On the Use of Cause-Specific Failure and Conditional Failure Probabilities: Examples From Clinical Oncology Data
From MaRDI portal
Publication:5288901
DOI10.2307/2290318zbMath0775.62297OpenAlexW4249492944MaRDI QIDQ5288901
Danny H. Wu, David J. Straus, Bayard D. Clarkson, Claire C. Tan, Murray F. Brennan, Claudia R. Little, Jeffrey J. Gaynor, Eric J. Feuer
Publication date: 7 September 1993
Full work available at URL: https://doi.org/10.2307/2290318
Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)
Related Items
Semiparametric estimation for accelerated failure time mixture cure model allowing non-curable competing risk, Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk, Statistical methods for dependent competing risks, A SURVIVAL ANALYSIS INCORPORATING AUXILIARY INFORMATION BY A BAYESIAN GENERALIZED METHOD OF MOMENTS: APPLICATION TO PURCHASE DURATION MODELING, Statistical inference methods for cumulative incidence function curves at a fixed point in time, A Bayesian multi‐risks survival (MRS) model in the presence of double censorings, Kernel regression for cause-specific hazard models with nonparametric covariate functions, Inference for cumulative incidence on discrete failure times with competing risks, Kernel regression for cause-specific hazard models with time-dependent coefficients, Bayesian approach for proportional hazards mixture cure model allowing non-curable competing risk, RELIABILITY STUDIES OF BIVARIATE BIRNBAUM–SAUNDERS DISTRIBUTION, Proportional hazards model for competing risks data with missing cause of failure, Semiparametric estimation based on parametric modeling of the cause-specific hazard ratios in competing risks, Unnamed Item, A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data, Nonparametric inference on cause‐specific quantile residual life, Constrained parametric model for simultaneous inference of two cumulative incidence functions, Reliability studies of bivariate distributions with exponential conditionals, Estimating Subject-Specific Dependent Competing Risk Profile with Censored Event Time Observations, Assessing diagnostic accuracy improvement for survival or competing‐risk censored outcomes, Nonparametric inference under competing risks and selection-biased sampling, Reliability Functions of Bivariate Distributions in Modeling Marked Point Processes, A competing risks analysis of the duration of federal target funds rates, Reliability studies of bivariate distributions with Pareto conditionals, A Note on Variance Estimation of the Aalen-Johansen Estimator of the Cumulative Incidence Function in Competing Risks, with a View towards Left-Truncated Data, Direct Parametric Inference for the Cumulative Incidence Function, Nonparametric quantile inference for cause-specific residual life function under length-biased sampling, Inference for the dependent competing risks model with masked causes of failure, Competing Risks Analysis of Correlated Failure Time Data, Fitting a Semi-Parametric Mixture Model for Competing Risks in Survival Data, Dependent competing risks: cause elimination and its impact on survival, Competing Risks and Multistate Models with R. By J. Beyersmann, M. Schumacher A. Allignol. New York, NY: Springer. 2012. 245 pages. €49.95 (hardback). ISBN 978-1-4614-2034-7., Bayesian inference of the fully specified subdistribution model for survival data with competing risks, Non-proportionality of Hazards in the Competing Risks Framework, Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood, Accelerated failure time models for the analysis of competing risks, Estimation with right-censored observations under a semi-Markov model
