sim_covariate: Simulating covariates

In ygeunkim/propensityml: Propensity score weighting using machine learning methods

Description

generates covariates in the references

Usage

sim_covariate(n, covmat = build_covariate())

Arguments

n	sample size
covmat	Covariance matrix of the covariates

Details

This function reproduces the setting in the paper in Setoguchi et al. and Lee et al. First generate binary covariates (w1, w3, w5, w6, w8, w9), and continuous covariates (w2, w4, w7, w10).

1. Generate 10-dim multivariate normal v1, v3, v5, v6, v8, v9 (corresponding to binary), and w7, w10

2. Dichotomize w1, w3, w5, w6, w8, w9

References

Setoguchi, S., Schneeweiss, S., Brookhart, M. A., Glynn, R. J., & Cook, E. F. (2008). Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiology and Drug Safety, 17(6), 546–555 https://doi.org/10.1002/pds.1555

Lee, B. K., Lessler, J., & Stuart, E. A. (2010). Improving propensity score weighting using machine learning. Statistics in Medicine. Statistics in Medicine, 29(3), 337-346. https://doi.org/10.1002/sim.3782

See Also

build_covariate

ygeunkim/propensityml documentation built on Jan. 1, 2021, 1:44 p.m.