scholarly journals Features of survival analysis on patients on the «waiting list» for kidney transplantation

2019 ◽  
Vol 18 (2) ◽  
pp. 215-222 ◽  
Author(s):  
A. B. Zulkarnaev
Keyword(s):  
Survival Analysis ◽  
Kidney Transplantation ◽  
Statistical Analysis ◽  
Regression Model ◽  
Competing Risks ◽  
Waiting Time ◽  
Cumulative Incidence ◽  
Waiting List ◽  
Regression Coefficients ◽  
Competing Events

Survival analysis is one of the most common methods of statistical analysis in medicine. The statistical analysis of the transplantation (or death) probability dependent on the waiting time on the "waiting list" is a rare case when the survival analysis is used to estimate the time before the event rather than to indirectly assess the risks. However, for an assessment to be adequate, the reason for censoringmust be independent of the outcome of interest. Patients on the waiting list are not only at risk of dying, they can be excluded from the waiting list due to deterioration of the comorbid background or as a result of kidney transplantation. Kaplan – Meier, Nelson – Aalen estimates, as well as a cause-specific Cox proportional hazards regression model, are consciously biased estimates of survival in the presence of competing risks. Since competing events are censored, it is impossible to directly assess the impact of covariates on their frequency, because there is no direct relationship between the regression coefficients and the intensity of these events. The determination of the median waiting time on the basis of such analysis generates a selection bias, which inevitably leads to a biased assessment. Thus, in presence of competing risks, these methods allow us to investigate the features of cause-and-effect relationships, but do not allow us to make a prediction of the individual probability of a particular event based on the value of its covariates. In the regression model of competing risks, the regression coefficients are monotonically related to the cumulative incidence function and the competing events have a direct impact on the regression coefficients. Its significant advantage is the additive nature of the cumulative incidence functions of all possible events. In the study of etiological associations, it is better to use Cox regression model, which allows to estimate the size of the effect of various factors. The regression model of competing risks, in turn, has a greater prognostic value and allows to estimate the probability of a specific outcome within a certain time in a single patient.

P1659SURVIVAL OF PATIENTS WAITING FOR KIDNEY TRANSPLANTATION IN TERMS OF COMPETING RISKS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Aleksei Zulkarnaev ◽  
Vadim Stepanov ◽  
Andrey Vatazin
Keyword(s):  
Kidney Transplantation ◽  
Competing Risks ◽  
Waiting Time ◽  
Cumulative Incidence ◽  
Graft Loss ◽  
Waiting List ◽  
Waiting Period ◽  
Risk Of Death ◽  
End Point ◽  
Kaplan Meier

Abstract Background and Aims In Russia, when choosing a donor-recipient pair, the “waiting time” factor has very little weight. Aim: to analyze the survival of patients on the waiting list for kidney transplantation and the results of transplantation depending on the duration of waiting. Method We performed a retrospective observational analysis that included 1,197 patients on the waiting list. The end point was exclusion from the waiting list (WL). The causes for exclusion (death, exclusion due to deterioration of the comorbid background or transplantation) were considered in terms of competing risks. Results In total, 72.5% of patients reached the end point: 21.1% of them died, 11% were excluded, and 40.4% underwent transplantation. Kaplan-Meier estimate showed that cumulative risk of death was 80.4% [95% CI 77.9; 88.6], of exclusion – 77.9% [95% CI 65.4; 88.2], of transplantation – 63.6% [95% CI 58.3; 69] after 10 years on the waiting list. However, such an assessment cannot be directly interpreted as a prediction of the relevant event risk for the patient in the WL, because it does not take into account competing events. According to a balanced assessment of the competing risks (Fine and Gray regression model), cumulative incidence was 30.9% [95% CI 27.7; 34.2] for death, 18.2% [95% CI 15.5; 21.1] for exclusion and 49.4% [95% CI 46; 52.6] for transplantation after 10 years on WL. The probability of transplantation was significantly higher than the risk of death up to and including 5 years of waiting (incidents rate ratio – IRR 1.769 [95% CI 1.098; 2.897]). When waiting 7 to 8 years, the probability of transplantation was less than the risk of death: IRR 0.25 (95% CI 0.093; 0.588; p=0.0009) – fig 1. Of the 483 recipients, 61 died and 119 returned to dialysis. The risk of graft loss after 10 years was 68.5% [95% CI 57.5; 79.1] and the risk of death of a recipient with a functioning graft was 48.3% [95% CI 34.7; 63] according to Kaplan-Meier estimate. The cumulative incidence was 30.8% [95% CI 23.3; 38.5] and 55.7% [95% CI 46.6; 63.5] according to Fine and Gray estimate, respectively. The risk of death after transplantation increases significantly when waiting for more than 6 years – IRR 4.325 [95% CI 1.649; 10.47], p=0.0045 relative to a shorter waiting period – fig 2. With an increase in the waiting period, the comorbid background (CIRS scale) deteriorates significantly, even adjusted for the initial patient condition: the partial correlation r=0.735; p<0.0001. The deteriorating comorbid background reduces the expediency of transplantation – fig 3 (the potential patient`s benefit is significantly reduced). Conclusion : 1. In the context of competing risks, the Fine and Gray estimate gives a more balanced risk assessment compared to the Kaplan-Meier method. 2. Increasing the waiting time for transplantation significantly increases the risk of death of the candidate on the waiting list and reduces the probability of transplantation, as well as increases the risk of death of the recipient after transplantation. Apparently, this is mainly due to the deterioration of the comorbid background.

