Package 'LTASR' reference manual

Title:	Functions to Replicate the Center for Disease Control and Prevention's 'LTAS' Software in R
Description:	A suite of functions for reading in a rate file in XML format, stratify a cohort, and calculate 'SMRs' from the stratified cohort and rate file.
Authors:	Stephen Bertke [aut, cre]
Maintainer:	Stephen Bertke <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.4
Built:	2024-12-21 06:51:39 UTC
Source:	CRAN

Checks all strata in py_table are contained in rate file

Description

Checks all strata in py_table are contained in rate file

Usage

checkStrata(py_table, rateobj)
checkStrata(py_table, rateobj)

Arguments

`py_table`	A stratified cohort created by `get_table`
`rateobj`	A rate object created by `parseRate`

Value

A list containing:

The py_table with strata removed not found in rateobj
The observations from py_table that were removed

Examples

library(LTASR)
library(dplyr)
library(purrr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Check Strata are in rate file
checkStrata(py_table, rateobj)

library(LTASR)
library(dplyr)
library(purrr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Check Strata are in rate file
checkStrata(py_table, rateobj)

Create exp_strata object

Description

exp_strata() creates an exp_strata that defines which variable to consider, any lag to be applied, and cutpoints for the strata.

Usage

exp_strata(var = character(), cutpt = numeric(), lag = 0)
exp_strata(var = character(), cutpt = numeric(), lag = 0)

Arguments

`var`	character naming the variable within the history data.frame to consider.
`cutpt`	numeric vector defining the cutpoints to use to stratify the calculated cumulative exposure for variable `var`. Should include min and max values (typically -Inf and Inf).
`lag`	numeric defining the lag, in years, to be applied to exposure variables. Default is 0 yrs (i.e. unlagged). Must be a whole number.

Value

an object of class exp_strata to be used in the get_table_history().

Examples

library(LTASR)
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 10)

library(LTASR)
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 10)

Expand data through range of date values

Description

Expand a data.frame to include all dates between a start and end value defined by parameters x and y

Usage

expand_dates(
  df,
  start,
  end,
  md_tmplt = seq(as.Date("1/1/2015", "%m/%d/%Y"), as.Date("12/31/2015",
    "%m/%d/%Y"), by = "day")
)
expand_dates(
  df,
  start,
  end,
  md_tmplt = seq(as.Date("1/1/2015", "%m/%d/%Y"), as.Date("12/31/2015",
    "%m/%d/%Y"), by = "day")
)

Arguments

`df`	Input data.frame
`start`	start date
`end`	end date
`md_tmplt`	Date vector that defines which dates within a year to output.

Value

A data.frame/tibble containing all variables of the input data.frame as well as a new variable, date, with repeated rows for each date between start and end spaced as defined by md_tmplt.

Examples

library(LTASR)
data <- data.frame(id = 1,
                   start = as.Date('3/1/2015', format='%m/%d/%Y'),
                   end = as.Date('3/15/2015', format='%m/%d/%Y'))
expand_dates(data, start, end)

library(LTASR)
data <- data.frame(id = 1,
                   start = as.Date('3/1/2015', format='%m/%d/%Y'),
                   end = as.Date('3/15/2015', format='%m/%d/%Y'))
expand_dates(data, start, end)

get_table reads in a data.frame/tibble containing basic demographic information for each person of the cohort and stratifies the person-time and deaths into 5-year age, 5-year calendar period, race, and sex strata. See Details for information on how the person file must be formatted.

Usage

get_table(persondf, rateobj, strata = dplyr::vars(), batch_size = 500)
get_table(persondf, rateobj, strata = dplyr::vars(), batch_size = 500)

Arguments

`persondf`	data.frame like object containing one row per person with the required demographic information
`rateobj`	a rate object created by the `parseRate` function, or the included rate object `us_119ucod_19602021`
`strata`	any additional variables contained in persondf on which to stratify. Must be wrapped in a `vars()` call from `dplyr`.
`batch_size`	a number specifying how many persons to stratify at a time. Default is 500

Details

The persondf tibble must contain the variables:

id,
gender (character: 'M'/'F'),
race (character: 'W'/'N'),
dob (date),
pybegin (date),
dlo (date),
vs (character: indicator identifying deaths as 'D')
rev (numeric: values 5-10),
code (character: ICD code)

Value

A data.frame with a row for each strata containing the number of observed deaths within each of the defined minors/outcomes (⁠_o1⁠-⁠_oxxx⁠) and the number of person days.

