R/phe_DSR.R
In publichealthengland/PHEindicatormethods: Common Public Health Statistics and their Confidence Intervals
Defines functions
phe_dsr
Documented in
phe_dsr
# -------------------------------------------------------------------------------------------------
#' Calculate Directly Standardised Rates using phe_dsr
#'
#' @description
#' `r lifecycle::badge("deprecated")`
#'
#' 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.
#'
#' @param data data.frame containing the data to be standardised, pre-grouped if
#' multiple DSRs required; unquoted string; no default
#' @param 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
#' @param n field name from data containing the populations for each
#' standardisation category (eg ageband) within each grouping set (eg area);
#' unquoted string; no default
#' @param 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
#' @param stdpoptype whether the stdpop has been specified as a vector or a
#' field name from data; quoted string "field" or "vector"; default = "vector"
#' @param 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"
#' @param 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
#' @param multiplier the multiplier used to express the final values (eg 100,000
#' = rate per 100,000); numeric; default 100,000
#'
#' @return 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
#'
#' @importFrom rlang sym quo_name
#' @import dplyr
#' @export
#'
#' @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)
#'
#' @section 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. \cr \cr 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.
#'
#' @keywords internal
#'
#' @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. \cr \cr
#' (2) Dobson A et al. Confidence intervals for weighted sums of Poisson parameters. Stat Med 1991;10:457-62.
#'
#'
#' @family PHEindicatormethods package functions
# -------------------------------------------------------------------------------------------------
# define the DSR function using Dobson method
phe_dsr <- function(data, x, n, stdpop = esp2013, stdpoptype = "vector",
type = "full", confidence = 0.95, multiplier = 100000) {
# call lifecycle warning
lifecycle::deprecate_soft("2.1.0", "phe_dsr()", "calculate_dsr()")
# check required arguments present
if (missing(data)|missing(x)|missing(n)) {
stop("function phe_dsr requires at least 3 arguments: data, x, n")
}
# check same number of rows per group
if (n_distinct(select(ungroup(count(data)),n)) != 1) {
stop("data must contain the same number of rows for each group")
}
# check stdpop is valid and append to data
if (!(stdpoptype %in% c("vector","field"))) {
stop("valid values for stdpoptype are vector and field")
} else if (stdpoptype == "vector") {
if (pull(slice(select(ungroup(count(data)),n),1)) != length(stdpop)) {
stop("stdpop length must equal number of rows in each group within data")
}
data <- mutate(data,stdpop_calc = stdpop)
} else if (stdpoptype == "field") {
if (deparse(substitute(stdpop)) %in% colnames(data)) {
data <- mutate(data,stdpop_calc = {{ stdpop }} )
} else stop("stdpop is not a field name from data")
}
# validate arguments
if (any(pull(data, {{ x }}) < 0, na.rm=TRUE)) {
stop("numerators must all be greater than or equal to zero")
} else if (any(pull(data, {{ n }}) <= 0)) {
stop("denominators must all be greater than zero")
} else if (!(type %in% c("value", "lower", "upper", "standard", "full"))) {
stop("type must be one of value, lower, upper, standard or full")
} else if (length(confidence) >2) {
stop("a maximum of two confidence levels can be provided")
} else if (length(confidence) == 2) {
if (!(confidence[1] == 0.95 & confidence[2] == 0.998)) {
stop("two confidence levels can only be produced if they are specified as 0.95 and 0.998")
}
} else if ((confidence < 0.9)|(confidence > 1 & confidence < 90)|(confidence > 100)) {
stop("confidence level must be between 90 and 100 or between 0.9 and 1")
}
# calculate DSR and CIs
if (length(confidence) == 2) {
# if two confidence levels requested
conf1 <- confidence[1]
