Special Issue about Competing Risks and Multi-State Models

There is a clear growing interest, at least in the statistical literature, in competing risks and multi-state models. With the rising interest in competing risks and multi-state models a number of software packages have been developed for the analysis of such models. The present special issue of the Journal of Statistical Software introduces a selection of R packages devoted to competing risks and multi-state models. This introduction to the special issue contains some background and highlights the contents of the contributions.

Special Issue about Competing Risks and Multi-State Models

There is a clear growing interest, at least in the statistical literature, in competing risks and multi-state models. With the rising interest in competing risks and multi-state models a number of software packages have been developed for the analysis of such models. The present special issue of the Journal of Statistical Software introduces a selection of R packages devoted to competing risks and multi-state models. This introduction to the special issue contains some background and highlights the contents of the contributions

Statistical analysis and application of competing risks model with regression

The paper deals with the methods of statistical analysis of dependent competing risks in the presence of covariates. The problem of identification of marginal and joint distributions of competing random variables is recalled and certain identifiability results in the framework of regression models are presented. The main objective is then to study the case when the correlation of competing variables depends on covariates, as this phenomenon has not been taken into account in the most of papers dealing with the identifiability of competing risks models with regression. Such a dependence is demonstrated and estimated on a real example with unemployment data

Competing risks analyses: objectives and approaches

Studies in cardiology often record the time to multiple disease events such as death, myocardial infarction, or hospitalization. Competing risks methods allow for the analysis of the time to the first observed event and the type of the first event. They are also relevant if the time to a specific event is of primary interest but competing events may preclude its occurrence or greatly alter the chances to observe it. We give a non-technical overview of competing risks concepts for descriptive and regression analyses. For descriptive statistics, the cumulative incidence function is the most important tool. For regression modelling, we introduce regression models for the cumulative incidence function and the cause-specific hazard function, respectively. We stress the importance of choosing statistical methods that are appropriate if competing risks are present. We also clarify the role of competing risks for the analysis of composite endpoint

Introduction to Competing Risk Model in the Epidemiological Research

Background and aims: Chronic kidney disease (CKD) is a public health challenge worldwide, with adverse consequences of kidney failure, cardiovascular disease (CVD), and premature death. The CKD leads to the end-stage of renal disease (ESRD) if late/not diagnosed. Competing risk modeling is a major issue in epidemiology research. In epidemiological study, sometimes, inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In these situations, competing risk analysis is preferred to other models in survival analysis studies. The purpose of this study was to describe the bias resulting from the use of standard survival analysis to estimate the survival of a patient with ESRD and to provide alternate statistical methods considering the competing risk. Methods: In this retrospective study, 359 patients referred to the hemodialysis department of Shahid Ayatollah Ashrafi Esfahani hospital in Tehran, and underwent continuous hemodialysis for at least three months. Data were collected through patient’s medical history contained in the records (during 2011-2017). To evaluate the effects of research factors on the outcome, cause-specific hazard model and competing risk models were fitted. The data were analyzed using Stata (a general-purpose statistical software package) software, version 14 and SPSS software, version 21, through descriptive and analytical statistics. Results: The median duration of follow-up was 3.12 years and mean age at ESRD diagnosis was 66.47 years old. Each year increase in age was associated with a 98% increase in hazard of death. In this study, statistical analysis based on the competing risk model showed that age, age of diagnosis, level of education (under diploma), and body mass index (BMI) were significantly associated with death (hazard ratio [HR] = 0.98, P < 0.001, HR = 0.99, P < 0.001, HR = 2.66, P = 0.008, and HR = 0.98, P < 0.020, respectively). Conclusion: In analysis of competing risk data, it was found that providing both the results of the event of interest and those of competing risks were of importance. The Cox model, which ignored the competing risks, presented the different estimates and results as compared to the proportional sub-distribution hazards model. Thus, it was revealed that in the analysis of competing risks data, the sub-distribution proportion hazards model was more appropriate than the Cox model

Modelling competing risks in nephrology research: an example in peritoneal dialysis

BACKGROUND: Modelling competing risks is an essential issue in Nephrology Research. In peritoneal dialysis studies, sometimes inappropriate methods (i.e. Kaplan-Meier method) have been used to estimate probabilities for an event of interest in the presence of competing risks. In this situation a competing risk analysis should be preferable. The objectives of this study are to describe the bias resulting from the application of standard survival analysis to estimate peritonitis-free patient survival and to provide alternative statistical approaches taking competing risks into account. METHODS: The sample comprises patients included in a university hospital peritoneal dialysis program between October 1985 and June 2011 (n = 449). Cumulative incidence function and competing risk regression models based on cause-specific and subdistribution hazards were discussed. RESULTS: The probability of occurrence of the first peritonitis is wrongly overestimated using Kaplan-Meier method. The cause-specific hazard model showed that factors associated with shorter time to first peritonitis were age (>=55 years) and previous treatment (haemodialysis). Taking competing risks into account in the subdistribution hazard model, age remained significant while gender (female) but not previous treatment was identified as a factor associated with a higher probability of first peritonitis event. CONCLUSIONS: In the presence of competing risks outcomes, Kaplan-Meier estimates are biased as they overestimated the probability of the occurrence of an event of interest. Methods which take competing risks into account provide unbiased estimates of cumulative incidence for each specific outcome experienced by patients. Multivariable regression models such as those based on cause-specific hazard and on subdistribution hazard should be used in this competing risk setting

Investigating the presence and impact of competing events on prognostic model research

Prognostic models are used to predict an individual’s future health outcomes, including the risk of disease progression and the development of further complications. The statistical methodology used to develop these models is often naïve to the presence of competing events, these are events which prevent or alter the probability of an outcome of interest from occurring. Not appropriately accounting for competing events is known to produce inflated absolute risk predictions for time-to-event outcomes, this bias is known as competing risks bias. However, there has been relatively little research about competing events in prognostic model research, for which absolute risk predictions are a key outcome. This thesis investigates the presence and impact of competing events on prognostic model research. To begin, two reviews were conducted to determine the presence, reporting, and management of competing events in current prediction model literature. Then competing risks statistical regression methods were applied to develop and internally validate a prognostic model using existing study data. These models were compared to models developed using standard time-to-event analysis techniques, naïve to competing events, with an external validation study. Finally, a simulation study was performed to identify the circumstances for which competing risks bias affects the predictive ability and calibration of prognostic models, with an overall aim to provide guidance for the optimal approaches to incorporate competing risks in prognostic model research

Mathematical methods and models for radiation carcinogenesis studies

Research on radiation carcinogenesis requires a twofold approach. Studies of primary molecular lesions and subsequent cytogenetic changes are essential, but they cannot at present provide numerical estimates of the risk of small doses of ionizing radiations. Such estimates require extrapolations from dose, time, and age dependences of tumor rates observed in animal studies and epidemiological investigations, and they necessitate the use of statistical methods that correct for competing risks. A brief survey is given of the historical roots of such methods, of the basic concepts and quantities which are required, and of the maximum likelihood estimates which can be derived for right censored and double censored data. Non-parametric and parametric models for the analysis of tumor rates and their time and dose dependences are explained