In oizin/funnelplot: Create Funnel Plots

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

funnelplot

The goal of funnelplot is to simplify risk adjusted comparisons of institutional (cluster) performance for the purpose of outlier discovery.

Installation

You can install the development version of funnelplot from GitHub with:

# install.packages("devtools")
devtools::install_github("oizin/funnelplot")

Example

This is a basic example which shows you how to produce a risk adjusted funnel plot with 95% and 99% control limits:

library(funnelplot)
data("example_data")

# outcome ~ covariates | cluster ID
f1 <- funnelModel(test ~ age + gender | hosp_id, pointTarget(limits = c(0.05,0.01)), data = example_data)
plot(f1) +
  theme_funnel()

funnelplot supports:

• Binary outcomes: situations in which the outcome can be conceptualised as a Bernoulli trial with outcomes 0 "failure" and 1 "success".
• Covariate risk adjustment: control for differences in the characteristics of observations within each cluster. This is done through using a statistical model to estimate the expected outcome for each observation. This is followed by cluster level calculation of a suitable comparison measure of the observed and expected outcomes.
• Multiplicity adjustment: control for potentially high false positive rates in the comparison of large numbers of clusters using Bonferroni or false discovery rate (FDR) based corrections.
• Overdispersion: In situations where there is large variation in performance between the clusters, and the outcome cannot be reliabilty predicted, one approach to avoiding labelling a large number of clusters as outliers is to adjust the control limits outward by an inflation factor (e.g. 20%). The inflation factor can be estimated using the data, with the ability to leave a certain percentage of the best and worst performing clusters left out of the calculation.

All objects returned by funnelplot::plot() are ggplot2 objects enabling further customisation:

library(ggplot2)
plot(f1) +
  labs(x = "Number of patients", y = "Performed test (%)", title = "Hospital performance (2018)", 
       subtitle = "Adjusted for patient age and gender") +
  scale_y_continuous(breaks = seq(0.2,0.6,0.2),labels = 100*seq(0.2,0.6,0.2)) +
  scale_color_discrete(name = "Control limit", labels = c(0.95,0.99)) +
  theme(panel.background = element_rect(fill = "gray96",
                                colour = "white",
                                size = 0.5, linetype = "solid"))

See the vignettes:

# vignettes
vignette("funnelplot-intro")

References

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 289-300.

Jones, H. E., Ohlssen, D. I., & Spiegelhalter, D. J. (2008). Use of the false discovery rate when comparing multiple health care providers. Journal of clinical epidemiology, 61(3), 232-240.

Spiegelhalter, D. J. (2005). Funnel plots for comparing institutional performance. Statistics in medicine, 24(8), 1185-1202.

oizin/funnelplot documentation built on March 11, 2021, 2:58 p.m.