multilevelGPSStratification: Stratification on GPS with multilevel treatments

In multilevelMatching: Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments

Description

Stratification on GPS with multilevel treatments

Usage

multilevelGPSStratification(Y, W, X, NS, GPSM = "multinomiallogisticReg",
  linearp = 0, nboot)

Arguments

Y	A continuous response vector (1 x n)
W	A treatment vector (1 x n) with numerical values indicating treatment groups
X	A covariate matrix (p x n) with no intercept
NS	The number of strata: (only required in the function multilevelGPSStratification)
GPSM	An indicator of the methods used for estimating GPS, options include "multinomiallogisticReg", "ordinallogisticReg", and "existing"
linearp	An indicator of subclassification on GPS (=0) or linear predictor of GPS (=1): (only required in the function multilevelGPSStratification)
nboot	The number of boot replicates for variance estimation: (only required in the function multilevelGPSStratification)

Value

A list with two elements, tauestimate, varestimate, where tauestimate is a vector of estimates for pairwise treatment effects, and varestimate is a vector of variance estimates, using bootstrapping method.

References

Yang, S., Imbens G. W., Cui, Z., Faries, D. E., & Kadziola, Z. (2016) Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments. Biometrics, 72, 1055-1065. https://doi.org/10.1111/biom.12505

Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. https://doi.org/10.1111/j.1468-0262.2006.00655.x

Abadie, A., & Imbens, G. W. (2016). Matching on the estimated propensity score. Econometrica, 84(2), 781-807. https://doi.org/10.3982/ECTA11293

See Also

multilevelGPSMatch; multilevelMatchX

Examples

simulated_data <- multilevelMatching::simulated_data
set.seed(123)
multilevelGPSStratification(
  Y = simulated_data$outcome ,
  W = simulated_data$treatment,
  X = simulated_data[ ,names(simulated_data) %in% paste0("covar", 1:6)],
  GPSM = "multinomiallogisticReg",
  NS = 5,
  linearp = TRUE,
  nboot = 10
)

multilevelMatching documentation built on May 8, 2019, 5:02 p.m.