Examples

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

Stratify Person Table with Time Varying Co-variate

Description

get_table_history reads in a data.frame/tibble (persondf) containing basic demographic information for each person of the cohort as well as a data.frame/tibble (historydf) containing time varying exposure information and stratifies the person-time and deaths into 5-year age, 5-year calendar period, race, sex and exposure categories. See Details for information on how the person file and history file must be formatted.

Usage

get_table_history(
  persondf,
  rateobj,
  historydf,
  exps = list(),
  strata = dplyr::vars(),
  batch_size = 500
)
get_table_history(
  persondf,
  rateobj,
  historydf,
  exps = list(),
  strata = dplyr::vars(),
  batch_size = 500
)

Arguments

`persondf`	data.frame like object containing one row per person with the required demographic information.
`rateobj`	a rate object created by the `parseRate` function, or the included rate object `us_119ucod_19602021`.
`historydf`	data.frame like object containing one row per person and exposure period. An exposure period is a period of time where exposure levels remain constant. See `Details` for required variables.
`exps`	a list containing exp_strata objects created by `exp_strata()`.
`strata`	any additional variables contained in persondf on which to stratify. Must be wrapped in a `vars()` call from `dplyr`.
`batch_size`	a number specifying how many persons to stratify at a time. Default is 500.

Details

The persondf tibble must contain the variables:

id,
gender (character: 'M'/'F'),
race (character: 'W'/'N'),
dob (date),
pybegin (date),
dlo (date),
rev (numeric: values 5-10),
code (character: ICD code)

The historydf tibble must contain the variables:

id,
begin_dt (date),
end_dt (date),
<daily exposure levels>

Value

A data.frame with a row for each strata containing the number of observed deaths within each of the defined minors/outcomes (⁠_o1⁠-⁠_oxxx⁠) and the number of person days.

Examples

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import example history file
history <- history_example %>%
  mutate(begin_dt = as.Date(begin_dt, format='%m/%d/%Y'),
         end_dt = as.Date(end_dt, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Define exposure of interest. Create exp_strata object.The `employed` variable
#indicates (0/1) periods of employment and will be summed each day of each exposure
#period. Therefore, this calculates duration of employment in days. The cut-points
#used below will stratify by person-time with less than and greater than a
#year of employment (365 days of employment).
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)

#Stratify cohort by employed variable.
py_table <- get_table_history(persondf = person,
                              rateobj = rateobj,
                              historydf = history,
                              exps = list(exp1))

#Multiple exposures can be considered.
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)
exp2 <- exp_strata(var = 'exposure_level',
                   cutpt = c(-Inf, 0, 10000, 20000, Inf),
                   lag = 10)

#Stratify cohort by employed variable.
py_table <- get_table_history(persondf = person,
                              rateobj = rateobj,
                              historydf = history,
                              exps = list(exp1, exp2))

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import example history file
history <- history_example %>%
  mutate(begin_dt = as.Date(begin_dt, format='%m/%d/%Y'),
         end_dt = as.Date(end_dt, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Define exposure of interest. Create exp_strata object.The `employed` variable
#indicates (0/1) periods of employment and will be summed each day of each exposure
#period. Therefore, this calculates duration of employment in days. The cut-points
#used below will stratify by person-time with less than and greater than a
#year of employment (365 days of employment).
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)

#Stratify cohort by employed variable.
py_table <- get_table_history(persondf = person,
                              rateobj = rateobj,
                              historydf = history,
                              exps = list(exp1))

#Multiple exposures can be considered.
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)
exp2 <- exp_strata(var = 'exposure_level',
                   cutpt = c(-Inf, 0, 10000, 20000, Inf),
                   lag = 10)

#Stratify cohort by employed variable.
py_table <- get_table_history(persondf = person,
                              rateobj = rateobj,
                              historydf = history,
                              exps = list(exp1, exp2))

Stratify Person Table with Time Varying Co-variate

Description

get_table_history_est reads in a data.frame/tibble (persondf) containing basic demographic information for each person of the cohort as well as a data.frame/tibble (historydf) containing time varying exposure information and stratifies the person-time and deaths into 5-year age, 5-year calendar period, race, sex and exposure categories. Additionally, average cumulative exposure values for each strata and each exposure variable are included. These strata are more crudely calculated by taking regular steps (such as every 7 days) as opposed to evaluating every individual day. See Details for information on how the person file and history file must be formatted.

