Modeling Survival Data: Extending the Cox Model

By R. Gray. 2002

1,695 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1ae7218ebdba84549f12828eeffbdc4fb7f67c67

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Citations in the corpus

Multi-Period Corporate Default Prediction With Stochastic Covariates (2007, positive): “Our method of construction of this covariate, based on market equity data and Compustat book liability data, is along the lines of that used by Vassalou and Xing (2004), Crosbie and Bohn (2002), and Hillegeist, Keating, Cram, and Lundstedt (2004), although Bharath and Shumway (2004) point out that d”
Survival rates and causes of mortality of leopards Panthera pardus in southern Africa (2014, methodology): “We used semi-parmetric Cox proportional-hazard models, rather than full parametric models, to calculate age- and sex-specific survival in non-protected and protected areas and to evaluate the effects of conservation status, age and sex on survival (Therneau & Grambsch, 2010).”
Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data (2004, methodology): “Based on the Kaplan-Meier survival curve for all the patients, we can estimate the probability that a censored case survives a speciﬁed length of time (Cox and Oakes 1984; Therneau and Grambsch 2000).”
COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece (2021, methodology): “The above‐mentioned statistical tests (Table 3) as well as the Cox Proportional Hazards model were performed using the R programming software (R Core Team, 2020) and the packages survival (Therneau and Grambsch, 2000), Imtest (Hothorn et al., 2007) and dplyr (Wickham et al., 2020).”
Tutorial in biostatistics: competing risks and multi‐state models (2007, background): “Several of the ideas presented in the sections on competing risks and multi-state models can also be found in Reference [1].”
Disease, predation and demography: Assessing the impacts of bovine tuberculosis on African buffalo by monitoring at individual and population levels (2009, methodology): “…version of a Cox proportional hazards (CPH) approach to assess survival differences between BTB-positive and negative individuals (Cox 1972; Therneau & Grambsch 2000), thereby removing the confounding effects of seasonal fluctuations by allowing the baseline mortality hazard to vary freely…”
Semiparametric Transformation Models With Random Effects for Recurrent Events (2007, neutral): “Therneau and Grambsch (2000) proposed fitting the proportional intensity model with normal random effects by the penalized partial likelihood method, but provided no formal theoretical justifications.”
Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries (2017, positive): “Kaplan-Meier curves were made in R using the survival package (43).”
Elastic Net Regularization Paths for All Generalized Linear Models (2021, methodology): “As noted in Therneau and Grambsch (2000), (start, stop] responses greatly increase the flexibility of the Cox model, allowing for • Time-dependent covariates, • Time-dependent strata, • Left truncation, • Multiple time scales, • Multiple events per subject, • Independent increment, marginal, and…”
Mixed Effects Cox Models (2015, background): “For further details see chapter 3 of Therneau and Grambsch [5] or any other mid-level survival text.”
Zelig: Everyone's Statistical Software (2006, background): “The authors of the following R packages have provided some of the models available through Zelig: MASS by William N. Venables and Brian D. Ripley; MCMCpack by Andrew D. Martin and Kevin M. Quinn; survival by Terry Therneau and Brian D. Ripley; and VGAM by Thomas Yee.”
An Introduction to Survival Analysis using Stata (2005, methodology): “An obvious comparison of this book is to Therneau and Grambsch (2000), whose first author wrote the very similar survival analysis implementation in S-PLUS and R.”
Validity of the Agency for Healthcare Research and Quality clinical practice guidelines: how quickly do guidelines become outdated? (2001, background): “In brief, we identified 6 situations that may require a guideline to be updated (or withdrawn), including changes in (1) the available interventions, (2) the evidence on the benefits and harms of existing interventions, (3) the outcomes that are considered important, (4) the evidence that current pr”