scholarly journals Survival analysis of patients in the waiting list for kidney transplantation in terms of competing risks

2019 ◽  
Vol 21 (1) ◽  
pp. 35-45 ◽  
Author(s):  
A. V. Vatazin ◽  
A. B. Zulkarnaev ◽  
V. A. Stepanov
Keyword(s):  
Kidney Transplantation ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Graft Loss ◽  
Cumulative Risk ◽  
Waiting List ◽  
Waiting Period ◽  
Risk Of Death ◽  
End Point ◽  
Kaplan Meier

Aim: to analyze the survival of patients on the waiting list for kidney transplantation and the results of transplantation depending on the duration of waiting.Materials and methods. We performed a retrospective observational analysis that included 1,197 patients on the waiting list. The end point was exclusion from the waiting list (WL). The causes for exclusion (death, exclusion due to deterioration of the comorbid background or transplantation) were considered in terms of competing risks.Results. In total, 72.5% of patients reached the end point: 21.1% of them died, 11% were excluded, and 40.4% underwent transplantation. Kaplan–Meier estimate showed that cumulative risk of death was 80.4% [95% CI 77.9; 88.6], of exclusion was 77.9% [95% CI 65.4; 88.2], of transplantation was 63.6% [95% CI 58.3; 69] after 10 years on the waiting list. However, such an assessment cannot be directly interpreted as a prediction of the relevant event risk of occurrence for the patient in the WL, because it does not take into account competing events. According to a balanced assessment of the competing risks (Fine and Gray estimate), cumulative incidence was 30.9% (95% CI 27.7; 34.2) for death, 18.2% [95% CI 15.5; 21.1] for exclusion and 49.4% [95% CI 46; 52.6%] for transplantation after 10 years on WL. The probability of transplantation was significantly higher than the risk of death up to and including 5 years of waiting (incidence rate ratio – IRR 1.769 [95% CI 1.098; 2.897]). When waiting 7 to 8 years, the probability of transplantation was less than the risk of death: IRR 0.25 (95% CI 0.093; 0.588; p = 0.0009). Of the 483 recipients, 61 died and 119 returned to dialysis. The risk of graft loss after 10 years was 68.5% [95% CI 57.5; 79.1] and the risk of death of a recipient with a functioning graft was 48.3% [95% CI 34.7; 63] according to Kaplan–Meier estimate. The cumulative incidence of the method was 30.8% [95% CI 23.3; 38.5%] and 55.7% [95% CI 46.6; 63.5%] according to Fine and Gray estimate, respectively. The risk of death after transplantation increases significantly when waiting for more than 6 years – IRR 4.325 [95% CI 1.649; 10.47], p = 0.0045 relative to a shorter waiting period. With an increase in the waiting period, the comorbid background (CIRS scale) deteriorates significantly, even adjusted for the initial patient condition: the partial correlation r = 0.735; p < 0.0001.Conclusion. 1. In the context of competing risks, the Fine and Gray estimate gives a more balanced risk assessment compared to the Kaplan–Meier method. 2. Increasing the waiting time for transplantation significantly increases the risk of death of the candidate on the waiting list and reduces the probability of transplantation, as well as increases the risk of death of the recipient after transplantation. Apparently, this is mainly due to the deterioration of the comorbid background.

Estimating sample size in the presence of competing risks – Cause-specific hazard or cumulative incidence approach?

