## S3 method for class "hybrid" hybrid(outcome, probit, modifiers = NULL, init = NULL, id = NULL, se = "R")
(0/1)numeric vector on the left hand side (1 indicating medication use), and predictors of medication use on the right hand side (right hand side permitted to contain variables on the right hand side of the outcome equation).
NULL, the average treatment effect will be estimated under the assumption of no effect modification.
alpha(probit model parameters),
beta(outcome model parameters),
eta(an intercept, with or without effect mofidier paramters),
sigmay(outcome error standard deviation),
rho(error correlation). If
NULL, an initial value will be chosen through OLS linear regression and probit-link GLM without regard to endogeneity.
"M"for model-based standard errors (based on inverse observed Fisher information), or
"R"for robust standard errors (based on methods of Huber and White). Defaults to
idis provided for clustered data, the cluster-robust variance estimator (with working independence) will be used even if the user specifies type
hybridprints a summary of the coefficient estimates, standard errors, Wald-based confidence intervals, and p-values for the outcome model, the treatment effects (and potentially effect modifiers), and the medication use probit model. prints a summary of the coefficient estimates, standard errors, Wald-based confidence intervals, and p-values for the outcome model, the treatment effects (and potentially effect modifiers), and the medication use probit model.
BFGSis used). The probit model and error correlation parameters are weakly identified and hence the error variance is set at unity. The data must be complete (no missing values) and numeric, with the exception of factors, which may be used on the right hand side of equations.
Maddala GS. Limited-dependent and qualitative variables in econometrics. Cambridgeshire: Cambridge University Press; 1983.
Spieker AJ, Delaney JAC, and McClelland RL. Evaluating the treatment effects model for estimation of cross-sectional associations between risk factors and cardiovascular biomarkers influenced by medication use. Pharmacoepidemiology and Drug Safety 24(12), 1286-1296.
#- Generate Data -# require(mvtnorm) set.seed(1) N <- 2000 X1 <- rnorm(N, 0, 1); X2 <- rnorm(N, 0, 1); X3 <- rnorm(N, 0, 1); errors <- rmvnorm(N, sigma = 50*matrix(c(1, 0.5, 0.5, 1), nrow = 2)) Y <- 50 + X1 + X2 + errors[,1] Z <- rep(0, N) Z[(-5 + X1 + X3 + errors[,2]) > 0] <- 1 Y[Z == 1] <- Y[Z == 1] - 0.5*X1[Z == 1] #- Estimate Model with No Effect Modification -# hybrid(Y ~ X1 + X2, probit = Z ~ X1 + X3) #- Estimate Model with Effect Modification -# hybrid(Y ~ X1 + X2, probit = Z ~ X1 + X3, modifiers = Z ~ X1) #- Estimate Model with Effect Modification and Model-Based Variance -# hybrid(Y ~ X1 + X2, probit = Z ~ X1 + X3, modifiers = Z ~ X1, se = "M")