calculate_ISRate: Calculate Indirectly Standardised Rates using...

In PHEindicatormethods: Common Public Health Statistics and their Confidence Intervals

Calculate Indirectly Standardised Rates using calculate_ISRate

Description

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

Usage

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

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). Alternatively, if not providing age breakdowns for observed events, field name from observed_totals containing the observed number of events within each grouping set ; 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 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
observed_totals	data.frame containing total observed events for each group, if not provided with age-breakdowns in data. Must only contain the count field (x) plus grouping columns required to join to data using the same grouping column names; default = NULL

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 internal 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.

This function directly replaced phe_isr which was fully deprecated in package version 2.0.0 due to ambiguous naming

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: assign_funnel_significance(), calculate_ISRatio(), 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(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))

## calculate multiple ISRs in single execution
df %>%
    group_by(indicatorid, year, sex) %>%
    calculate_ISRate(obs, pop, refdf$refcount, refdf$refpop)

## execute without outputting metadata fields
df %>%
    group_by(indicatorid, year, sex) %>%
    calculate_ISRate(obs, pop, refdf$refcount, refdf$refpop, type="standard", confidence=99.8)

## calculate 95% and 99.8% CIs in single execution
df %>%
    group_by(indicatorid, year, sex) %>%
    calculate_ISRate(obs, pop, refdf$refcount, refdf$refpop, confidence = c(0.95, 0.998))

## Calculate ISR when observed totals aren't available with age-breakdowns
observed_totals <- data.frame(indicatorid = rep(c(1234, 5678, 91011, 121314), each = 10),
                       year = rep(rep(2006:2010, each = 2),4),
                       sex = rep(rep(c("Male", "Female"), each = 1),20),
                       observed = sample(1500:2500, 40))

df %>%
    group_by(indicatorid, year, sex) %>%
    calculate_ISRate(observed, pop, refdf$refcount, refdf$refpop,
    observed_totals = observed_totals)

PHEindicatormethods documentation built on May 31, 2023, 8:13 p.m.