Usage

get_table_history_est(
  persondf,
  rateobj,
  historydf,
  exps,
  strata = dplyr::vars(),
  step = 7,
  batch_size = 25 * step
)
get_table_history_est(
  persondf,
  rateobj,
  historydf,
  exps,
  strata = dplyr::vars(),
  step = 7,
  batch_size = 25 * step
)

Arguments

`persondf`	data.frame like object containing one row per person with the required demographic information.
`rateobj`	a rate object created by the `parseRate` function, or the included rate object `us_119ucod_19602021`.
`historydf`	data.frame like object containing one row per person and exposure period. An exposure period is a period of time where exposure levels remain constant. See `Details` for required variables.
`exps`	a list containing exp_strata objects created by `exp_strata()`.
`strata`	any additional variables contained in persondf on which to stratify. Must be wrapped in a `vars()` call from `dplyr`.
`step`	numeric defining number of days to jump when calculating cumulative exposure values. Exact stratification specifies a step of 1 day.
`batch_size`	a number specifying how many persons to stratify at a time.

Details

The persondf tibble must contain the variables:

id,
gender (character: 'M'/'F'),
race (character: 'W'/'N'),
dob (date),
pybegin (date),
dlo (date),
rev (numeric: values 5-10),
code (character: ICD code)

The historydf tibble must contain the variables:

id,
begin_dt (date),
end_dt (date),
<daily exposure levels>

Value

A data.frame with a row for each strata containing the number of observed deaths within each of the defined minors/outcomes (⁠_o1⁠-⁠_oxxx⁠) and the number of person days.

Examples

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import example history file
history <- history_example %>%
  mutate(begin_dt = as.Date(begin_dt, format='%m/%d/%Y'),
         end_dt = as.Date(end_dt, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021
#Define exposure of interest. Create exp_strata object.The `employed` variable
#indicates (0/1) periods of employment and will be summed each day of each exposure
#period. Therefore, this calculates duration of employment in days. The cut-points
#used below will stratify by person-time with less than and greater than a
#year of employment (365 days of employment).
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)

#Stratify cohort by employed variable.
py_table <- get_table_history_est(persondf = person,
                                  rateobj = rateobj,
                                  historydf = history,
                                  exps = list(exp1))

#Multiple exposures can be considered.
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)
exp2 <- exp_strata(var = 'exposure_level',
                   cutpt = c(-Inf, 0, 10000, 20000, Inf),
                   lag = 10)

#Stratify cohort by employed variable.
py_table <- get_table_history_est(persondf = person,
                                  rateobj = rateobj,
                                  historydf = history,
                                  exps = list(exp1, exp2))

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import example history file
history <- history_example %>%
  mutate(begin_dt = as.Date(begin_dt, format='%m/%d/%Y'),
         end_dt = as.Date(end_dt, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021
#Define exposure of interest. Create exp_strata object.The `employed` variable
#indicates (0/1) periods of employment and will be summed each day of each exposure
#period. Therefore, this calculates duration of employment in days. The cut-points
#used below will stratify by person-time with less than and greater than a
#year of employment (365 days of employment).
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)

#Stratify cohort by employed variable.
py_table <- get_table_history_est(persondf = person,
                                  rateobj = rateobj,
                                  historydf = history,
                                  exps = list(exp1))

#Multiple exposures can be considered.
exp1 <- exp_strata(var = 'employed',
                   cutpt = c(-Inf, 365, Inf),
                   lag = 0)
exp2 <- exp_strata(var = 'exposure_level',
                   cutpt = c(-Inf, 0, 10000, 20000, Inf),
                   lag = 10)

#Stratify cohort by employed variable.
py_table <- get_table_history_est(persondf = person,
                                  rateobj = rateobj,
                                  historydf = history,
                                  exps = list(exp1, exp2))

Example History File for Testing

Description

A tibble containing example history file data to be used for testing and demonstration of the package

Usage

history_example
history_example

Format

A data frame with 4 rows and 5 variables:

id: unique identifier; numeric
begin_dt: beginning date of an exposure period; character
end_dt: beginning date of an exposure period; character
employed: a hypothetical variable indicating employment during the given exposure period; numeric (0/1)
exposure_level: a hypothetical variable identifying daily exposure levels to be summed to calculate a cumulative exposure; numeric

...

