Methodology
The delay modelling process for the NAACCR annual submission includes several complex algorithms and methods. This page provides an overview of some of the methodologies used in the process.
Delay Model
Data Used
Table 1 shows that data portion used in the new model.
Diagnosis Year  Reporting Year  

2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  
2005  2  3  4  5  6  7  8  9  10  11  12  13  
2006  2  3  4  5  6  7  8  9  10  11  12  
2007  2  3  4  5  6  7  8  9  10  11  
2008  2  3  4  5  6  7  8  9  10  
2009  2  3  4  5  6  7  8  9  
2010  2  3  4  5  6  7  8  
2011  2  3  4  5  6  7  
2012  2  3  4  5  6  
2013  2  3  4  5  
2014  2  3  4  
2015  2  3  
2016  2 
Model
For each cancer site and eligible registry, the models and covariates are:
All races (year of diagnosis, age group (<50, 5064, 65+))
By race (year of diagnosis, age group (<50, 5064, 65+), race (White, Black, AsianPacific Islanders (API))
By ethnicity (year of diagnosis, age group (<50, 5064, 65+), ethnicity (Hispanic, nonHispanic))
By race and ethnicity (year of diagnosis, age group (<50, 5064, 65+), race and ethnicity (White Hispanic, White nonHispanic, Black Hispanic, Black nonHispanic))
The modeling steps are shown below.
Step 1: Find ratios of sequential counts ratios of delay times 3 and 2, ratios of delay times 4 and 3, ratios of delay times 5 and 4, …, and ratios of delay times 11 to 10. If there is a missing cell, the ratio is not calculated.
Step 2: Group the ratios found in Step 1 into 4 groups: (1) ratios of delay times 3 and 2; (2) ratios of delay times 4 and 3; (3) ratios of delay times 5 and 4; (4) ratios of delay times j and j1 (j=6, 7, 8, 9, 10, and 11). These four groups are dependent variables in the model. Normally, if there is no missing counts, group 1 has 11 ratios, group 2 has 10 ratios, group 3 has 9 ratios, and group 4 has 33 ratios.
Four Dependent Variables  

a  b  c  d  
Diagnosis Year  r_{3/2}  r_{4/3}  r_{5/4}  r_{6/5}  r_{7/6}  r_{8/7}  r_{9/8}  r_{10/9}  r_{11/10} 
2005  y_{2008}/ y_{2007} 
y_{2009}/ y_{2008} 
y_{2010}/ y_{2009} 
y_{2011}/ y_{2010} 
y_{2012}/ y_{2011} 
y_{2013}/ y_{2012} 
y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
2006  y_{2009}/ y_{2008} 
y_{2010}/ y_{2009} 
y_{2011}/ y_{2010} 
y_{2012}/ y_{2011} 
y_{2013}/ y_{2012} 
y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
2007  y_{2010}/ y_{2009} 
y_{2011}/ y_{2010} 
y_{2012}/ y_{2011} 
y_{2013}/ y_{2012} 
y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 
2008  y_{2011}/ y_{2010} 
y_{2012}/ y_{2011} 
y_{2013}/ y_{2012} 
y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2009  y_{2012}/ y_{2011} 
y_{2013}/ y_{2012} 
y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2010  y_{2013}/ y_{2012} 
y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2011  y_{2014}/ y_{2013} 
y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2012  y_{2015}/ y_{2014} 
y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2013  y_{2016}/ y_{2015} 
y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2014  y_{2017}/ y_{2016} 
y_{2018}/ y_{2017} 

2015  y_{2018}/ y_{2017} 

2016 
Step 3: Excluding Registries that have too much missing data. Eliminate registries that do not have (1) 5 out 11 ratios of delay times 3 and 2; (2) 5 out 10 ratios of delay times 4 and 3; (3) 5 out 9 ratios of delay times 5 and 4; (4) 20 out 33 ratios of the remaining ratios. No delay modeling will be conducted for these registries because they do not have a sufficient history of reporting delay.
Step 4: Step 4: Fit multivariate ANOVA model where the dependent variables are the logarithm of the ratios derived from Step 2.
Step 5: The fitted model is then used to produce delay adjustment factors. For example, let a, b, c, and d denote r_{(3/2)}, r_{(4/3)},r_{(5/4)}, and r_{(5+)}, respectively, as estimates of the ratio from the model. The delay adjustment factor for diagnosis year 2016 is obtained as a*b*c*d^{6}, for diagnosis year 2015 b*c*d^{6}, for diagnosis year 2014 c*d^{6}, and so on.