Funnel plots for population‐based cancer survival: principles, methods and applications. (4th September 2013)

• Record Type: Journal Article
• Authors: Quaresma, M.; Coleman, M. P.; Rachet, B.
• Is Part Of: Statistics in medicine. Volume 33:Number 6(2014)
• Page Start: 1070
• Page End: 1080
• Publication Date: 2013-09-04
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.5953
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim5953-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim5953-para-0001">Funnel plots are graphical tools designed to detect excessive variation in performance indicators by simple visual inspection of the data. Their main use in the biomedical domain so far has been to detect publication bias in meta‐analyses, but they have also been recommended as the most appropriate way to display performance indicators for a vast range of health‐related outcomes. Here, we extend the use of funnel plots to population‐based cancer survival and several related measures. We present three applications to familiarise the reader with their interpretation. We propose funnel plots for various cancer survival measures, as well as age‐standardised survival, trends in survival and excess hazard ratios. We describe the components of a funnel plot and the formulae for the construction of the control limits for each of these survival measures. We include three transformations to construct the control limits for the survival function: complementary log–log, logit and logarithmic transformations. We present applications of funnel plots to explore the following: (i) small‐area and temporal variation in cancer survival; (ii) racial and geographical variation in cancer survival; and (iii) geographical variation in the excess hazard of death. Funnel plots provide a simple and informative graphical tool to display geographical variation and trend in a range of cancer survival measures. We recommend their use as a routine instrument for cancer survival comparisons, to inform health policy makers in planning and assessing cancer policies. We advocate the use of the complementary log–log or logit transformation to construct the control limits for the survival function. Copyright © 2013 John Wiley & Sons, Ltd.</p> </abstract>