Comparison of different approaches to estimating age standardized net survival.
BACKGROUND
Age-standardized net survival provides an important population-based summary of cancer survival that appropriately accounts for differences in other-cause mortality rates and standardizes the population age distribution to allow fair comparisons. Recently, there has been debate over the most appropriate method for estimating this quantity, with the traditional Ederer II approach being shown to have potential bias.


METHODS
We compare lifetable-based estimates (Ederer II), a new unbiased method based on inverse probability of censoring weights (Pohar Perme) and model-based estimates. We make the comparison in a simulation setting; generating scenarios where we would expect to see a large theoretical bias.


RESULTS
Our simulations demonstrate that even in relatively extreme scenarios there is negligible bias in age-standardized net survival when using the age-standardized Ederer II method, modelling with continuous age or using the Pohar Perme method. However, both the Ederer II and modelling approaches have some advantages over the Pohar Perme method in terms of greater precision, particularly for longer-term follow-up (10 and 15 years).


CONCLUSIONS
Our results show that, when age-standardizing, concern over bias with the traditional methods is unfounded. We have also shown advantages in using the more traditional and modelling methods.
Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK. paul.lambert@le.ac.uk.|Department of Medical Epidemiology and Biostatistics, Karolinska Insitutet, Stockholm, PO BOX 281, 24105, Sweden. paul.lambert@le.ac.uk.
Paul C
PC
Lambert
Department of Medical Epidemiology and Biostatistics, Karolinska Insitutet, Stockholm, PO BOX 281, 24105, Sweden. paul.dickman@ki.se.
Paul W
PW
Dickman
Department of Health Sciences, University of Leicester, University Road, Leicester, LE1 7RH, UK. mark.rutherford@le.ac.uk.
Mark J
MJ
Rutherford
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008875:Middle Aged; D008962:Models, Theoretical; D009369:Neoplasms / Q000401:mortality*; D012042:Registries / Q000706:statistics & numerical data; D016019:Survival Analysis; D015996:Survival Rate