Data Science

Apply Weights? It’s Easy in R with the Survey Package!

Posted on by Monika Wahi
You can do a population-based analysis if the original dataset used multi-stage sampling.
11
Nov

Apply weights is a task you sometimes need to do in public health, especially if you are dealing with a population-based dataset. Here is an example where I apply the weights in the NHANES dataset as demonstration (which you can read about here).

If you watch the video, you’ll see I’m using the P_DEMO.xpt file from NHANES, which is the demographics file. It has the weight variables in it. I import this into a dataframe in the R GUI environment named DG_a.

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Topic: Study Design

Data Curation Foundations

Designing Big Data Healthcare Studies: Part 1

Designing Big Data Healthcare Studies: Part 2

Data Science of Experimental Design

Apply Weights Kluge

The first thing you have to do before conducting a weighted design is a kluge. You have to create this variable named One that is filled entirely with the value 1. This will be used in later code for counting.

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DG_a$One <- c(1)

Next, I demonstrate how to create a binary flag. The idea is we will count the proportions of 1 vs. 0 in the dataset. I chose the source variable RIAGENDR, which is the gender variable. It is coded 2 for women, so I created a flag called WOMAN, which is coded as 1 when the respondent said 2 for RIAGENDR, and 0 otherwise. I also check my recode with a table command.

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DG_a$WOMAN <- 0
DG_a$WOMAN[DG_a$RIAGENDR == 2] <- 1
table(DG_a$RIAGENDR, DG_a$WOMAN, useNA = c("always"))

Now we are ready to use the survey package and the weight variables to construct a design.

Apply Weights Design

The way the survey package works is you first have to set up a design object, then use that object in the programming for calculating weighted estimates. Here is the original design object code I used.

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library(survey)
options(survey.lonely.psu = "adjust" )
nhanes_design <-
    svydesign(
        id = ~ SDMVPSU,
        strata = ~ SDMVPSU,
        data = DG_a ,
        weight = ~ WTINTPRP,
        nest = TRUE)

In the next step, we have to modify the design to accommodate our new binary variable WOMAN. But notice how we actually recode it as a factor variable on-the-fly, called WOMAN_f. That way, we can use the levels and labels option. Notice how we apply labels.

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nhanes_design <- 
    update( 
        nhanes_design,
        WOMAN_f = factor(WOMAN, levels = c(0,1), labels = c("Not Woman", "Woman")))

Get Weighted Proportions

Now, we can get weighted proportions of our WOMAN_f variable by running this code using the svymean command:

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svymean(~WOMAN_f, nhanes_design, na.rm = TRUE

And we can also get weighted counts. The first command counts the number the total dataset represents. This is where we use that One variable we made earlier, and we use the command svytotal. The second command gets weighted counts of the WOMAN_f variable using the svyby command.

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svytotal( ~ One , nhanes_design)
svyby( ~ One , ~ WOMAN_f, nhanes_design, svytotal)

See the output below. After weighting, women represented about 51% of the dataset representing about 320 million individuals, of which 158 million were women.

You can do biostatistics in R, SAS, or Python.

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You can do a population-based analysis if the original dataset used multi-stage sampling.
Apply Weights? It’s Easy in R with the Survey Package!

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Apply weights to get weighted proportions and counts! Read my blog post to learn how to use the survey package in R.

Monika Wahi

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