Cancer registries: a novel alternative to long-term clinical trial follow-up based on results of a comparative study.
BACKGROUND
Data collection and review were identified as major contributors to the cost of randomized clinical trials (RCTs).


OBJECTIVE
We proposed and assessed a novel alternative for long-term clinical trial follow-up based on the data captured through an accredited Cancer Registry (CR) that is part of the National Cancer Database (NCDB).


METHODS
Patients from Mayo Clinic, Rochester, enrolled in the North Central Cancer Treatment Group N934653 (COST) trial (98 patients) and the American College of Surgeons Oncology Group Z0030 trial (55 patients) were included in the study. Demographic, treatment, and long-term outcome data were compared between the hospital-based CR and the RCTs' databases. Concordances were used to estimate the agreement between two databases. Kaplan-Meier curves were plotted to examine the consistency of time-to-event long-term outcomes of the CR and RCT databases.


RESULTS
High concordances (>95%) were observed for most demographic and treatment variables between the CR data and RCT data. The vital status concordances were 100% and 94.5% between the CR and COST and Z0030 databases, respectively. Three discrepant death dates were observed, one in the COST trial and two in the Z0030 trial. The concordances of disease-free status between the CR and RCT databases were 99.0% and 87.3%, and 15 discrepant disease recurrence cases were identified: 4 for COST and 11 for Z0030.


CONCLUSIONS
The analysis has been focused on patients from a single site, Mayo Clinic, Rochester, enrolled in two large RCT evaluating surgical treatments. The findings herein need to be confirmed in a broader setting, such as multi-center, multi-registry including nonsurgical trials.


CONCLUSIONS
CR data were nearly identical to data from two randomized phase III trials in different disease types and conducted by two different cooperative groups. The NCDB Cancer Registries represent a feasible alternative for obtaining long-term follow-up data for large clinical trials.
Shi
Qian
Q
Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA. shi.qian2@mayo.edu
You
Y Nancy
YN
Nelson
Heidi
H
Allen
Mark S
MS
Winchester
David
D
Stewart
Andrew
A
Young-Fadok
Tonia
T
Decker
Paul A
PA
Green
Erin M
EM
Holton
Sara J
SJ
Ballman
Karla V
KV
D003082:Colectomy / Q000706:statistics & numerical data; D003113:Colonoscopy / Q000706:statistics & numerical data; D015179:Colorectal Neoplasms / Q000401:mortality* / Q000601:surgery; D016001:Confidence Intervals; D003625:Data Collection / Q000379:methods; D019992:Databases as Topic; D006801:Humans; D053208:Kaplan-Meier Estimate; D009364:Neoplasm Recurrence, Local; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012042:Registries / Q000706:statistics & numerical data*; D018570:Risk Assessment; D014481:United States