2015 ◽  
Vol 27 (1) ◽  
pp. 114-125 ◽  
Author(s):  
BC Tai ◽  
ZJ Chen ◽  
D Machin
Keyword(s):  
Sample Size ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Exponential Model ◽  
Sample Sizes ◽  
Subdistribution Hazard ◽  
Competing Events ◽  
Event Times ◽  
The Impact

In designing randomised clinical trials involving competing risks endpoints, it is important to consider competing events to ensure appropriate determination of sample size. We conduct a simulation study to compare sample sizes obtained from the cause-specific hazard and cumulative incidence (CMI) approaches, by first assuming exponential event times. As the proportional subdistribution hazard assumption does not hold for the CMI exponential (CMIExponential) model, we further investigate the impact of violation of such an assumption by comparing the results obtained from the CMI exponential model with those of a CMI model assuming a Gompertz distribution (CMIGompertz) where the proportional assumption is tenable. The simulation suggests that the CMIExponential approach requires a considerably larger sample size when treatment reduces the hazards of both the main event, A, and the competing risk, B. When treatment has a beneficial effect on A but no effect on B, the sample sizes required by both methods are largely similar, especially for large reduction in the main risk. If treatment has a protective effect on A but adversely affects B, then the sample size required by CMIExponential is notably smaller than cause-specific hazard for small to moderate reduction in the main risk. Further, a smaller sample size is required for CMIGompertz as compared with CMIExponential. The choice between a cause-specific hazard or CMI model in competing risks outcomes has implications on the study design. This should be made on the basis of the clinical question of interest and the validity of the associated model assumption.

scholarly journals Competing Risks Models for an Enterprises Duration on the Market

2020 ◽  
Vol 20 (1) ◽  
pp. 456-473
Author(s):  
Dominika M. Urbańczyk
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Waiting Time ◽  
Research Topic ◽  
Business Activity ◽  
Full Understanding ◽  
Kaplan Meier ◽  
Duration Distribution ◽  
The Impact ◽  
Valuable Result

AbstractResearch background: Enterprises are an important element of the economy, which explains that the analysis of their duration on the market is an important and willingly undertaken research topic. In the case of complex problems like this, considering only one type of event, which ends the duration, is often insufficient for full understanding.Purpose: In this paper there is an analysis of the duration of enterprises on the market, taking into account various reasons for the termination of their business activity as well as their characteristics.Research methodology: A survival analysis can be used to study duration on the market. However, the possibility of considering the waiting time for only one type of event is its important limitation. One solution is to use competing risks. Various competing risks models (naive Kaplan-Meier estimator, subdistribution model, subhazard and cause-specific hazard) are presented and compared with an indication of their advantages and weakness.Results: The competing risks models are estimated to investigate the impact of the causes of an enterprises liquidation on duration distribution. The greatest risk concerns enterprises with a natural person as the owner (regardless of the reason of failure). For each of the competing risks, it is also indicated that there is a section of activity which adversely affects the ability of firms to survive on the market.Novelty: A valuable result is considering the reasons for activity termination in the duration analysis for enterprises from the Mazowieckie Voivodeship.

scholarly journals The Estimation and Modeling of Cause-specific Cumulative Incidence Functions Using Time-dependent Weights

2017 ◽  
Vol 17 (1) ◽  
pp. 181-207 ◽  
Author(s):  
Paul C. Lambert
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
American Statistical Association ◽  
Survival Models ◽  
Cumulative Incidence Functions ◽  
Competing Risks Data ◽  
Incidence Functions ◽  
Competing Risk Models ◽  
Nonparametric Estimates

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event's occurrence precludes another's. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal of the American Statistical Association 94: 496–509) model for competing-risks data. Geskus (2011, Biometrics 67: 39–49) has recently shown that standard software can estimate some of the key measures in competing risks by restructuring the data and incorporating weights. This has a number of advantages because any tools developed for standard survival analysis can then be used to analyze competing-risks data. In this article, I describe the stcrprep command, which restructures the data and calculates the appropriate weights. After one uses stcrprep, a number of standard Stata survival analysis commands can then be used to analyze competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF, and stcox will fit the Fine and Gray (1999) proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, stcrprep opens up new opportunities for competing-risk models. I illustrate this by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF.

Application of survival analysis method for the examination of unemployment leaving forms

2017 ◽  
Vol 62 (8) ◽  
pp. 5-18
Author(s):  
Beata Bieszk-Stolorz
Keyword(s):  
Survival Analysis ◽  
Conditional Probability ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Cumulative Incidence Function ◽  
Analysis Method ◽  
Incidence Function ◽  
Kaplan Meier

The purpose of this article is to present selected methods of the survival analysis to evaluate the probability of leaving unemployment for the various types of competing risks. Complement to the unity of the Kaplan-Meier estimator, cumulative incidence function and cumulative conditional probability were used in the study. With these three estimators, the probability of deregistering caused by undertaking work, refusal and other causes were compared. The analysis was based on data from the Powiat Labour Office in Szczecin.