conf2 <- confidence[2]
# calculate DSR and CIs
phe_dsr <- data %>%
mutate(wt_rate = na.zero({{ x }}) * .data$stdpop_calc / ({{ n }}),
sq_rate = na.zero({{ x }}) * (.data$stdpop_calc / ({{ n }}))^2, na.rm=TRUE) %>%
summarise(total_count = sum({{ x }}, na.rm=TRUE),
total_pop = sum({{ n }}),
value = sum(.data$wt_rate) / sum(.data$stdpop_calc) * multiplier,
vardsr = 1/sum(.data$stdpop_calc)^2 * sum(.data$sq_rate),
lower95_0cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_lower(sum({{ x }}, na.rm=TRUE), conf1) - sum({{ x }}, na.rm=TRUE)) * multiplier,
upper95_0cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_upper(sum({{ x }}, na.rm=TRUE), conf1) - sum({{ x }}, na.rm=TRUE)) * multiplier,
lower99_8cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_lower(sum({{ x }}, na.rm=TRUE), conf2) - sum({{ x }}, na.rm=TRUE)) * multiplier,
upper99_8cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_upper(sum({{ x }}, na.rm=TRUE), conf2) - sum({{ x }}, na.rm=TRUE)) * multiplier,
.groups = "keep") %>%
select(!c("vardsr")) %>%
mutate(confidence = "95%, 99.8%",
statistic = paste("dsr per",format(multiplier,scientific=F)),
method = "Dobson")
# remove DSR calculation for total counts < 10
phe_dsr <- phe_dsr %>%
mutate(across(c("value", "upper95_0cl", "lower95_0cl",
"upper99_8cl", "lower99_8cl"),
function(x) if_else(.data$total_count < 10, NA_real_, x)),
statistic = if_else(.data$total_count < 10,
"dsr NA for total count < 10",
.data$statistic))
# drop fields not required based on value of type argument
if (type == "lower") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "upper95_0cl", "upper99_8cl",
"confidence", "statistic", "method"))
} else if (type == "upper") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "lower95_0cl", "lower99_8cl",
"confidence", "statistic", "method"))
} else if (type == "value") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "lower95_0cl", "lower99_8cl", "upper95_0cl", "upper99_8cl",
"confidence", "statistic", "method"))
} else if (type == "standard") {
phe_dsr <- phe_dsr %>%
select(!c("confidence", "statistic", "method"))
}
} else {
# scale confidence level if single value specified
if (confidence >= 90) {
confidence <- confidence/100
}
# calculate DSR with a single CI
phe_dsr <- data %>%
mutate(wt_rate = na.zero({{ x }}) * .data$stdpop_calc / ({{ n }}),
sq_rate = na.zero({{ x }}) * (.data$stdpop_calc / ({{ n }}))^2, na.rm=TRUE) %>%
summarise(total_count = sum({{ x }},na.rm=TRUE),
total_pop = sum({{ n }}),
value = sum(.data$wt_rate) / sum(.data$stdpop_calc) * multiplier,
vardsr = 1/sum(.data$stdpop_calc)^2 * sum(.data$sq_rate),
lowercl = .data$value +
sqrt((.data$vardsr / sum({{ x }},na.rm=TRUE))) *
(byars_lower(sum({{ x }},na.rm=TRUE),
confidence) - sum({{ x }},na.rm=TRUE)) * multiplier,
uppercl = .data$value + sqrt((.data$vardsr / sum({{ x }}, na.rm=TRUE))) *
(byars_upper(sum({{ x }},na.rm=TRUE),
confidence) - sum({{ x }},na.rm=TRUE)) * multiplier,
.groups = "keep") %>%
select(!c("vardsr")) %>%
mutate(confidence = paste(confidence*100,"%",sep=""),
statistic = paste("dsr per",format(multiplier,scientific=F)),
method = "Dobson")
# remove DSR calculation for total counts < 10
phe_dsr <- phe_dsr %>%
mutate(across(c("value", "uppercl", "lowercl"),
function(x) if_else(.data$total_count < 10, NA_real_, x)),
statistic = if_else(.data$total_count < 10,
"dsr NA for total count < 10",
.data$statistic))
# drop fields not required based on value of type argument
if (type == "lower") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "uppercl", "confidence", "statistic", "method"))
} else if (type == "upper") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "lowercl", "confidence", "statistic", "method"))
} else if (type == "value") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "lowercl", "uppercl", "confidence", "statistic", "method"))
} else if (type == "standard") {
phe_dsr <- phe_dsr %>%
select(!c("confidence", "statistic", "method"))
}
}
return(phe_dsr)
}
publichealthengland/PHEindicatormethods documentation built on Dec. 15, 2024, 3:18 p.m.
