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Surveillance Research Program

Example 1

An example of U.S. Female Breast Cancer for All Races by Age at Prevalence.

Objective

Calculate U.S. complete prevalence for malignant female breast cancer on January 1, 2023 for all races combined using ComPrev.

 

Key Point

The population of the SEER 12 registries does not have the same age-specific racial breakdown as the U.S. So, we must first project SEER limited-duration prevalence to the U.S. by separate race categories. These results will be summed to get estimates for all races combined. 

 

Steps

  1. If you do not have files saved from the results of the ProjPrev tutorial, please download and save the following .dic and .txt files to your computer. These files will be needed in a later step.

  2. Start the ComPrev application. 

  3. Select File, New Session from the menu. 

  4. For Type of Session, select Complete Prevalence. Then press the Next button at the bottom of the window. 

  5. Since we are using age groups with 90+ in our limited-duration file, make sure the "90+ Age Group Data Source" is selected on the Inc/Surv Parameters tab to use the built in data file for ComPrev. Then press the Next button. 

  6. On the LDP tab, you will need to select your LDP data dictionary. Under step 1, where it says DIC File, press the Browse button. In the Open File dialog that is displayed, navigate to where you saved the file "us.ldprev.female.breast.by.age.2025sub.dic," select the file, and press the Open button. Steps to create the U.S. limited-duration prevalence using SEER*Stat and ProjPrev are available here.

  7. Double check that the software automatically loaded "us.ldprev.female.breast.by.age.2025sub.txt" from your saved location in the Data File box.

  8. In the step 2 "Select Age Variable" section, "Age at Prevalence Date (Calculated)" should be visible in the drop-down box. Use the "Verify Age Groups..." button to confirm that your LDP file has the same age groupings as the ComPrev data source you are using. 

  9. Next press the Mapping... button under Step 3. The Data Mapping window will be displayed. This window lets you tell ComPrev how to map your data file to its internal data. 

    • At the top of the window, you will see 2 drop-down boxes. These two drop-down boxes allow you to select the variables within the LDP file and how they match up to the variables within ComPrev's default data source. ComPrev has already tried to match them up on its own. The values shown in these drop-down boxes indicate that ComPrev could not find an equivalent variable in the LDP file for Site or Sex. So you need to tell ComPrev what to use.

    • Select the drop-down box in the first row for "Site." Since there is no variable in the LDP file to match up, we need to tell ComPrev to just use one single constant value for the "Site" variable. The drop-down box contains an entry for  "[No LDP field. Select ComPrev value below:]." You want to scroll down the list until you see an entry under this called "Breast" and select it. You are telling ComPrev to just use the single value "Breast" for the "Site" variable.

    • Next select the drop-down box in the second row of the top table in this window for the ComPrev Field "Sex." This is another variable that ComPrev's default data source contains, but the LDP file does not. For this we want to select the value "Female," since the data in the LDP file is for Breast / Female. When you are done making this selection, you have completed your mapping of this file and can press the OK button.  

  10. Back on the Session Setup window - LDP tab, press the Next button. On the Selection tab, you will see that the values for Breast and Female are already selected for you. These values were determined from your LDP data file. Press the Next button. 

  11. On the Settings tab, you can see that most of the information is already filled in for you from the LDP data file. The Prevalence Date is 1/1/2023, the Initial Data of Diagnosis is 1992, and the number of Age Groups is "20 Age Groups (0, 1-4, 5-9, ..., 90+)." Press the Finish button at the bottom of the window. 

  12. A Progress Window will be briefly displayed while ComPrev is calculating the complete prevalence statistics. When they are done, the Main window will be displayed. 

  13. On the left side of the Main Window, the list of cohorts to view are displayed. Since we only had data for one cohort, we just have the Breast  Female cohort available. 

  14. On the right side of the Main Window, 5 tabs are displayed. 

    • Incidence: You can view the incidence parameters used by ComPrev as a table or graph. 

    • Survival: You can view the survival parameters used by ComPrev as a table or graph. 

    • Completeness Index: Contains a table with completeness index and standard error (if requested) by age at prevalence as well as a graph of the completeness index.

    • Prevalence Estimates: You can view the results as a table or graph. The table includes the completeness index (percents), the limited-duration and complete prevalence (percents and counts), and the U.S. population by age. SE's (Standard Errors) are included if requested. The first graph, Limited Duration Prevalence vs. Complete Prevalence, allows you to view the counts and percents for Limited Duration overlapping those of Complete Prevalence. The second graph, Complete Prevalence Count, allows you to view the counts for each age group. 

    • Settings: Displays all of the information you specified to create this session. 

  15. Even though we did not calculate the limited-duration prevalence statistics with an all ages category, one is added to the results. Because ProjPrev included the populations in the data file, ComPrev is able to calculate the limited-duration and complete prevalence percents for the all ages category. If populations were not included, these statistics could not be calculated. The "Collapse Age Groups" drop-down box at the top right of the Prevalence Estimates tab allows you to view the data using larger age groupings. Note that if you use non-default age groups, some statistics may not be available (e.g. completeness indexes, standard errors).