General Right Censoring and Its Impact on the Analysis of Survival Data

Biometrics ◽  
1979 ◽  
Vol 35 (1) ◽  
pp. 139 ◽  
Author(s):  
S. W. Lagakos
Keyword(s):  
Survival Data ◽  
Right Censoring

A Practical Guide to Family Studies with Lifetime Data

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Age Of Onset ◽  
Familial Aggregation ◽  
Threshold Model ◽  
Right Censoring ◽  
Annual Review ◽  
Publication Date ◽  
Lifetime Data ◽  
Competing Risks Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Nadarajah-Haghighi Model for Survival Data With Long Term Survivors in the Presence of Right Censored Data

2021 ◽  
pp. 695-709
Author(s):  
Umar Usman ◽  
Shamsuddeen Suleiman ◽  
Bello Magaji Arkilla ◽  
Yakubu Aliyu
Keyword(s):  
Survival Data ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Right Censoring ◽  
Right Censored Data ◽  
Term Survival ◽  
Proposed Model ◽  
Cure Fraction ◽  
Long Term Survivors

In this paper, a new long term survival model called Nadarajah-Haghighi model for survival data with long term survivors was proposed. The model is used in fitting data where the population of interest is a mixture of individuals that are susceptible to the event of interest and individuals that are not susceptible to the event of interest. The statistical properties of the proposed model including quantile function, moments, mean and variance were provided. Maximum likelihood estimation procedure was used to estimate the parameters of the model assuming right censoring. Furthermore, Bayesian method of estimation was also employed in estimating the parameters of the model assuming right censoring. Simulations study was performed in order to ascertain the performances of the MLE estimators. Random samples of different sample sizes were generated from the model with some arbitrary values for the parameters for 5%, 1:3% and 1:5% cure fraction values. Bias, standard error and mean square error were used as discrimination criteria. Additionally, we compared the performance of the proposed model with some competing models. The results of the applications indicates that the proposed model is more efficient than the models compared with. Finally, we fitted some models considering type of treatment as a covariate. It was observed that the covariate  have effect on the shape parameter of the proposed model.

Survival forests for data with dependent censoring

2017 ◽  
Vol 28 (2) ◽  
pp. 445-461 ◽  
Author(s):  
Hoora Moradian ◽  
Denis Larocque ◽  
François Bellavance
Keyword(s):  
Survival Data ◽  
Survival Function ◽  
Real Data ◽  
Dependent Censoring ◽  
Right Censoring ◽  
True Time ◽  
Splitting Rule ◽  
The Individual ◽  
Individual Trees ◽  
Survival Forests

Tree-based methods are very powerful and popular tools for analysing survival data with right-censoring. The existing methods assume that the true time-to-event and the censoring times are independent given the covariates. We propose different ways to build survival forests when dependent censoring is suspected, by using an appropriate estimator of the survival function when aggregating the individual trees and/or by modifying the splitting rule. The appropriate estimator used in this paper is the copula-graphic estimator. We also propose a new method for building survival forests, called p-forest, that may be used not only when dependent censoring is suspected, but also as a new survival forest method in general. The results from a simulation study indicate that these modifications improve greatly the estimation of the survival function in situations of dependent censoring. A real data example illustrates how the proposed methods can be used to perform a sensitivity analysis.

scholarly journals Methods to Analyse Time-to-Event Data: The Kaplan-Meier Survival Curve

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Graziella D’Arrigo ◽  
Daniela Leonardis ◽  
Samar Abd ElHafeez ◽  
Maria Fusaro ◽  
Giovanni Tripepi ◽  
...  
Keyword(s):  
Survival Data ◽  
Molecular Mechanisms ◽  
Survival Curve ◽  
Interval Censoring ◽  
Right Censoring ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Kaplan Meier ◽  
Meier Survival Curve

Studies performed in the field of oxidative medicine and cellular longevity frequently focus on the association between biomarkers of cellular and molecular mechanisms of oxidative stress as well as of aging, immune function, and vascular biology with specific time to event data, such as mortality and organ failure. Indeed, time-to-event analysis is one of the most important methodologies used in clinical and epidemiological research to address etiological and prognostic hypotheses. Survival data require adequate methods of analyses. Among these, the Kaplan-Meier analysis is the most used one in both observational and interventional studies. In this paper, we describe the mathematical background of this technique and the concept of censoring (right censoring, interval censoring, and left censoring) and report some examples demonstrating how to construct a Kaplan-Meier survival curve and how to apply this method to provide an answer to specific research questions.