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Defines functions phe_dsr
Documented in phe_dsr
# -------------------------------------------------------------------------------------------------
#' Calculate Directly Standardised Rates using phe_dsr
#'
#' @description
#' `r lifecycle::badge("deprecated")`
#'
#' 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.
#'
#' @param data data.frame containing the data to be standardised, pre-grouped if
#' multiple DSRs required; unquoted string; no default
#' @param 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
#' @param n field name from data containing the populations for each
#' standardisation category (eg ageband) within each grouping set (eg area);
#' unquoted string; no default
#' @param 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
#' @param stdpoptype whether the stdpop has been specified as a vector or a
#' field name from data; quoted string "field" or "vector"; default = "vector"
#' @param 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"
#' @param 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
#' @param multiplier the multiplier used to express the final values (eg 100,000
#' = rate per 100,000); numeric; default 100,000
#'
#' @return 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
#'
#' @importFrom rlang sym quo_name
#' @import dplyr
#' @export
#'
#' @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)
#'
#' @section 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. \cr \cr 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.
#'
#' @keywords internal
#'
#' @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. \cr \cr
#' (2) Dobson A et al. Confidence intervals for weighted sums of Poisson parameters. Stat Med 1991;10:457-62.
#'
#'
#' @family PHEindicatormethods package functions
# -------------------------------------------------------------------------------------------------
# define the DSR function using Dobson method
phe_dsr <- function(data, x, n, stdpop = esp2013, stdpoptype = "vector",
type = "full", confidence = 0.95, multiplier = 100000) {
# call lifecycle warning
lifecycle::deprecate_soft("2.1.0", "phe_dsr()", "calculate_dsr()")
# check required arguments present
if (missing(data)|missing(x)|missing(n)) {
stop("function phe_dsr requires at least 3 arguments: data, x, n")
}
# check same number of rows per group
if (n_distinct(select(ungroup(count(data)),n)) != 1) {
stop("data must contain the same number of rows for each group")
}
# check stdpop is valid and append to data
if (!(stdpoptype %in% c("vector","field"))) {
stop("valid values for stdpoptype are vector and field")
} else if (stdpoptype == "vector") {
if (pull(slice(select(ungroup(count(data)),n),1)) != length(stdpop)) {
stop("stdpop length must equal number of rows in each group within data")
}
data <- mutate(data,stdpop_calc = stdpop)
} else if (stdpoptype == "field") {
if (deparse(substitute(stdpop)) %in% colnames(data)) {
data <- mutate(data,stdpop_calc = {{ stdpop }} )
} else stop("stdpop is not a field name from data")
}
# validate arguments
if (any(pull(data, {{ x }}) < 0, na.rm=TRUE)) {
stop("numerators must all be greater than or equal to zero")
} else if (any(pull(data, {{ n }}) <= 0)) {
stop("denominators must all be greater than zero")
} else if (!(type %in% c("value", "lower", "upper", "standard", "full"))) {
stop("type must be one of value, lower, upper, standard or full")
} else if (length(confidence) >2) {
stop("a maximum of two confidence levels can be provided")
} else if (length(confidence) == 2) {
if (!(confidence[1] == 0.95 & confidence[2] == 0.998)) {
stop("two confidence levels can only be produced if they are specified as 0.95 and 0.998")
}
} else if ((confidence < 0.9)|(confidence > 1 & confidence < 90)|(confidence > 100)) {
stop("confidence level must be between 90 and 100 or between 0.9 and 1")
}
# calculate DSR and CIs
if (length(confidence) == 2) {
# if two confidence levels requested
conf1 <- confidence[1]
conf2 <- confidence[2]
