials, a single endpoint is used to answer the primary question and forms the basis for monitoring the experimental therapy. Many trials are lengthy in duration and investigators are interested in using an intermediate endpoint for an accelerated approval, but will rely on the primary endpoint (such as, overall survival) for the full approval of the drug by the Food and Drug Administration. We have designed a clinical trial where both intermediate (progression-free survival, (PFS)) and primary endpoints (overall survival, (OS)) are used for monitoring the trial so the overall type I error rate is preserved at the pre-specified alpha level of 0.05. A two-stage procedure is used. In the first stage, the Bonferroni correction was used where the global type I error rate was allocated to each of the endpoints. In the next stage, the O’Brien-Fleming approach was used to design the boundary for the interim and final analysis for each endpoint. Data were generated assuming several parametric copulas with exponential marginals. Different degrees of dependence, as measured by Kendall’s τ, between OS and PFS were assumed: 0 (independence) 0.3, 0.5 and 0.70. This approach is applied to an example in a prostate cancer trial.

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“…For complete data, we refer to Chen and Fan (2005, 2006a) for a detailed discussion of existing approaches and references. For bivariate censored data, existing work include Frees and Valdez (1998), Klugman and Parsa (1999), Wang and Wells (2000), Chen and Fan (2007), and Denuit et al (2006). Frees and Valdez (1998) and Klugman and Parsa (1999) consider fully parametric models of bivariate distribution (or survival) functions, and they address model selection of parametric copulas and parametric marginals for insurance company data on losses and allocated loss adjustment expenses (ALAEs).…”

Section: Introductionmentioning

confidence: 99%

“…Using various model selection techniques including AIC/BIC, Frees and Valdez (1998) select the Pareto marginal distributions and the Gumbel copula, while Klugman and Parsa (1999) select inverse paralogistic for loss marginal distribution, inverse Burr for ALAE marginal distribution and the Frank copula. Wang and Wells (2000), Denuit et al (2006) and Chen and Fan (2007) consider model selection of semiparametric bivariate distribution (or survival) functions in which they do not specify marginals, but restrict the parametric copulas to be in the Archimedean family. In particular, Wang and Wells (2000) propose a model selection procedure for comparing copulas in the one-parameter Archimedean family, allowing for various censoring mechanisms, as long as a consistent nonparametric estimator for the bivariate joint distribution (or survival) function is available.…”

Section: Introductionmentioning

confidence: 99%

“…Wang and Wells (2000), Denuit et al (2006) and Chen and Fan (2007) consider model selection of semiparametric bivariate distribution (or survival) functions in which they do not specify marginals, but restrict the parametric copulas to be in the Archimedean family. In particular, Wang and Wells (2000) propose a model selection procedure for comparing copulas in the one-parameter Archimedean family, allowing for various censoring mechanisms, as long as a consistent nonparametric estimator for the bivariate joint distribution (or survival) function is available. Their selection procedure is based on comparing point estimates of the integrated squared difference between the true Archimedean copula and a parametric copula; the one with the smallest value of the integrated squared difference is chosen over the rest of the one-parameter Archimedean copulas.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and model selection of semiparametric multivariate survival functions under general censorship

Chen

Fan

Pouzo

et al. 2010

Journal of Econometrics

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We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

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Model Selection and Semiparametric Inference for Bivariate Failure-Time Data

Cited by 88 publications

References 0 publications

Impact of Copula Directional Specification on Multi-Trial Evaluation of Surrogate End Points

Impact of Copula Directional Specification on Multi-Trial Evaluation of Surrogate End Points

Adjustment on the Type I Error Rate for a Clinical Trial Monitoring for both Intermediate and Primary Endpoints

Estimation and model selection of semiparametric multivariate survival functions under general censorship

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