Diagnostics for Confounding of Time-Varying and Other Joint Exposures


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Documentation for package ‘confoundr’ version 1.2

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apply.scope Function to subset the output table from balance() or diagnose() to covariate balance metrics at a certain distance (e.g. a certain recency) or produce estimates that average over person-time.
balance Function to create a balance table for a specified diagnostic. Takes input from lengthen() or omit.history().
catie_sim Simulated data, loosely based on the Clinical Antipsychotic Trial of Intervention Effectiveness (CATIE) study.
diagnose Function to loop over the lengthen() and balance() functions.
example_sml Artifical data set used to illustrate the functionality of confoundr.
lengthen Function to create a "tidy" dataframe where the key observation is the pairing of exposure and covariate measurement times
makehistory.one Function to create exposure history for a single time varying exposure.
makehistory.two Function to create joint exposure history for two distinct time-varying exposures
makeplot Function to create balance plot for a specified diagnostic. Takes input from balance() or apply.scope() or diagnose().
omit.history Function to remove irrelevant covariate history from a tidy dataframe used to construct balance tables and plots. Takes input from lengthen(), balance() or diagnose().
toy_long Artifical data set used to test the functionality of confoundr.
toy_long_dropoutN Artifical data set used to test the functionality of confoundr.
toy_long_dropoutY Artifical data set used to test the functionality of confoundr.
toy_wide_censN Artifical data set used to test the functionality of confoundr.
toy_wide_censY Artifical data set used to test the functionality of confoundr.
toy_wide_dropoutN Artifical data set used to test the functionality of confoundr.
toy_wide_dropoutY Artifical data set used to test the functionality of confoundr.
widen Function to transform data from person-time format to person format suitable for lengthen()