PHEindicatormethods (version 2.0.2)

phe_dsr: 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).

Usage

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

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

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

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_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)

## 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))

## calculate DSRs for multiple grouping sets in single execution

df %>%
    group_by(indicatorid, year, sex) %>%
    phe_dsr(obs, pop, type = "standard")