PHEindicatormethods (version 1.1.5)

phe_isr: Calculate Indirectly Standardised Rates using phe_isr

Description

Calculates indirectly standardised rates with confidence limits using Byar's [1] or exact [2] CI method.

Usage

phe_isr(data, x, n, x_ref, n_ref, refpoptype = "vector", type = "full",
  confidence = 0.95, multiplier = 1e+05)

Arguments

data

data.frame containing the data to be standardised, pre-grouped if multiple ISRs 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

x_ref

the observed number of events in the reference population for each standardisation category (eg age band); unquoted string referencing a numeric vector or field name from data depending on value of refpoptype; no default

n_ref

the reference population for each standardisation category (eg age band); unquoted string referencing a numeric vector or field name from data depending on value of refpoptype; no default

refpoptype

whether x_ref and n_ref have been specified as vectors 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 90 and 100; 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 observed events, expected events, indirectly standardised rate, lower confidence limit, upper confidence limit, confidence level, statistic and method for each grouping set

Notes

User MUST ensure that x, n, x_ref and n_ref vectors are all ordered by the same standardisation category values as records will be matched by position. For numerators >= 10 Byar's method [1] is applied using the byars_lower and byars_upper functions. For small numerators Byar's method is less accurate and so an exact method [2] based on the Poisson distribution is used.

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] Armitage P, Berry G. Statistical methods in medical research (4th edn). Oxford: Blackwell; 2002.

See Also

Other PHEindicatormethods package functions: phe_dsr, phe_life_expectancy, phe_mean, phe_proportion, phe_quantile, phe_rate, phe_sii, phe_smr

Examples

# NOT RUN {
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))

refdf <- data.frame(refcount = sample(200, 19, replace = TRUE),
                    refpop = sample(10000:20000, 19, replace = TRUE))

df %>%
    group_by(indicatorid, year, sex) %>%
    phe_isr(obs, pop, refdf$refcount, refdf$refpop)

## OR

df %>%
    group_by(indicatorid, year, sex) %>%
    phe_isr(obs, pop, refdf$refcount, refdf$refpop, type="standard", confidence=99.8)

# }