Competing risks survival analysis
This form of analysis is known by its use of death certificates. In traditional overall survival analysis, the cause of death is irrelevant to the analysis. In a competing risks survival analyses, each death certificate is reviewed. If the disease of interest is cancer, and the patient dies of a car accident, the patient is labelled as censored at death instead of being labelled as having died. Issues with this method arise, as each hospital and or registry may code for causes of death differently. For example, there is variability in the way a patient who has cancer and commits suicide is coded/labelled. In addition, if a patient has an eye removed from an ocular cancer and dies getting hit while crossing the road because he did not see the car, he would often be considered to be censored rather than having died from the cancer or its subsequent effects.Hazard rate
The relative survival form of analysis is more complex than "competing risks" but is considered the gold-standard for performing a cause-specific survival analysis. It is based on two rates: the overall hazard rate observed in a diseased population and the background or expected hazard rate in the general or background population. Deaths from the disease in a single time period are the total number of deaths (overall number of deaths) minus the expected number of deaths in the general population. If 10 deaths per hundred population occur in a population of cancer patients, but only 1 death occurs per hundred general population, the disease specific number of deaths (''excess hazard rate'') is 9 deaths per hundred population. The classic equation for the ''excess hazard rate'' is as follows:Cancer survival
Relative survival is typically used in the analysis of cancer registry data. Cause-specific survival estimation using the coding of death certificates has considerable inaccuracy and inconsistency and does not permit the comparison of rates across registries. The diagnosis of cause-of-death is varied between practitioners. How does one code for a patient who dies of heart failure after receiving a chemotherapeutic agent with known deleterious cardiac side-effects? In essence, what really matters is not why the population dies but if the rate of death is higher than that of the general population. If all patients are dying of car crashes, perhaps the tumour or treatment predisposes them to have visual or perceptual disturbances, which lead them to be more likely to die in a car crash. In addition, it has been shown that patients coded in a large US cancer registry as suffering from a non-cancer death are 1.37 times as likely to die than does a member of the general population. If the coding was accurate, this figure should approximate 1.0 as the rate of those dying of non-cancer deaths (in a population of cancer sufferers) should approximate that of the general population. Thus, the use of relative survival provides an accurate way to measure survival rates that are associated with the cancer in question.Epidemiology
InSoftware
There are several software suites available to estimate relative survival rates. Regression modelling can be performed using maximum likelihood estimation methods by using Stata or R. For example, the R package cmprsk may be used for competing risk analyses which utilize sub-distribution or 'Fine and Gray' regression methods. Gray, Bob. "cmprsk: Subdistribution Analysis of Competing Risks. R package version 2.2-1." http://CRAN. R-project. org/package= cmprsk (2010).See also
*References
{{Reflist Epidemiology Medical statistics