Datasets were simulated using baseline covariates (sampling with replacement) from the Effects of Aspirin in Gestation and Reproduction (EAGeR) study. Data generating mechanisms were described in our manuscript (Zhong et al. (inpreparation), Am. J. Epidemiol.). True marginal causal effects on risk difference, log risk ratio and log odds ratio scales were attached to the dataset attributes (true_rd, true_logrr,true_logor).
data(eager_sim_obs)An object of class data.frame with 200 rows and 8 columns:
binary, simulated outcome which is condition on all other covariates in the dataset
binary, simulated exposure which is conditon on all other covarites expect sim_Y.
binary, indicator of the eligibility stratum
count, number of prior pregnancy losses
continuous, age in years
count, months of conception attempts prior to randomization
continuous, body mass index
continuous, mean arterial blood pressure
Schisterman, E.F., Silver, R.M., Lesher, L.L., Faraggi, D., Wactawski-Wende, J., Townsend, J.M., Lynch, A.M., Perkins, N.J., Mumford, S.L. and Galai, N., 2014. Preconception low-dose aspirin and pregnancy outcomes: results from the EAGeR randomised trial. The Lancet, 384(9937), pp.29-36.
Zhong, Y., Naimi, A.I., Kennedy, E.H., (In preparation). AIPW: An R package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology