1. Defining Cancer Recurrence in a Population-Based Cancer Surveillance Study
Trevor D. Thompson
NAACCR Conference 2019
Mathematical Statistician
Cancer Surveillance Branch
June 13, Vancouver, British Columbia, Canada

2. Background
Cancer recurrence is an important outcome not routinely captured by population-based registries
Difficult time to event outcome to define
Who is eligible for a recurrence?
When to start the time to event clock (i.e. when are patients at risk)?
When to censor?
Assume disease-free through active follow-up date (last review of medical records) unless otherwise documented?
At last documented disease-free date – clinical follow-up schedules vary
Should cause of death information figure in?

3. NPCR Patient-Centered Outcomes Research (PCOR)
Five US states (CO, LA, ID, NH, RI)
Outcomes of interest – disease-free (DF) status, recurrence, and progression
Active follow-up of medical records for a minimum of 32 months post-diagnosis with the majority followed at least 60 months
N=17,802 breast and colorectal cancer cases diagnosed in 2011

4. NPCR Patient-Centered Outcomes Research (PCOR)
Recurrence Status
Coding:
0 = Patient never found to be DF
1 = Found to be DF and remained DF till the end of study
2 = Documented Recurrence
3 = Uncertain if recurrence based on incomplete documentation
4 = Uncertain if recurrence based on conflicting documentation
9 = Unknown; patient was DF but unclear if remained DF or recurred
First recurrence date
Type of recurrence (in situ, local, regional, distant)
Recurrence source

5. Study Population Include only: First primary diagnosed in study period
Surgery patients
Achieved DF status (at risk for recurrence)
Known stage
Exclude patients with no follow-up time:
Missing surgery or recurrence date
Unknown recurrence status and last DF date = surgery date
N=9826 female breast cancers and N=3770 colorectal cancers

6. Methods Outcomes: Time from surgery to recurrence
Time from surgery to death or recurrence
Censoring Methods/Assumptions:
Assume no recurrence through active follow-up (AFU) date
Censor at last documented DF date
Used SEER Cause-Specific Death Classification as part of outcome algorithm
Uses ICD codes to define cancer deaths for first primaries
0=Alive or dead of other cause, 1=Dead attributable to cancer DX
We used cancer-specific COD only for sequence numbers 02+

7. Methods
Discrete variables presented as frequencies and percentages; continuous variables presented as 25th / 50th / 75th percentiles
Freedom from recurrence and recurrence-free survival presented as Kaplan-Meier estimates/figures
Estimates censored at 4-years where follow-up was relatively complete

8. Events Algorithm – AFU Assumption
R = Recurrence, DR = Death or Recurrence
Patients with a documented recurrence classified as both events at date of recurrence
Patients classified as DF until end of study according to abstractor
Patients that were DF but unclear if remained DF until end of study
Vital Status
COD
R Status
R Censor/Event Date
DR Status
DR Censor/Event Date
Alive NA
No Event
AFU Date
Died
Non-Cancer*
First of Death/AFU Date
Event
Death Date
Cancer*
Last DF Date
Vital Status
COD
R Status
R Censor/Event Date
DR Status
DR Censor/Event Date
Alive NA
No Event
Last DF Date
Died
Non-Cancer*
Death Date
Event
Cancer*
Imputed halfway between last DF & Death
* According to SEER Cause-Specific Death algorithm

9. Events Algorithm – Documented DF
R = Recurrence, DR = Death or Recurrence
Patients with a documented recurrence classified as both events at date of recurrence
Patients classified as DF until end of study according to abstractor
Patients that were DF but unclear if remained DF until end of study
Vital Status
COD
R Status
R Censor/Event Date
DR Status
DR Censor/Event Date
Alive NA
No Event
Last DF Date
Died
Non-Cancer*
Event
Death Date
Cancer*
Vital Status
COD
R Status
R Censor/Event Date
DR Status
DR Censor/Event Date
Alive NA
No Event
Last DF Date
Died
Non-Cancer*
Event
Death Date
Cancer*
Imputed halfway between last DF & Death
* According to SEER Cause-Specific Death algorithm

