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.

Table 1. Data portion used in the new model (with number of years of reporting delay in each cell).
Diagnosis Year Reporting Year
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
2011 2 3 4 5 6 7 8 9 10 11 12 13
2012   2 3 4 5 6 7 8 9 10 11 12
2013     2 3 4 5 6 7 8 9 10 11
2014       2 3 4 5 6 7 8 9 10
2015         2 3 4 5 6 7 8 9
2016           2 3 4 5 6 7 8
2017             2 3 4 5 6 7
2018               2 3 4 5 6
2019                 2 3 4 5
2020                   2 3 4
2021                     2 3
2022                       2

Model

For each cancer site and eligible registry, the models and covariates are:

All races (year of diagnosis, age group (<50, 50-64, 65+))

By race (year of diagnosis, age group (<50, 50-64, 65+), race (White, Black))

By race and ethnicity (year of diagnosis, age group (<50, 50-64, 65+), race and ethnicity (Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian and Pacific Islanders))

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 j-1 (j=6, 7, 8, 9, 10, and 11). These four groups are dependent variables in the model. Normally, if there are no missing counts, group 1 has 11 ratios, group 2 has 10 ratios, group 3 has 9 ratios, and group 4 has 33 ratios.

Table 2. Four dependent variables formed by ratios of delay times (Labeled as a, b, c, and d).
  Four Dependent Variables
  a b c d
Diagnosis Year r3/2 r4/3 r5/4 r6/5 r7/6 r8/7 r9/8 r10/9 r11/10
2011 y2014/
y2013		 y2015/
y2014		 y2016/
y2015		 y2017/
y2016		 y2018/
y2017		 y2019/
y2018		 y2020/
y2019		 y2021/
y2020		 y2022/
y2021
2012 y2015/
y2014		 y2016/
y2015		 y2017/
y2016		 y2018/
y2017		 y2019/
y2018		 y2020/
y2019		 y2021/
y2020		 y2022/
y2021		 y2023/
y2022
2013 y2016/
y2015		 y2017/
y2016		 y2018/
y2017		 y2019/
y2018		 y2020/
y2019		 y2021/
y2020		 y2022/
y2021		 y2023/
y2022		 y2024/
y2023
2014 y2017/
y2016		 y2018/
y2017		 y2019/
y2018		 y2020/
y2019		 y2021/
y2020		 y2022/
y2021		 y2023/
y2022		 y2024/
y2023		  
2015 y2018/
y2017		 y2019/
y2018		 y2020/
y2019		 y2021/
y2020		 y2022/
y2021		 y2023/
y2022		 y2024/
y2023		    
2016 y2019/
y2018		 y2020/
y2019		 y2021/
y2020		 y2022/
y2021		 y2023/
y2022		 y2024/
y2023		      
2017 y2020/
y2019		 y2021/
y2020		 y2022/
y2021		 y2023/
y2022		 y2024/
y2023		        
2018 y2021/
y2020		 y2022/
y2021		 y2023/
y2022		 y2024/
y2023		          
2019 y2022/
y2021		 y2023/
y2022		 y2024/
y2023		            
2020 y2023/
y2022		 y2024/
y2023		              
2021 y2024/
y2023		                
2022                  

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: Using the logarithm of the ratios derived from Step 2 as the dependent variables to fit a statistical model. 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 2022 is obtained as a*b*c*d6, for diagnosis year 2021 b*c*d6, for diagnosis year 2020 c*d6, and so on.