Modeling gender and age adjusted incidence rates

National Health Institute (NHI) provides a tool box for calculation of cancer incidence and percentage change. Their algorithm for Jointpoint Trend Analysis is well-documented but does not provide the best tool at hand for most problems. The normal approximation is not the most optimal choice for situations with a low incidence rate in which I would recommend to apply modern logistic regression algorithms which are far more versatile.

 

The difference between careful parametrization in a binomial regression model and the plug-and-play functionality of the NHI suite becomes obvious in an example in which we look at cancers in children. Data source: NORDCAN

Logistic regression models. Joint point model (left) using stepwise linear gender specific regression models and polynomial models (right) using gender specific polynomial regression models.
 


Graphs with gender specific 95% prediction limits
 
 
 
 


R-script Data Extraction
SAS program

Joint Point Model based on software from NIH
The estimation procedure does not allow zero-counts, which introduces bias.


Furthermore, errors are approximate normal distributed.


The logistic regression model predicts a total of 190 cancer cases during the period 1979-2014, whereas the Jointpoint trend program from NHI predicts 158 cases of cancer when adjusting for calendar year. Binomial model estimates a total combined incidence rate of 0.57 (per 100,000) corrected for calendar year, whereas the Jointpoint trend analysis program yields an incidence rate of 0.47 (per 100,000). We observe a total of 33,679,014 person years.
We have used actual connective tissue cancer incidence counts for Danes age 0 to 24 from the NORDCAN register of gender specific incidence rates with a total of 189 cases in the period 1979-2014.


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