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.

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, Asian-Pacific Islanders (API))

By ethnicity (year of diagnosis, age group (<50, 50-64, 65+), ethnicity (Hispanic, non-Hispanic))

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

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 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.

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 2017 is obtained as a*b*c*d⁶, for diagnosis year 2016 b*c*d⁶, for diagnosis year 2015 c*d⁶, and so on.