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().
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. 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. 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)
#>
#> Call:
#> ivreg::ivreg(formula = Y ~ X | Z, data = dat1)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.8341 0.1659 0.1659 0.2723 0.2723
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.72766 0.02515 28.938 <2e-16 ***
#> X 0.10647 0.04743 2.245 0.025 *
#>
#> Diagnostic tests:
#> df1 df2 statistic p-value
#> Weak instruments 1 998 432.087 <2e-16 ***
#> Wu-Hausman 1 997 1.844 0.175
#> Sargan 0 NA NA NA
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.4105 on 998 degrees of freedom
#> Multiple R-Squared: 0.03246, Adjusted R-squared: 0.03149
#> Wald test: 5.038 on 1 and 998 DF, p-value: 0.02501
#>
# 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)
#>
#> Call:
#> ivreg::ivreg(formula = Y ~ X | G1 + G2 + G3, data = dat2)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.2728 -0.2728 -0.2034 0.7272 0.7966
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.20345 0.05217 3.900 0.000103 ***
#> X 0.06932 0.07339 0.945 0.345113
#>
#> Diagnostic tests:
#> df1 df2 statistic p-value
#> Weak instruments 3 996 63.882 <2e-16 ***
#> Wu-Hausman 1 997 0.004 0.951
#> Sargan 2 NA 0.225 0.893
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.4327 on 998 degrees of freedom
#> Multiple R-Squared: 0.006156, Adjusted R-squared: 0.00516
#> Wald test: 0.8922 on 1 and 998 DF, p-value: 0.3451
#>