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Generate summaries of NHANES

nhanes_summarize.Rd

The description should include

  • what is NHANES

  • Who is included in the data to be summarized

  • How is the summary computed

Usage

nhanes_summarize(
  data,
  key,
  outcome_variable,
  outcome_quantiles = NULL,
  outcome_stats = NULL,
  group_variable = NULL,
  group_cut_n = NULL,
  group_cut_type = NULL,
  stratify_variable = NULL,
  time_variable = "svy_year",
  time_values = NULL,
  pool = FALSE,
  subset_calls = list(),
  standard_variable = "demo_age_cat",
  standard_weights = NULL,
  simplify_output = TRUE
)

Arguments

data

[data.frame] A set of NHANES data with one row per survey participant and one column per variable. See nhanes_data for more details See Details for specific requirements. See nhanes_data for an example.

key

[data.frame] A data set with one row per variable and with columns that describe the variable. See nhanes_key for more details See Details for specific requirements. See nhanes_key for an example.

outcome_variable

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

outcome_quantiles

[numeric(1+)] The quantiles to be summarized for a continuous outcome. The default is c(0.25, 0.50, 0.75). For example,

  • outcome_quantiles = c(.5) will compute the 50th percentile (i.e., the median)

  • outcome_quantiles = c(.25, .5, .75) will compute the 25th, 50th, and 75th percentile.

  • outcome_quantiles = seq(.1, .9, by = .1) will compute every 10th percentile, except for the 0th and 100th

outcome_stats

[character(1+)]

The statistics that should be computed. Multiple statistics may be requested. Valid options depend on the type of outcome to be summarized. For continuous outcomes, valid options include

  • 'mean': estimates the mean value of the outcome

  • 'quantile': estimates 25th, 50th, and 75th percentile of the outcome.

For categorical outcomes, valid options include

  • 'percentage': estimates the prevalence of the outcome

  • 'percentage_kg': estimates the prevalence and uses Korn and Graubard's method to estimate a 95% confidence interval

  • 'count': estimates the number of US adults with the outcome.

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_variable

[character(1)] The name of the time variable. The default, svy_year, corresponds to the variable in nhanes_data that indicates which 2 year NHANES cycle an observation was collected in.

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).

subset_calls

[named list(n)]

the names of subset_calls are variable names, and the values are values of the variable to include in the subsetted data. For example, subset_calls = list("demo_gender" = "Women") will subset the data to include rows where demo_gender is equal to "Women". Multiple entries are allowed and collapsed with the logical & operator. For example, subset_calls = list(demo_gender = "Women", bp_med_use = "Yes") will subset the data to include rows where demo_gender is equal to 'Women' AND bp_med_use is equal to "Yes"

standard_variable

[character(1)]

The name of the variable used to create standardization groups. The default is to use demo_age_cat, which leads to age standardization.

standard_weights

[numeric(n)]

The proportionate weights for each group defined by the standard variable. The number of weights should equal the number of groups defined by standard_variable and all weights must be >0.

simplify_output

[logical(1)]

The type of output returned will be nhanes_design if simplify_output is FALSE and a data.table otherwise.

Value

if simplify_output is TRUE, a data.table. Otherwise, an nhanes_design object is returned.

Examples


nhanes_summarize(data = nhanes_data,
                 key = nhanes_key,
                 outcome_variable = "bp_sys_mean")
#>      svy_year statistic estimate std_error ci_lower ci_upper n_obs
#>        <fctr>    <char>    <num>     <num>    <num>    <num> <int>
#>  1: 1999-2000      mean 122.7799 0.7232809 121.3623 124.1975  4694
#>  2: 2001-2002      mean 122.4941 0.4625177 121.5876 123.4006  5181
#>  3: 2003-2004      mean 122.6814 0.4827482 121.7352 123.6275  4836
#>  4: 2005-2006      mean 122.2579 0.4650055 121.3465 123.1692  5012
#>  5: 2007-2008      mean 121.5421 0.3779936 120.8013 122.2830  5664
#>  6: 2009-2010      mean 120.4879 0.4809511 119.5452 121.4305  6043
#>  7: 2011-2012      mean 121.6124 0.6464260 120.3454 122.8794  5334
#>  8: 2013-2014      mean 121.4265 0.3198856 120.7996 122.0535  5692
#>  9: 2015-2016      mean 123.3559 0.4613174 122.4517 124.2600  5551
#> 10: 2017-2020      mean 123.1297 0.3709403 122.4027 123.8567  8010
#>     unreliable_status unreliable_reason review_needed review_reason
#>                <lgcl>            <char>        <lgcl>        <char>
#>  1:             FALSE              <NA>         FALSE          <NA>
#>  2:             FALSE              <NA>         FALSE          <NA>
#>  3:             FALSE              <NA>         FALSE          <NA>
#>  4:             FALSE              <NA>         FALSE          <NA>
#>  5:             FALSE              <NA>         FALSE          <NA>
#>  6:             FALSE              <NA>         FALSE          <NA>
#>  7:             FALSE              <NA>         FALSE          <NA>
#>  8:             FALSE              <NA>         FALSE          <NA>
#>  9:             FALSE              <NA>         FALSE          <NA>
#> 10:             FALSE              <NA>         FALSE          <NA>