Modify an NHANES design

Usage

nhanes_design_update(
  x,
  outcome_variable = NULL,
  group_variable = NULL,
  group_cut_n = NULL,
  group_cut_type = NULL,
  stratify_variable = NULL,
  time_values = NULL,
  pool = NULL
)

Arguments

x

an nhanes_design object

outcome_variable

[character(1)] The name of the outcome variable to be summarized.

group_variable

[character(1)] The name of the group variable. See Details for a description of the group variable and the stratify variable.

group_cut_n

[integer(1)] The number of groups to form using the group variable. This is only relevant if the group variable is continuous, and can be omitted. Default is 3

group_cut_type

[character(1)] The method used to create groups with the grouping variable. This is only relevant i fthe group variable is continuous, and can be omitted. Valid options are:

"interval": equal interval width, e.g., three groups with ages of 0 to <10, 10 to <20, and 20 to < 30 years.
"frequency": equal frequency, e.g., three groups with ages of 0 to <q, q to <p, and p to <r, where q, p, and r are selected so that roughly the same number of people are in each group.

stratify_variable

[character(1)] the name of the stratify variable. See Details for a description of the group variable and the stratify variable.

time_values

[character(1+)] The time values that will be included in this design object. The default is to include all time values present in data. Valid options are:

'most_recent': includes the most recent time value.
'last_5': includes the 5 most recent time values.
'all': includes all time values present in data.
You can also give a vector of specific time values, e.g., c("2009-2010", "2011-2012", "2013-2014"), if these values are present in the time_variable column (they are for nhanes_data).

pool

[logical(1)] If FALSE (the default), results are presented for individual times, separately. If TRUE, data from each time value are pooled together. Note that only contiguous cycles should be pooled together, e.g., using pool = TRUE with time_values = 'last_5' is okay, but using pool = TRUE with time_values = c("2009-2010", "2013-2014") is not recommended (that would be a strange result to interpret).

Value

a modified nhanes_design object.

Examples

library(cardioStatsUSA)

ds <- nhanes_design(data = nhanes_data,
                    key = nhanes_key, 
                    time_values = 'most_recent',
                    outcome_variable = 'bp_sys_mean')

print(ds)

## -------------------------------- NHANES design --------------------------------
## 
## Outcome variable: bp_sys_mean
## - label: Systolic blood pressure (SBP), mm Hg
## - type: continuous
## - description: Mean systolic blood pressure in mm Hg. This is based on the
##     average of up to 3 readings. Participants were required to have at least one
##     reading. Overall, >95% of participants with at least one systolic blood
##     pressure reading had three readings.  From 1999-2000 through 2015-2016,
##     systolic blood pressure was measured using a mercury sphygmomanometer.  In
##     2017-2020, systolic blood pressure was measured using an oscillometric device.
##     The systolic blood pressure in 2017-2020 was calibrated to the mercury device
##     by adding 1.5 mm Hg to the mean measured oscillometric value.
## 
## Group variable: None
## Stratify variable: None
## 
## N observations
## - Unweighted: 8,965
## - Weighted: 247,835,696
## --------------------------------------------------------------------------------

There are a few ways the design can be updated. For example, we can add a group variable:

nhanes_design_update(ds, group_variable = 'demo_gender')

## -------------------------------- NHANES design --------------------------------
## 
## Outcome variable: bp_sys_mean
## - label: Systolic blood pressure (SBP), mm Hg
## - type: continuous
## - description: Mean systolic blood pressure in mm Hg. This is based on the
##     average of up to 3 readings. Participants were required to have at least one
##     reading. Overall, >95% of participants with at least one systolic blood
##     pressure reading had three readings.  From 1999-2000 through 2015-2016,
##     systolic blood pressure was measured using a mercury sphygmomanometer.  In
##     2017-2020, systolic blood pressure was measured using an oscillometric device.
##     The systolic blood pressure in 2017-2020 was calibrated to the mercury device
##     by adding 1.5 mm Hg to the mean measured oscillometric value.
## 
## Group variable: demo_gender
## - label: Gender
## - type: categorical
## - description: Self-reported gender
## 
## Stratify variable: None
## 
## N observations
## - Unweighted: 8,965
## - Weighted: 247,835,696
## --------------------------------------------------------------------------------

Pipes and multiple modifications are allowed:

ds %>% 
 nhanes_design_update(group_variable = 'demo_gender',
                      stratify_variable = 'demo_race')

## -------------------------------- NHANES design --------------------------------
## 
## Outcome variable: bp_sys_mean
## - label: Systolic blood pressure (SBP), mm Hg
## - type: continuous
## - description: Mean systolic blood pressure in mm Hg. This is based on the
##     average of up to 3 readings. Participants were required to have at least one
##     reading. Overall, >95% of participants with at least one systolic blood
##     pressure reading had three readings.  From 1999-2000 through 2015-2016,
##     systolic blood pressure was measured using a mercury sphygmomanometer.  In
##     2017-2020, systolic blood pressure was measured using an oscillometric device.
##     The systolic blood pressure in 2017-2020 was calibrated to the mercury device
##     by adding 1.5 mm Hg to the mean measured oscillometric value.
## 
## Group variable: demo_gender
## - label: Gender
## - type: categorical
## - description: Self-reported gender
## 
## Stratify variable: demo_race
## - label: Race/ethnicity
## - type: categorical
## - description: Self-reported race/ethnicity. From 1999-2000 through 2009-2010
##     this was available as non-Hispanic White, non-Hispanic Black, Hispanic and
##     other. From 2011-2012 through 2017-2020 this was available as non-Hispanic
##     White, non-Hispanic Black, non-Hispanic Asian, Hispanic and other.
## 
## N observations
## - Unweighted: 8,965
## - Weighted: 247,835,696
## --------------------------------------------------------------------------------