PHEindicatormethods (version 2.1.0)

assign_funnel_significance: Identifies whether each value in a dataset falls outside of 95 and/or 99.8 percent control limits based on the aggregated average value across the whole dataset as an indicator of statistically significant difference.

Description

This follows the funnel plot methodology published on the PHE Fingertips Technical Guidance page: https://fingertips.phe.org.uk/profile/guidance/supporting-information/PH-methods

Usage

assign_funnel_significance(
  data,
  numerator,
  denominator,
  rate,
  statistic = NULL,
  rate_type = NULL,
  multiplier = NULL
)

Value

returns the original data.frame with the significance level appended

Arguments

data

a data.frame containing the data to assign significance for; unquoted string; no default

numerator

field name from data containing the observed numbers of cases in the sample meeting the required condition (the numerator or observed counts for the control limits); unquoted string; no default

denominator

field name from data containing the population(s) in the sample (the denominator or expected counts for the control limits); unquoted string; no default

rate

field name from data containing the rate data when creating funnels for a Crude or Directly Standardised Rate; unquoted string; no default

statistic

type of statistic to inform funnel calculations. Acceptable values are "proportion", "ratio" or "rate"; string; no default

rate_type

if statistic is "rate", specify either "dsr" or "crude"; string; no default

multiplier

the multiplier that the rate is normalised with (ie, per 100,000); only required when statistic = "rate"; numeric; no default

Author

Matthew Francis

See Also

Other PHEindicatormethods package functions: calculate_ISRate(), calculate_ISRatio(), calculate_dsr(), calculate_funnel_limits(), calculate_funnel_points(), phe_dsr(), phe_life_expectancy(), phe_mean(), phe_proportion(), phe_quantile(), phe_rate(), phe_sii()

Examples

library(dplyr)
df <- data.frame(
  Area = c("A", "B", "C", "D"),
  numerator = c(10232, 12321, 15123, 13213),
  denominator = c(15232, 16123, 17932, 18475)
)
df %>%
  assign_funnel_significance(numerator, denominator,
                             statistic = "proportion", multiplier = 100)