sim_outcome: Simulating Dataset for Various Scenarios

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

Description

generates a dataset for various scenarios

Usage

sim_outcome(
  n,
  covmat = build_covariate(),
  scenario = LETTERS[1:7],
  b = c(0, 0.8, -0.25, 0.6, -0.4, -0.8, -0.5, 0.7),
  a = c(-3.85, 0.3, -0.36, -73, -0.2, 0.71, -0.19, 0.26),
  gam = -0.4
)

Arguments

n	sample size
covmat	Covariance matrix of the covariates
scenario	scenarios
b	coefficients for confounder and exposure predictors
a	coefficients in outcome model
gam	coefficient of exposure

Details

About scenarios:

• A: additivity and linearity

• B: mild non-linearity

• C: moderate non-linearity

• D: mild non-additivity

• E: mild non-additivity and non-linearity

• F: moderate non-linearity

• F: moderate non-additivity and non-linearity

See Appendix of Setoguchi et al.

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

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