Comparing conditional survival functions with missing population marks in a competing risks model
DOI10.1016/j.csda.2015.10.001zbMath1468.62022OpenAlexW1805075981WikidataQ36462234 ScholiaQ36462234MaRDI QIDQ1659490
Dipankar Bandyopadhyay, María Amalia Jácome
Publication date: 15 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc4712751
Computational methods for problems pertaining to statistics (62-08) Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Testing in survival analysis and censored data (62N03)
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Cites Work
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- Using integrated weighted survival difference for the two-sample censored data problem
- Estimating future stage entry and occupation probabilities in a multistage model based on randomly right-censored data
- A large sample study of the life table and product limit estimates under random censorship
- Survival analysis. Techniques for censored and truncated data.
- Kaplan-Meier representation of competing risk estimates
- Testing equality of survival distributions when the population marks are missing
- Testing dependence between the failure time and failure modes: an application of enlarged filtration
- NONPARAMETRIC ESTIMATION OF CONDITIONAL CUMULATIVE HAZARDS FOR MISSING POPULATION MARKS
- A modified log rank test for competing risks with missing failure type
- Using Weighted Kaplan-Meier Statistics in Nonparametric Comparisons of Paired Censored Survival Outcomes
- Maximum of the Weighted Kaplan-Meier Tests with Application to Cancer Prevention and Screening Trials
- Maximum of the weighted Kaplan–Meier tests for the two-sample censored data
- Weighted Kaplan-Meier Statistics: A Class of Distance Tests for Censored Survival Data
- Nonparametric Estimation from Incomplete Observations
- Analysis of competing risks survival data when some failure types are missing
- Censored Data and the Bootstrap
- A nonidentifiability aspect of the problem of competing risks.
- On distribution-free tests for equality of survival distributions
- Monitoring a general class of two-sample survival statistics with applications
- Some Tests for Comparing Cumulative Incidence Functions and Cause-Specific Hazard Rates
- Restricted tests for testing independence of time to failure and cause of failure in a competing-risks model
- Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure
- On Testing Dependence between Time to Failure and Cause of Failure via Conditional Probabilities
- A generalized Wilcoxon test for comparing arbitrarily singly-censored samples
- A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship
- Statistical models based on counting processes
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