Source

Internally Generated

Map ICD codes to grouped minors

Description

Map ICD codes to grouped minors

Usage

mapDeaths(persondf, rateobj)
mapDeaths(persondf, rateobj)

Arguments

`persondf`	Person data.frame
`rateobj`	A rate object created from `parseRate`, or the included rate object `us_119ucod_19602021`.

Value

A data.frame for each death observed in the person file with the following variables: id, code, rev: from the persondf minor: the minor/outcome from the rate file that the death was mapped to

Examples

library(LTASR)

#Import example person file
person <- person_example

#Import default rate object
rateobj <- us_119ucod_19602021

#Check mapping of deaths to minors/outcomes
mapDeaths(person, rateobj)

library(LTASR)

#Import example person file
person <- person_example

#Import default rate object
rateobj <- us_119ucod_19602021

#Check mapping of deaths to minors/outcomes
mapDeaths(person, rateobj)

Parses LTAS rate file in .xml format

Description

Parses LTAS rate file in .xml format

Usage

parseRate(xmlpath)
parseRate(xmlpath)

Arguments

xmlpath

path of LTAS rate file

Value

returns a list containing:

$residual: the minor number where all unknown deaths will be assigned
$MinorDesc: a data.frame/tibble giving descriptions of minor numbers as well as how minors are mapped to majors
$mapping: a data.frame/tibble listing how each icd-code and revision will be mapped to each minor number
$age_cut: a numeric specifying cut-points for age strata
$cp_cut: a numeric specifying cut-points for calendar period strata

Example Person File for Testing

Description

A tibble containing example person file data to be used for testing and demonstration of the package

Usage

person_example
person_example

Format

A tibble with 3 observations and 9 variables:

id: unique identifier; character
gender: Gender/Sex; character 'M' or 'F'
race: Race; character 'W' or 'N'
dob: Date of Birth; character to be converted to date
pybegin: date to begin follow-up/at-risk accumulation, character to be converted to date
dlo: Date last observed; character to be converted to date
vs: indicator identifying the vital status of the cohort. A value of 'D' indicates an observed death; character
rev: ICD revision of the ICD code; numeric
code: ICD-code for the cause of death; character

...

Source

Internally Generated

Calculate SMRs for Custom minor groupings

Description

smr_major will collapse minor outcomes into "major" groupings as defined in the rate object, rateobj.

Usage

smr_custom(smr_minor_table, minor_grouping)
smr_custom(smr_minor_table, minor_grouping)

Arguments

`smr_minor_table`	A data.frame/tibble as created by `smr_minor` containing observed and expected number of deaths for each minor outcome
`minor_grouping`	A numeric vector defining which minors to group together

Value

A data.frame/tibble containing the expected and observed number of deaths as well the SMR, lower CI and upper CI for the outcome by the user

Examples

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Calculate SMRs for all minors
smr_minor_table <- smr_minor(py_table, rateobj)

#Calculate custom minor grouping for all deaths
smr_custom(smr_minor_table, 1:119)

#' #Calculate custom minor grouping for all deaths
smr_custom(smr_minor_table, 4:40)

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Calculate SMRs for all minors
smr_minor_table <- smr_minor(py_table, rateobj)

#Calculate custom minor grouping for all deaths
smr_custom(smr_minor_table, 1:119)

#' #Calculate custom minor grouping for all deaths
smr_custom(smr_minor_table, 4:40)

Calculate SMRs for Major groupings

Description

smr_major will collapse minor outcomes into "major" groupings as defined in the rate object, rateobj.

Usage

smr_major(smr_minor_table, rateobj)
smr_major(smr_minor_table, rateobj)

Arguments

`smr_minor_table`	A data.frame/tibble as created by `smr_minor` containing observed and expected number of deaths for each minor outcome
`rateobj`	A rate object created by `parseRate`, or the included rate object `us_119ucod_19602021`.

