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Estimating cancer treatment intensity from SEER cancer registry data: methods and implications for population-based registry studies of pediatric cancers
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Estimating cancer treatment intensity from SEER cancer registry data: methods and implications for population-based registry studies of pediatric cancers
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Journal Article
Estimating cancer treatment intensity from SEER cancer registry data: methods and implications for population-based registry studies of pediatric cancers
2020
Overview
Objective
The Intensity of Treatment Rating (ITR) Scale condenses treatment and clinical characteristics into a single measure to study treatment effects on downstream health outcomes across cancer types. This rating was originally developed for clinicians to determine from medical charts. However, large studies are often unable to access medical charts for all study participants. We developed and tested a method of estimating treatment intensity (TI) using cancer registry and patient self-reported data.
Methods
We estimated two versions of TI for a cohort of pediatric cancer survivors—one utilized information solely available from cancer registry variables (TI
R
) and the other included registry and self-reported information (TI
S
) from survey participants. In a subset of cases (
n
= 135) for whom the gold standard TI (TI
C
) was known, both TI
R
and TI
S
were compared to TI
C
by calculating percent agreement and weighted Cohen’s kappa, overall and within cancer subtypes.
Results
In comparison to TI
C
, 71% of TI scores from both methods were in agreement (
k
= 0.61 TI
R
/0.54 TI
S
). Among subgroups, agreement ranged from lowest (46% TI
R
/39% TI
S
) for non-defined tumors (e.g., “Tumor-other”), to highest (94% TI
R
/94% TI
S
) for acute lymphoblastic leukemia (ALL).
Conclusions
We developed a methodology to estimate TI for pediatric cancer research when medical chart review is not possible. High reliability was observed for ALL, the most common pediatric cancer. Additional validation is needed among a larger sample of other cancer subgroups. The ability to estimate TI from cancer registry data would assist with monitoring effects of treatment during survivorship in registry-based epidemiological studies.
