OneSampleMR (version 0.1.5)

asmm: Additive structural mean model

Description

asmm is not a function. This helpfile is to note that the additive structural mean model (ASMM) is simply fit with a linear IV estimator, such as available in ivreg::ivreg().

Details

For a binary outcome the ASMM estimates a causal risk difference.

References

Clarke PS, Palmer TM, Windmeijer F. Estimating structural mean models with multiple instrumental variables using the Generalised Method of Moments. Statistical Science, 2015, 30, 1, 96-117. tools:::Rd_expr_doi("10.1214/14-STS503")

Palmer TM, Sterne JAC, Harbord RM, Lawlor DA, Sheehan NA, Meng S, Granell R, Davey Smith G, Didelez V. Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. American Journal of Epidemiology, 2011, 173, 12, 1392-1403. tools:::Rd_expr_doi("10.1093/aje/kwr026")

Robins JM. The analysis of randomised and nonrandomised AIDS treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: A Focus on AIDS (L. Sechrest, H. Freeman and A. Mulley, eds.). 1989. 113–159. US Public Health Service, National Center for Health Services Research, Washington, DC.

Examples

# Single instrument example
# Data generation from the example in the ivtools ivglm() helpfile
set.seed(9)
n    <- 1000
psi0 <- 0.5
Z    <- rbinom(n, 1, 0.5)
X    <- rbinom(n, 1, 0.7*Z + 0.2*(1 - Z))
m0   <- plogis(1 + 0.8*X - 0.39*Z)
Y    <- rbinom(n, 1, plogis(psi0*X + log(m0/(1 - m0))))
dat1 <- data.frame(Z, X, Y)
fit1 <- ivreg::ivreg(Y ~ X | Z, data = dat1)
summary(fit1)

# Multiple instrument example
set.seed(123456)
n    <- 1000
psi0 <- 0.5
G1   <- rbinom(n, 2, 0.5)
G2   <- rbinom(n, 2, 0.3)
G3   <- rbinom(n, 2, 0.4)
U    <- runif(n)
pX   <- plogis(0.7*G1 + G2 - G3 + U)
X    <- rbinom(n, 1, pX)
pY   <- plogis(-2 + psi0*X + U)
Y    <- rbinom(n, 1, pY)
dat2 <- data.frame(G1, G2, G3, X, Y)
fit2 <- ivreg::ivreg(Y ~ X | G1 + G2 + G3, data = dat2)
summary(fit2)