10. Study Population - Demographics
Breast Cancer
N=9826
Colorectal Cancer
N=3770
Total
N=13596
Age (years)
51 / 61 / 70
56 / 67 / 76
52 / 62 / 72
Sex
Male
1924 (51.0%)
1924 (14.2%)
Female
9826 (100.0%)
1846 (49.0%)
11672 (86.8%)
Race
White
8598 (87.8%)
3237 (86.0%)
11835 (87.3%)
Black
1008 (10.3%)
465 (12.4%)
1473 (10.9%)
American Indian / Alaska Native
44 (0.4%)
13 (0.3%)
57 (0.4%)
Asian / Pacific Islander
124 (1.3%)
41 (1.1%)
165 (1.2%)
Other
15 (0.2%)
6 (0.2%)
21 (0.2%)
Unknown 37 8 45
Ethnicity
Non-Hispanic
9281 (94.5%)
3556 (94.3%)
12837 (94.4%)
Hispanic
545 (5.5%)
214 (5.7%)
759 (5.6%)

11. Study Population – AJCC Stage
Breast Cancer
N=9826
Colorectal Cancer
N=3770
Total
N=13596
AJCC Stage
2007 (20.4%)
347 (9.2%)
2354 (17.3%) I
4438 (45.2%)
1115 (29.6%)
5553 (40.8%) II
2533 (25.8%)
1162 (30.8%)
3695 (27.2%)
III
794 (8.1%)
1027 (27.2%)
1821 (13.4%) IV
54 (0.5%)
119 (3.2%)
173 (1.3%)

12. 4-year Freedom from Breast Cancer Recurrence by AJCC Stage

13. 4-year Freedom from Colorectal Cancer Recurrence by AJCC Stage

14. Summary: 4-Year Recurrence-Free Survival
AJCC Stage
Breast cancer
n=9,826
Colorectal cancer
n=3,770
AFU
Documented DF
95.2%
94.6%
82.2%
80.2% I
91.8%
91.3%
81.8%
II-III
83.2%
82.4%
69.2%
67.7% IV
63.4%
63.0%
37.8%
36.7%

15. Summary
PCOR dataset provides a unique opportunity to analyze cancer recurrence in a population-based setting
Active follow-up of medical records with directly coded disease-free, recurrence, and progression status
Recurrence is a difficult outcome to define
Important to weigh the pros/cons of definitions/algorithms and acknowledge limitations
Recurrence-free survival presents additional challenge of shorter follow-up for recurrence compared to death (via linkages)

16. Summary
Recommend using active follow-up date in definitions rather than last DF date
Logically makes sense given the nature of the PCOR data collection (abstractors reviewing records and directly coding recurrence status)
Results in less censoring - increases % with complete follow-up
Yielded similar results for recurrence and recurrence-free survival compared to documented DF dates
Recommend using cause of death information as part of algorithms

17. Acknowledgements PCOR states – Investigators & Staff CDC
Colorado Central Cancer Registry
Randi K. Rycroft
Cancer Data Registry of Idaho
Christopher J. Johnson
Louisiana Tumor Registry
Xiao-Cheng Wu, Mei-chin, Vivien Chen
Rhode Island Cancer Registry
David Rousseau
New Hampshire State Cancer Registry
Judith Rees, Bruce Riddle, Maria O. Celaya
CDC
*Trevor Thompson (Analytic Lead)
*Loria Pollack (Project and Manuscript Lead)
Manxia Wu
MaryBeth Freeman
Reda Wilson
Jen Wike
Vicki Benard
ICF International
Kevin B. Zhang
Funding: This work was supported by the Patient Centered Outcome Research Trust Fund through CDC Cooperative Agreements of the National Program of Cancer Registries: in conjunction with the participating states and a CDC Patient Centered Outcome Research contract to ICF