Complex Survey Data

CLARITE provides preliminary support for handling complex survey designs, similar to how the r-package survey works.

A SurveyDesignSpec can be created, which is used to obtain survey design objects for specific variables:

sd_discovery = clarite.survey.SurveyDesignSpec(survey_df=survey_design_discovery,

There are a few different options for the ‘single_cluster’ parameter, which controls how strata with single clusters are handled in the linearized covariance calculation:

  • error - Throw an error
  • scaled - Use the average value of other strata
  • centered - Use the average of all observations
  • certainty - Single-cluster strata don’t contribute to the variance

After a SurveyDesignSpec is created, it can be passed into the ewas function to utilize the survey design parameters:

ewas_discovery = clarite.analyze.ewas("logBMI", covariates, nhanes_discovery_bin, nhanes_discovery_cat, nhanes_discovery_cont, sd_discovery, cov_method='stata')