Value

A data.frame/tibble containing the expected and observed number of deaths as well as SMRs, lower CI and upper CI for each major as defined in the rate object rateobj

Examples

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Calculate SMRs for all minors
smr_minor_table <- smr_minor(py_table, rateobj)

#Calculate SMRs major groupings found within rate file
smr_major(smr_minor_table, rateobj)

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Calculate SMRs for all minors
smr_minor_table <- smr_minor(py_table, rateobj)

#Calculate SMRs major groupings found within rate file
smr_major(smr_minor_table, rateobj)

Calculate SMRs for Minors

Description

smr_minor calculates SMRs for all minor groupings found within the rate object, rateobj, for the stratified cohort py_table

Usage

smr_minor(py_table, rateobj)
smr_minor(py_table, rateobj)

Arguments

`py_table`	A stratified cohort created by `get_table`, or the included rate object `us_119ucod_19602021`.
`rateobj`	A rate object created by `parseRate`

Value

A dataframe/tibble containing the expected and observed number of deaths as well as SMRs, lower CI and upper CI for each minor found in the rate object rateobj

Examples

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Calculate SMRs for all minors
smr_minor(py_table, rateobj)

library(LTASR)
library(dplyr)

#Import example person file
person <- person_example %>%
  mutate(dob = as.Date(dob, format='%m/%d/%Y'),
         pybegin = as.Date(pybegin, format='%m/%d/%Y'),
         dlo = as.Date(dlo, format='%m/%d/%Y'))

#Import default rate object
rateobj <- us_119ucod_19602021

#Stratify person table
py_table <- get_table(person, rateobj)

#Calculate SMRs for all minors
smr_minor(py_table, rateobj)

119 UCOD U.S. Death Rate, 1960-2021

Description

A list containing referent underlying cause of death (UCOD) rate information for the US population from 1960-2021 for the 119 minor/outcome LTAS groupings

Usage

us_119ucod_19602021
us_119ucod_19602021

Format

A list with 4 elements:

residual: the minor/outcome number to which unknown/uncategorized outcomes will be mapped to
MinorDesc: a data.frame containing descriptions for each minor and major grouping
mapping: a tibble detailing which minor number each icd-code and revision combination will be mapped to
rates: the population referent rate for each minor for each gender/race/calendar period/age strata

...

Source

Available upon request from [email protected]

119 UCOD U.S. Death Rate, 1960-2022

Description

A list containing referent underlying cause of death (UCOD) rate information for the US population from 1960-2022 for the 119 minor/outcome LTAS groupings

Usage

us_119ucod_recent
us_119ucod_recent

Format

A list with 4 elements:

residual: the minor/outcome number to which unknown/uncategorized outcomes will be mapped to
MinorDesc: a data.frame containing descriptions for each minor and major grouping
mapping: a tibble detailing which minor number each icd-code and revision combination will be mapped to
rates: the population referent rate for each minor for each gender/race/calendar period/age strata

...

Source

Available upon request from [email protected]

Package 'LTASR'

Help Index

Checks all strata in py_table are contained in rate file

Description

Usage

Arguments

Value

Examples

Create exp_strata object

Description

Usage

Arguments

Value

Examples

Expand data through range of date values

Description

Usage

Arguments

Value

Examples

Stratify Person Table

Description

Usage

Arguments

Details

Value

Examples

Stratify Person Table with Time Varying Co-variate

Description

Usage

Arguments

Details

Value

Examples

Stratify Person Table with Time Varying Co-variate

Description

Usage

Arguments

Details

Value

Examples

Example History File for Testing

Description

Usage

Format

Source

Map ICD codes to grouped minors

Description

Usage

Arguments

Value

Examples

Parses LTAS rate file in .xml format

Description

Usage

Arguments

Value

Example Person File for Testing

Description

Usage

Format

Source

Calculate SMRs for Custom minor groupings

Description

Usage

Arguments

Value

Examples

Calculate SMRs for Major groupings

Description

Usage

Arguments

Value

Examples

Calculate SMRs for Minors

Description

Usage

Arguments

Value

Examples