Exploring the Effects of Early Censoring and Analysis of Clinical Trial Survival Data on Effectiveness and Cost-effectiveness Estimation through a Case Study in Advanced Breast Cancer

2018 ◽  
Vol 38 (7) ◽  
pp. 789-796 ◽  
Author(s):  
Adrian Bagust ◽  
Sophie J. Beale
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Overall Survival ◽  
Survival Data ◽  
Relative Effectiveness ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Right Censoring ◽  
New Drugs ◽  
Interim Analyses

Interim analyses of clinical trial data are frequently used to provide evidence to obtain marketing authorization for new drugs. However, results from such analyses may not reflect true estimates of relative effectiveness when trial follow-up is complete. Survival results, available at 2 time points from a breast cancer clinical trial, were compared to test the hypothesis that using immature data and a widely used right-censoring rule leads to biased survival estimates. Kaplan-Meier progression-free and overall survival data from 2 published CLEOPATRA trial reports (2012 and 2014) were digitized. Overlaying these results highlighted divergent trends. Parametric functions were fitted to both data sets but did not indicate consistent patterns that could be used as a basis for long-term extrapolation. Heavy censoring of patients in the early data cut coincides with sudden changes in hazard trends and survival patterns, supporting the hypothesis of censoring bias. This challenges the validity of estimates of clinical benefit (progression-free survival and overall survival) based on extrapolation of results from interim analyses of trial data, using a commonly employed censoring rule.

scholarly journals Giniinc: A Stata Package for Measuring Inequality from Incomplete Income and Survival Data

2018 ◽  
Vol 18 (3) ◽  
pp. 692-715
Author(s):  
Long Hong ◽  
Guido Alfani ◽  
Chiara Gigliarano ◽  
Marco Bonetti
Keyword(s):  
Censored Data ◽  
Survival Data ◽  
Gini Index ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Right Censoring ◽  
Parametric Models ◽  
Test Statistic ◽  
Right Censored Data ◽  
Nonparametric Bounds

Often, observed income and survival data are incomplete because of left- or right-censoring or left- or right-truncation. Measuring inequality (for instance, by the Gini index of concentration) from incomplete data like these will produce biased results. We describe the package giniinc, which contains three independent commands to estimate the Gini concentration index under different conditions. First, survgini computes a test statistic for comparing two (survival) distributions based on the nonparametric estimation of the restricted Gini index for right-censored data, using both asymptotic and permutation inference. Second, survbound computes nonparametric bounds for the unrestricted Gini index from censored data. Finally, survlsl implements maximum likelihood estimation for three commonly used parametric models to estimate the unrestricted Gini index, both from censored and truncated data. We briefly discuss the methods, describe the package, and illustrate its use through simulated data and examples from an oncology and a historical income study.

Semiparametric Analysis of Survival Data with Left Truncation and Dependent Right Censoring

Biometrics ◽  
2005 ◽  
Vol 61 (2) ◽  
pp. 567-575 ◽  
Author(s):  
Hongyu Jiang ◽  
Jason P. Fine ◽  
Rick Chappell
Keyword(s):  
Survival Data ◽  
Right Censoring ◽  
Left Truncation ◽  
Semiparametric Analysis

scholarly journals HYPOTHESIS TESTING FOR THE PARAMETERS OF LOG-LOGISTIC REGRESSION MODEL WITH LEFT-TRUNCATED AND RIGHT-CENSORED SURVIVAL DATA

2018 ◽  
Vol 80 (3) ◽  
Author(s):  
Wan Nur Atikah Wan Mohd Adnan ◽  
Jayanthi Arasan
Keyword(s):  
Survival Data ◽  
Logistic Model ◽  
Right Censoring ◽  
Parameter Estimates ◽  
Truncation Level ◽  
Censored Survival Data ◽  
Proposed Model ◽  
The Right ◽  
Data Log ◽  
Better Than

Left-truncation and right-censoring (LTRC) arise naturally in lifetime data. Data may be left-truncated due to a limitation in the study design. Failure of a unit is observed only if it fails after a certain period. Usually, the units under study may not be followed until all of them have failed but the study has to be stopped at a certain time. This introduces the right censoring into the survival data.  Log-logistic model is extended to accommodate the left-truncated and right-censored survival data.  The bias, standard error (SE), and root mean square error (RMSE) of the parameter estimates are computed to evaluate the performance of the model at different sample sizes, censoring proportion (CP), and truncation level (TL). The results show that the SE of the parameter estimates increase as the truncation level (TL) and censoring proportion (CP) increase. Having low and high TL (5% and 15%) in the data, the graphs clearly show that the empirical power of both tests increases with the increase of TL for parameter and . The SE and RMSE also decrease as the sample size increases. Following that, power analysis is conducted via simulation to compare the performance of hypothesis tests based on the Wald and Likelihood Ratio (LR) for the parameters. The results clearly indicate that the Wald performs slightly better than the LR when dealing with the proposed model.

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