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.

scholarly journals Rotation survival forest for right censored data

2015 ◽  
Author(s):  
Lifeng Zhou ◽  
Qingsong Xu ◽  
Hong Wang
Keyword(s):  
Censored Data ◽  
Survival Data ◽  
Importance Measure ◽  
Event Data ◽  
Time To Event ◽  
Right Censored Data ◽  
Rotation Forest ◽  
Censored Survival Data ◽  
Time To Event Data ◽  
Survival Forests

Recently, survival ensembles have found more and more applications in biological and medical research when censored time-to-event data are often confronted. In this research, we investigate the plausibility of extending rotation forest, originally proposed for classification purpose, to survival analysis. Supported by the proper statistical analysis, we show that rotation survival forests are able to outperform the state-of-art survival ensembles on right censored data. We also provide a C-index based variable importance measure for evaluating covariates in censored survival data.

scholarly journals Factorial analyses of treatment effects under independent right-censoring

2019 ◽  
Vol 29 (2) ◽  
pp. 325-343
Author(s):  
Dennis Dobler ◽  
Markus Pauly
Keyword(s):  
Asymptotic Properties ◽  
Small Sample ◽  
Right Censoring ◽  
Event Data ◽  
Time To Event ◽  
Sample Design ◽  
Time To Event Data ◽  
Practical Usefulness ◽  
Simulation Results ◽  
Special Case

This paper introduces new effect parameters for factorial survival designs with possibly right-censored time-to-event data. In the special case of a two-sample design, it coincides with the concordance or Wilcoxon parameter in survival analysis. More generally, the new parameters describe treatment or interaction effects and we develop estimates and tests to infer their presence. We rigorously study their asymptotic properties and additionally suggest wild bootstrapping for a consistent and distribution-free application of the inference procedures. The small sample performance is discussed based on simulation results. The practical usefulness of the developed methodology is exemplified on a data example about patients with colon cancer by conducting one- and two-factorial analyses.

scholarly journals Proposals on Kaplan–Meier plots in medical research and a survey of stakeholder views: KMunicate

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e030215 ◽  
Author(s):  
Tim P Morris ◽  
Christopher I Jarvis ◽  
William Cragg ◽  
Patrick P J Phillips ◽  
Babak Choodari-Oskooei ◽  
...  
Keyword(s):  
Outcome Measures ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Time To Event Data ◽  
Kaplan Meier ◽  
Starting Point ◽  
Wide Range ◽  
International Participation ◽  
Over Time

ObjectivesTo examine reactions to the proposed improvements to standard Kaplan–Meier plots, the standard way to present time-to-event data, and to understand which (if any) facilitated better depiction of (1) the state of patients over time, and (2) uncertainty over time in the estimates of survival.DesignA survey of stakeholders’ opinions on the proposals.SettingA web-based survey, open to international participation, for those with an interest in visualisation of time-to-event data.Participants1174 people participated in the survey over a 6-week period. Participation was global (although primarily Europe and North America) and represented a wide range of researchers (primarily statisticians and clinicians).Main outcome measuresTwo outcome measures were of principal importance: (1) participants’ opinions of each proposal compared with a ‘standard’ Kaplan–Meier plot; and (2) participants’ overall ranking of the proposals (including the standard).ResultsMost proposals were more popular than the standard Kaplan–Meier plot. The most popular proposals in the two categories, respectively, were an extended table beneath the plot depicting the numbers at risk, censored and having experienced an event at periodic timepoints, and CIs around each Kaplan–Meier curve.ConclusionsThis study produced a high response number, reflecting the importance of graphics for time-to-event data. Those producing and publishing Kaplan–Meier plots—both authors and journals—should, as a starting point, consider using the combination of the two favoured proposals.

