When describing the survival experience of a group of people or patients typically the method of overall survival is used, and it presents estimates of the proportion of people or patients alive at a certain point in time.
Issues with this method arise, as each hospital and or registry may code for causes of death differently.
[citation needed] For example, there is variability in the way a patient who has cancer and commits suicide is coded/labelled.
[1] 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.
How does one code for a patient who dies of heart failure after receiving a chemotherapeutic agent with known deleterious cardiac side-effects?
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 five consecutive years are multiplied, the resulting figure would be known as cumulative relative survival (CRS).
It is analogous to the five-year overall survival rate, but it is a way of describing cancer-specific risk of death over five years after diagnosis.
Regression modelling can be performed using maximum likelihood estimation methods by using Stata or R.[4][5] For example, the R package cmprsk may be used for competing risk analyses which utilize sub-distribution or 'Fine and Gray' regression methods.