scholarly journals P1507COMPETING RISKS MODELING IN PERITONEAL DIALYSIS: THE URGE FOR A CHANGE IN SURVIVAL ANALYSIS METHODOLOGY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Anabela Malho Guedes ◽  
Roberto Calças ◽  
Ana Domingos ◽  
Teresa Jerónimo ◽  
Pedro Neves ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Peritoneal Dialysis ◽  
Renal Transplantation ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Cumulative Incidence Function ◽  
Charlson Index ◽  
Risk Of Death ◽  
Incidence Function ◽  
Kaplan Meier

Abstract Background and Aims Survival analysis is a cornerstone in medical research. For this purpose Kaplan-Meier is the most widely used statistical test, but the presence of competing risks violates the fundamental assumption that the censoring mechanism is independent of survival time. This leads to overestimation of the cumulative probability of cause-specific failure. Cumulative incidence estimate and competing risks analysis are preferred. The purpose of this study was to compare different survival analysis methods: Kaplan-Meier and cumulative incidence function estimates in a cohort of Peritoneal Dialysis (PD) patients. Method The survival of 115 incident patients on PD in a university hospital was evaluated after establishing 2 cohorts: patients starting renal replacement therapy with PD (PD first; n=85) and patients switching to PD on the first 6 months of dialysis (PD transfer; n=30). Kaplan-Meier, cumulative incidence function, cause-specific and subdistribution hazards were performed. The event of interest was death and the competing risk events were transfer to hemodialysis and renal transplantation. Results Besides higher residual renal function (RRF) and kt/V in the PD first group, there were no other significant differences between groups. There were 22 deaths. PD first group had a better survival with both Kaplan-Meier (log-rank test, p=0.013) and cumulative incidence function (p=0.021) approaches. The Cox regression model showed, as protecting variables, higher albumin (HR=0.174; CI95% 0.054-0.562), higher RRF (HR=0.785; CI95% 0.666-0.925) and PD first (HR=0.350; CI95% 0.132-0.927). Higher Charlson Index predicted worse outcome (HR=1.459; CI95% 1.159-1.835). PD as first dialysis therapy was associated with 65.0 % lower risk of death comparing with PD transfer. The subdistribution multivariable model found higher Charlson Index (HR=1.389; CI95% 1.118-1.725) and lower RRF (HR=0.798; CI95% 0.680-0.936) were statistically associated with death, but not PD transfer or albumin. This result differs from the obtained using the cause-specific hazard model. Analyzing the competing events, patients submitted to renal transplantation had a lower Charlson Index. Conclusion The probability of death was overestimated by the Kaplan-Meier method. The bias of Kaplan-Meier is especially great when the hazard of the competing risks is large. This study consisted on a statistical critical analysis of a real medical example, broader clinical conclusions related with “PD first initiative” should be cautious in this context. It is primordial to recognize the presence of competing risks in studies with multiple outcomes, as in Peritoneal Dialysis studies, to estimate cumulative incidence and yield more accurate results. This study shows how different conclusions are attained with different statistical methodology and its relevance in clinical context.

scholarly journals Semiparametric regression and risk prediction with competing risks data under missing cause of failure

2020 ◽  
Vol 26 (4) ◽  
pp. 659-684 ◽  
Author(s):  
Giorgos Bakoyannis ◽  
Ying Zhang ◽  
Constantin T. Yiannoutsos
Keyword(s):  
Risk Prediction ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Semiparametric Regression ◽  
Missing At Random ◽  
Regression Coefficients ◽  
Cumulative Incidence Functions ◽  
Hazards Model ◽  
Cause Of Failure ◽  
Incidence Functions

Abstract The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our approach is based on a novel computationally efficient maximum pseudo-partial-likelihood estimation method for the semiparametric proportional cause-specific hazards model. Using modern empirical process theory we derive the asymptotic properties of the proposed estimators for the regression coefficients and the covariate-specific cumulative incidence functions, and provide methodology for constructing simultaneous confidence bands for the latter. Simulation studies show that our estimators perform well even in the presence of a large fraction of missing cause of failures, and that the regression coefficient estimator can be substantially more efficient compared to the previously proposed augmented inverse probability weighting estimator. The method is applied using data from an HIV cohort study and a bladder cancer clinical trial.

scholarly journals Comparison between classic survival analysis methods and competing risks for analysis of competing events

Científica ◽  
2019 ◽  
Vol 47 (2) ◽  
pp. 183
Author(s):  
Flávia Silva Corrêa Tomaz ◽  
Sebastião Martins Filho ◽  
Leandro Roberto de Macedo ◽  
Cristiane Márcia dos Santos
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Analysis Methods ◽  
Competing Events
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