scholarly journals Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective

2016 ◽  
Vol 27 (4) ◽  
pp. 1258-1270 ◽  
Author(s):  
Huirong Zhu ◽  
Stacia M DeSantis ◽  
Sheng Luo
Keyword(s):  
Survival Data ◽  
Proportional Hazards Model ◽  
Joint Modeling ◽  
Cox Proportional Hazards Model ◽  
Alpha Tocopherol ◽  
Repeated Measure ◽  
Terminal Event ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-event data by using a Cox proportional hazards model with piecewise constant baseline hazard. We use a Bayesian framework via Markov chain Monte Carlo simulations implemented in the BUGS programming language. Via an extensive simulation study, we apply the joint model and obtain estimates that are more accurate than those of the corresponding independence model. We apply the proposed method to an alpha-tocopherol, beta-carotene lung cancer prevention study.

scholarly journals An Updated Algorithm for Moderate Censoring in Time-to-Event Data Using Rank-based Regression

2019 ◽  
pp. 1-18
Author(s):  
Milind A. Phadnis
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Real Life ◽  
Dispersion Function ◽  
Event Data ◽  
Time To Event ◽  
Karnofsky Score ◽  
Time To Event Data

Aim: To propose an updated algorithm with an extra step added to the Newton-type algorithm used in robust rank based non-parametric regression for minimizing the dispersion function associated with Wilcoxon scores in order to account for the effect of covariates. Methodology: The proposed accelerated failure time approach is aimed at incorporating right random censoring in survival data sets for low to moderate levels of censoring. The existing Newton algorithm is modified to account for the effect of one or more covariates. This is done by first applying Mantel scores to residuals obtained from a regression model, and second by minimizing the dispersion function of these scored residuals. Diagnostic check of the model fit is performed by observing the distribution of the residuals and suitable Bent scores are considered in the case of skewed residuals. To demonstrate the efficacy of this method, a simulation study is conducted to compare the power of this method under three different scenarios: non-proportional hazard, proportional and constant hazard, and proportional but non-constant hazard. Results: In most situations, this method yielded reasonable estimates of power for detecting an association of the covariate with the response as compared to popular parametric and semi-parametric approaches. The estimates of the regression coefficient obtained from this method were evaluated and were found to have low bias, low mean square error, and adequate coverage. In a real-life example pertaining to pancreatic cancer study, the proposed method performed admirably well and provided a more realistic interpretation about the effect of covariates (age and Karnofsky score) compared to a standard parametric (lognormal) model. Conclusion: In situations where there is no clear best parametric fit for time-to-event data with moderate level of censoring, the proposed method provides a robust alternative to obtain regression coefficients (both adjusted and unadjusted) with a performance comparable to that of a proportional hazards model.

scholarly journals A novel estimator of survival without assumption of censored survival time

2021 ◽  
Author(s):  
Shee-Ping Chen
Keyword(s):  
Survival Time ◽  
Survival Function ◽  
Event Data ◽  
Time To Event ◽  
Survival Probabilities ◽  
Time To Event Data ◽  
Kaplan Meier

Abstract The Kaplan-Meier estimator is commonly used to analyze time-to-event data, but it may over estimate the survival function when censored events occur. Several methods have been developed to adjust the traditional Kaplan-Meier estimator. Most of these adjusted methods are based on their assumptions for survival time of censored events. We here propose a novel estimator of survival without assumption of censored survival time, and it gives sensible estimates of survival probabilities in various censoring conditions.

scholarly journals Rotation survival forest for right censored data

2015 ◽  
Author(s):  
Lifeng Zhou ◽  
Qingsong Xu ◽  
Hong Wang
Keyword(s):  
Censored Data ◽  
Survival Data ◽  
Importance Measure ◽  
Event Data ◽  
Time To Event ◽  
Right Censored Data ◽  
Rotation Forest ◽  
Censored Survival Data ◽  
Time To Event Data ◽  
Survival Forests

Recently, survival ensembles have found more and more applications in biological and medical research when censored time-to-event data are often confronted. In this research, we investigate the plausibility of extending rotation forest, originally proposed for classification purpose, to survival analysis. Supported by the proper statistical analysis, we show that rotation survival forests are able to outperform the state-of-art survival ensembles on right censored data. We also provide a C-index based variable importance measure for evaluating covariates in censored survival data.

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