Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

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)

Arguments

Value

A list containing:

1. The py_table with strata removed not found in rateobj

2. 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)

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)

Arguments

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)

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")
)

Arguments

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)

Stratify Person Table

Description

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)

Arguments

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)

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
)

Arguments

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))

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
)

Arguments

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))

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

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)

Arguments

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)

Parses LTAS rate file in .xml format

Description

Parses LTAS rate file in .xml format

Usage

parseRate(xmlpath)

Arguments

Value

returns a list containing:

1. $residual: the minor number where all unknown deaths will be assigned

2. $MinorDesc: a data.frame/tibble giving descriptions of minor numbers as well as how minors are mapped to majors

3. $mapping: a data.frame/tibble listing how each icd-code and revision will be mapped to each minor number

4. $age_cut: a numeric specifying cut-points for age strata

5. $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

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)

Arguments

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)

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)

Arguments

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)

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)

Arguments

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)

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

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 sbertke@cdc.gov

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

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 sbertke@cdc.gov