# calculate DSR and CIs
phe_dsr <- data %>%
mutate(wt_rate = na.zero({{ x }}) * .data$stdpop_calc / ({{ n }}),
sq_rate = na.zero({{ x }}) * (.data$stdpop_calc / ({{ n }}))^2, na.rm=TRUE) %>%
summarise(total_count = sum({{ x }}, na.rm=TRUE),
total_pop = sum({{ n }}),
value = sum(.data$wt_rate) / sum(.data$stdpop_calc) * multiplier,
vardsr = 1/sum(.data$stdpop_calc)^2 * sum(.data$sq_rate),
lower95_0cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_lower(sum({{ x }}, na.rm=TRUE), conf1) - sum({{ x }}, na.rm=TRUE)) * multiplier,
upper95_0cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_upper(sum({{ x }}, na.rm=TRUE), conf1) - sum({{ x }}, na.rm=TRUE)) * multiplier,
lower99_8cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_lower(sum({{ x }}, na.rm=TRUE), conf2) - sum({{ x }}, na.rm=TRUE)) * multiplier,
upper99_8cl = .data$value + sqrt((.data$vardsr/sum({{ x }}, na.rm=TRUE)))*
(byars_upper(sum({{ x }}, na.rm=TRUE), conf2) - sum({{ x }}, na.rm=TRUE)) * multiplier,
.groups = "keep") %>%
select(!c("vardsr")) %>%
mutate(confidence = "95%, 99.8%",
statistic = paste("dsr per",format(multiplier,scientific=F)),
method = "Dobson")
# remove DSR calculation for total counts < 10
phe_dsr <- phe_dsr %>%
mutate(across(c("value", "upper95_0cl", "lower95_0cl",
"upper99_8cl", "lower99_8cl"),
function(x) if_else(.data$total_count < 10, NA_real_, x)),
statistic = if_else(.data$total_count < 10,
"dsr NA for total count < 10",
.data$statistic))
# drop fields not required based on value of type argument
if (type == "lower") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "upper95_0cl", "upper99_8cl",
"confidence", "statistic", "method"))
} else if (type == "upper") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "lower95_0cl", "lower99_8cl",
"confidence", "statistic", "method"))
} else if (type == "value") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "lower95_0cl", "lower99_8cl", "upper95_0cl", "upper99_8cl",
"confidence", "statistic", "method"))
} else if (type == "standard") {
phe_dsr <- phe_dsr %>%
select(!c("confidence", "statistic", "method"))
}
} else {
# scale confidence level if single value specified
if (confidence >= 90) {
confidence <- confidence/100
}
# calculate DSR with a single CI
phe_dsr <- data %>%
mutate(wt_rate = na.zero({{ x }}) * .data$stdpop_calc / ({{ n }}),
sq_rate = na.zero({{ x }}) * (.data$stdpop_calc / ({{ n }}))^2, na.rm=TRUE) %>%
summarise(total_count = sum({{ x }},na.rm=TRUE),
total_pop = sum({{ n }}),
value = sum(.data$wt_rate) / sum(.data$stdpop_calc) * multiplier,
vardsr = 1/sum(.data$stdpop_calc)^2 * sum(.data$sq_rate),
lowercl = .data$value +
sqrt((.data$vardsr / sum({{ x }},na.rm=TRUE))) *
(byars_lower(sum({{ x }},na.rm=TRUE),
confidence) - sum({{ x }},na.rm=TRUE)) * multiplier,
uppercl = .data$value + sqrt((.data$vardsr / sum({{ x }}, na.rm=TRUE))) *
(byars_upper(sum({{ x }},na.rm=TRUE),
confidence) - sum({{ x }},na.rm=TRUE)) * multiplier,
.groups = "keep") %>%
select(!c("vardsr")) %>%
mutate(confidence = paste(confidence*100,"%",sep=""),
statistic = paste("dsr per",format(multiplier,scientific=F)),
method = "Dobson")
# remove DSR calculation for total counts < 10
phe_dsr <- phe_dsr %>%
mutate(across(c("value", "uppercl", "lowercl"),
function(x) if_else(.data$total_count < 10, NA_real_, x)),
statistic = if_else(.data$total_count < 10,
"dsr NA for total count < 10",
.data$statistic))
# drop fields not required based on value of type argument
if (type == "lower") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "uppercl", "confidence", "statistic", "method"))
} else if (type == "upper") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "value", "lowercl", "confidence", "statistic", "method"))
} else if (type == "value") {
phe_dsr <- phe_dsr %>%
select(!c("total_count", "total_pop", "lowercl", "uppercl", "confidence", "statistic", "method"))
} else if (type == "standard") {
phe_dsr <- phe_dsr %>%
select(!c("confidence", "statistic", "method"))
}
}
return(phe_dsr)
}
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