get_table_history {LTASR}

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

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))