phe_dsr: Calculate Directly Standardised Rates using phe_dsr

In publichealthengland/PHEindicatormethods: Common Public Health Statistics and their Confidence Intervals

Calculate Directly Standardised Rates using phe_dsr

Description

Calculates directly standardised rates with confidence limits using Byar's method (1) with Dobson method adjustment (2). This function is soft-deprecated, please use calculate_dsr() instead.

Usage

phe_dsr(
  data,
  x,
  n,
  stdpop = esp2013,
  stdpoptype = "vector",
  type = "full",
  confidence = 0.95,
  multiplier = 1e+05
)

Arguments

data	data.frame containing the data to be standardised, pre-grouped if multiple DSRs required; unquoted string; no default
x	field name from data containing the observed number of events for each standardisation category (eg ageband) within each grouping set (eg area); unquoted string; no default
n	field name from data containing the populations for each standardisation category (eg ageband) within each grouping set (eg area); unquoted string; no default
stdpop	the standard populations for each standardisation category (eg age band); unquoted string referencing a numeric vector or field name from data depending on value of stdpoptype; default = esp2013
stdpoptype	whether the stdpop has been specified as a vector or a field name from data; quoted string "field" or "vector"; default = "vector"
type	defines the data and metadata columns to be included in output; can be "value", "lower", "upper", "standard" (for all data) or "full" (for all data and metadata); quoted string; default = "full"
confidence	the required level of confidence expressed as a number between 0.9 and 1 or a number between 90 and 100 or can be a vector of 0.95 and 0.998, for example, to output both 95 percent and 99.8 percent percent CIs; numeric; default 0.95
multiplier	the multiplier used to express the final values (eg 100,000 = rate per 100,000); numeric; default 100,000

Value

When type = "full", returns a tibble of total counts, total populations, directly standardised rates, lower confidence limits, upper confidence limits, confidence level, statistic and method for each grouping set

Notes

User MUST ensure that x, n and stdpop vectors are all ordered by the same standardisation category values as records will be matched by position.

For total counts >= 10 Byar's method (1) is applied using the internal byars_lower and byars_upper functions. When the total count is < 10 DSRs are not reliable and will therefore not be calculated.

References

(1) Breslow NE, Day NE. Statistical methods in cancer research, volume II: The design and analysis of cohort studies. Lyon: International Agency for Research on Cancer, World Health Organisation; 1987.

(2) Dobson A et al. Confidence intervals for weighted sums of Poisson parameters. Stat Med 1991;10:457-62.

See Also

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

Examples

library(dplyr)
df <- data.frame(indicatorid = rep(c(1234, 5678, 91011, 121314),
                 each = 19 * 2 * 5),
                 year = rep(2006:2010, each = 19 * 2),
                 sex = rep(rep(c("Male", "Female"), each = 19), 5),
                 ageband = rep(c(0,5,10,15,20,25,30,35,40,45,
                                 50,55,60,65,70,75,80,85,90), times = 10),
                 obs = sample(200, 19 * 2 * 5 * 4, replace = TRUE),
                 pop = sample(10000:20000, 19 * 2 * 5 * 4, replace = TRUE))

## default execution
df %>%
  group_by(indicatorid, year, sex) %>%
  phe_dsr(obs, pop)
# ->
df %>%
  group_by(indicatorid, year, sex) %>%
  mutate(esp2013 = esp2013) %>%
  calculate_dsr(obs, pop, stdpop = esp2013)


## calculate both 95% and 99.8% CIs in single execution
df %>%
  group_by(indicatorid, year, sex) %>%
  phe_dsr(obs, pop, confidence = c(0.95, 0.998))
# ->
df %>%
  group_by(indicatorid, year, sex) %>%
  mutate(esp2013 = esp2013) %>%
  calculate_dsr(obs, pop, stdpop = esp2013, confidence = c(0.95, 0.998))

## alternatively, append the standard populations to your input dataframe
df %>%
  group_by(indicatorid, year, sex) %>%
  mutate(esp2013 = esp2013) %>%
  phe_dsr(obs, pop, stdpoptype = "field")
# ->
df %>%
  group_by(indicatorid, year, sex) %>%
  mutate(esp2013 = esp2013) %>%
  calculate_dsr(obs, pop, stdpop = esp2013)

publichealthengland/PHEindicatormethods documentation built on Dec. 15, 2024, 3:18 p.m.