CODEWITHSUNDEEP
rregressionglmsurvey
IanH
IanH

Reputation: 161

Use of svyglm and svydesign with R for multistage stratified cluster design

I have a complicated data set which was made by a multistage stratified cluster design. I had originally analysed this using glm, however now realise that I have to use svyglm. I'm not quite sure about how is best to model the data utilising svyglm. I was wondering if anyone could help shed some light.

I am attempting to see the effect that a variety of covariates taken at time 1 have on a binary outcome taken at time 2.

The sampling strategy was as follows: state -> urban/rural -> district -> subdistrict -> village. Within each village, individuals were randomly selected, with each of these having an id (uniqid).

I have a variable in the df for each of these stages of the sampling strategy. I also have the following variables: outcome, age, sex, income, marital_status, urban_or_rural_area, uniqid, weights. The formula that I want for my regression equation is outcome ~ age + sex + income + marital_status + urban_or_rural_area . Weights are coded by the weights variable. I had set the family to binomial(link = logit).

If anyone has any idea how such an approach could be coded in R with svyglm I would be most appreciative. I'm quite confused as to what should be inputted as ID, fpc and nest. Do I have to specify all levels of the stratified design or just some?

Any direction, or resources which explain this well would be massively appreciated.

Upvotes: 1

Views: 643

Answers (1)

Thomas Lumley
Thomas Lumley

Reputation: 2765

You don't really give enough information about the design: which of the geographical units are strata and which are clusters. For example, my guess is that you sample both urban and rural in all states, and you don't sample all villages, but I don't know whether you sample all districts or subdistricts. I also don't know whether your overall sampling fraction is large or small (so whether the with-replacement approximation is ok)

Let's pretend you sample just some districts, so districts are your Primary Sampling Units, and that the overall sampling fraction of people is small. The design command is

your_design <- svydesign(id=~district, weights=~weights,
 strata=~interaction(state, urban_rural,drop=TRUE), 
 data=your_data_frame)

That is, the strata are combinations of state and urban/rural and any combinations that aren't in your data set don't exist in the population (maybe some states are all-rural or all-urban). Within each stratum you have districts, and only some of these appear in the sample. In your geographical hierarchy, districts are then the first level that is sampled rather than exhaustively enumerated.

You don't need fpc unless you want to specify the full multistage design without replacement.

The nest option is not about how the survey was done but is about how variables are coded. The US National Center for Health Statistics (bless their hearts) set up a lot of designs that have many strata and two primary sampling units per stratum. They call these primary sampling units 1 and 2; that is, they reuse the names 1 and 2 in every stratum. The svydesign function is set up to expect different sampling unit names in different strata, and to verify that each sampling unit name appears in just one stratum, as a check against data errors. This check has to be disabled for NCHS surveys and perhaps some others that also reuse sampling unit names. You can always leave out the nest option at first; svydesign will tell you if it might be needed.

Finally, the models:

svyglm(outcome ~ age + sex + income + marital_status + urban_or_rural_area,
  design=your_design, family=quasibinomial)

Using binomial or quasibinomial will give identical answers, but using binomial will give you a harmless warning about non-integer weights. If you use quasibinomial, the harmless warning is suppressed.

Upvotes: 1

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