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Funnel plots for population-based cancer survival: principles, methods and applications
M Quaresma1, M P Coleman, B Rachet
1Cancer Research UK Cancer Survival Group, Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, U.K.
Statistics in Medicine
|September 17, 2013
Summary
Funnel plots visually identify variations in cancer survival data, aiding in assessing geographical and temporal trends. These graphical tools help inform health policy and cancer control strategies.
Area of Science:
- Biostatistics
- Cancer Epidemiology
- Health Services Research
Background:
- Funnel plots are graphical tools for detecting excessive variation in performance indicators.
- Their primary use in biomedicine has been identifying publication bias in meta-analyses.
- They are recommended for displaying health-related outcomes but underutilized for cancer survival data.
Purpose of the Study:
- To extend the application of funnel plots to population-based cancer survival and related measures.
- To propose funnel plots for various cancer survival metrics, including age-standardized survival, survival trends, and excess hazard ratios.
- To demonstrate their utility in exploring variations in cancer survival and mortality.
Main Methods:
- Describing funnel plot components and control limit formulae for survival measures.
- Utilizing complementary log-log, logit, and logarithmic transformations for survival function control limits.
- Applying funnel plots to analyze small-area, temporal, racial, and geographical variations in cancer survival and excess hazard.
Main Results:
- Demonstrated funnel plots as effective for visualizing geographical and temporal variations in cancer survival.
- Showcased applications for analyzing racial and geographical disparities in survival.
- Illustrated their use in examining geographical differences in the excess hazard of death.
Conclusions:
- Funnel plots offer a simple, informative graphical method for displaying cancer survival trends and geographical variations.
- Recommend routine use of funnel plots for cancer survival comparisons to inform health policy.
- Advocate for complementary log-log or logit transformations for constructing survival function control limits.
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