Competing Risks a Statistical Approach

 

Sujatha V.

School of Advanced Sciences, Department of Mathematics, VIT University, Vellore.

 

ABSTRACT:

Survival analyses are commonly applied to study death or other events of interest. In such analyses, so-called competing risks may form an important problem. Competing risk are said to be present when a patient is at risk of more than one mutually exclusive event, such as death from different cause which will prevent any other from happening. when competing events are not independent treating subjects dying from extraneous causes as censored and then computing the ordinary Kaplan-Meir estimate results in biased survival estimates, in such cases,the cumulative incidence and cause-specific hazard may lead to valid results, but the resulting model does not allow one to estimate risks conditional on removal of one or more causes of the event.

 

 

 

INTRODUCTION:

In survival analysis, when a subject experience the two or more causes to fail, competing risks occur. A logical objective for competing risks data is to assess the relationship of relevant predictors to the failure rate.In the presence of competing risks, the usual survival methods should be applied with caution and one has to be aware of the consequences of their use. The Kaplan–Meier method is the most common as well as the most controversial technique in the competing risks framework. It is a method for estimating survival probabilities (Kaplan and Meier, 1958) at different time points. When competing risks are present, Kaplan–Meier estimates (denoted by KM) cannot be interpreted as probabilities. Their complement (1− KM) can be interpreted as the probability of an event of interest, where the other types of events do not exist. However, this concept is not useful in practice.

 

(Kalbfleisch and Prentice (1980))^[2] suggested an approach that accounted for the competing risks. This method is labelled the cumulative incidence function Using this technique, the probability of any event happening is partitioned into the probabilities for each type of event. Three estimators may be used to perform competing risks analysis: 1) the complement of the Kaplan-Meier estimator and 2) the cumulative incidence function. 3) the conditional probability function. when the  distinct causes of failures are dependent then some specific methods are needed to  estimate the survival probabilities. The Cox proportional hazards model may be used for regression analysis, but the interpretation of the results becomes different.

 

REFERENCES:

1.       H. Putter:Tutorial in Biostatistics: Competing Risks and Multi-State Models. Analysis Using the m state Package. available athttp://cran.r-project.org/ web/ packages/ mstate/vignettes/ Tutorial.pdf, cited on June 1, 2011.

2.       J. D. Kalbfleisch, R. L. Prentice: The Statistical Analysis of Failure Time Data. John Wiley & Sons, New York, 2002.

3.       Lim HJ, Zhang X, Dyck Ret al. Methods of competing risks analysis of end-stage renal disease and mortality among people with diabetes. BMC Med Res Methodol 2010; 10: 97

4.       N. Porta, G. Gómez, M. Luz Calle: The Role of Survival Functions in Competing Risks available athttp://www.eio.upc.es/nporta, cited on June 20, 2011.

5.       Pepe MS, Mori M. Kaplan Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Stat Med 1993; 12: 737–751

6.       Verduijn M, Grootendorst DC, Dekker FWet al. The analysis of competing events like cause-specific mortality—beware of the Kaplan–Meier method. Nephrol Dial Transplant 2011; 26: 56–61

 

 

Accepted on 10.08.2016        © RJPT All right reserved

Research J. Pharm. and Tech 2016; 9(11): 1886-1891