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Calculate survey design weight within a NHANES dataset

Source: R/create_design.R
create_design.Rd

Input an NHANES dataset and apply the proper weight calculation. There are 3 categories of weights:

  1. Interview weight

  2. Mobile Exam Center (MEC) weight

  3. Fasting weight

The probability of being sampled for each type of NHANES category decreases from interview to fasting samples. Therefore, when selecting the proper weight, the practitioner should use the weight with the lowest probability when combining variables across categories. For example, when performing an analysis using demographics (interview), diabetes information (interview), and DEXA scanning (MEC), the associated MEC weight is the proper weight variable to use.

It is also important to select the proper year grouping for the cycle. NHANES cycles for 1999 and 2001 use 4-year sample weights, while all subsequent cycles use 2-year sample weights. This type of combination requires careful attention to:

  1. The variables used, to determine weight category (interview, MEC, fasting).

  2. The cycles (years) used, to select proper year grouping variable.

This function will allow the user to input a dataset, select analysis start & end years, and specify the type of weight category. The resulting survey design will calculate the proper weight and apply that when creating the design object.

NOTE: It is not required to specify variables for this function and it is highly recommended to perform preprocessing of variables before creating a complex design object.

See also as_survey_design

Usage

create_design(
  dsn,
  start_yr,
  end_yr,
  wt_type = c("interview", "mec", "fasting")
)

Arguments

dsn

Tibble or data-frame.

start_yr

Numeric. Lower bound for year filtering (inclusive). Must be an odd year representing a valid NHANES cycle start: 1999, 2001, 2003, ..., 2019, 2021. For example, use 2007 for the 2007-2008 cycle. Data will be filtered to include years between start_yr and end_yr.

end_yr

Numeric. Upper bound for year filtering (inclusive). Must be an odd year >= start_yr. Weight calculations are based on the number of cycles actually present in the filtered data, so it is valid to have gaps (e.g., start_yr=1999, end_yr=2017 with 2007-2010 missing).

wt_type

Character. Category of weight to be used. Use the weight category with the lowest probability of selection, but only if at least one variable from that category is to be used. Accepts full names ("interview", "mec", "fasting") or abbreviations ("int", "mec", "fast").

Value

A survey design object of class tbl_svy (from srvyr package) containing the calculated design weights and survey design metadata (PSUs, strata). Participants without valid weights for the specified weight type are automatically filtered out before design object creation. Participants with zero weights are retained in the design object but will be automatically excluded from most survey analyses.

Details

Weight Calculation for Combined Cycles

NHANES provides 4-year weights for the 1999-2000 and 2001-2002 cycles, while all subsequent cycles provide only 2-year weights. When combining multiple cycles:

  • If 1999 or 2001 cycles are included: Use the 4-year weight variable multiplied by 2/n where n is the total number of cycles. The numerator is 2 because the 4-year weight represents two 2-year cycles.

  • For cycles 2003 and beyond: Use the 2-year weight variable multiplied by 1/n.

  • The denominator n is always the total number of cycles in the analysis.

Example: Combining 4 cycles (1999, 2001, 2003, 2005):

  • 1999 & 2001: wtmec4yr * 2/4

  • 2003 & 2005: wtmec2yr * 1/4

Fasting weights follow the same rules: wtsaf4yr * 2/n for 1999/2001 and wtsaf2yr * 1/n for 2003+.

Fasting Subsample Weights

For fasting subsample analyses combining 1999-2002 cycles, the 4-year fasting weight (WTSAF4YR) exists in laboratory files (e.g., LAB10AM, LAB13AM) but is typically NOT in demographic files obtained via nhanesA. If your dataset includes 1999 or 2001 cycles with fasting data, ensure WTSAF4YR is present in the merged dataset. The function will validate its presence and apply the same 2/n scaling used for other 4-year weights.

Examples

# \donttest{
# Load demographics data
demo <- read_nhanes("demo")
#> Loading: DEMO
#> DEMO complete! (113,249 rows)

# Create design object with interview weights
design <- create_design(
  dsn = demo,
  start_yr = 1999,
  end_yr = 2011,
  wt_type = "interview"
)

# Combine with examination data and use MEC weights
bmx <- read_nhanes("bmx")
#> Loading: BMX
#> BMX complete! (105,626 rows)
combined <- demo |>
  dplyr::left_join(bmx, by = c("seqn", "year"))

design_mec <- create_design(
  dsn = combined,
  start_yr = 2007,
  end_yr = 2017,
  wt_type = "mec"
)
#> 
#> Note: 2428 participants have zero mec weights and will be retained in the
#> design object.
#> These participants were not in the subsample for this weight category
#> and will be automatically excluded from analyses by the {survey} package.
# }