This package is used to assess non-linear exposure-outcome relationships using instrumental variable (IV) analysis in the context of Mendelian randomisation (MR). In this package, there are two IV methods for investigating the shape of the exposure-outcome relationship: a fractional polynomial method (fracpoly_mr) and a piecewise linear method (piecewise_mr). The population (i.e. one-sample) is divided into strata using the exposure distribution, and a causal effect is estimated, referred to as a localized average causal effect (LACE), in each stratum. The fractional polynomial method fits across these LACE using meta-regression. The piecewise linear method estimates a continuous piecewise linear function by consecutively adding the LACE together.
- fracpoly_mr - this method performs IV analysis using fractional polynomials
- piecewise_mr - this method performs IV analysis using piecewise linear function
install.packages("devtools")
library(devtools)
install_github("jrs95/nlmr")
library(nlmr)
### IV (g), exposure (x) & outcome (y)
epsx <- rexp(10000)
u <- runif(10000, 0, 1)
g <- rbinom(10000, 2, 0.3)
epsy <- rnorm(10000)
ag <- 0.25
x <- 1 + ag * g + u + epsx
y <- 0.15 * x^2 + 0.8 * u + epsy
### Covariates (c)
c1 = rnorm(10000)
c2 = rnorm(10000)
c3 = rbinom(10000, 2, 0.33)
c = data.frame(c1 = c1, c2 = c2, c3 = as.factor(c3))
### Analyses
fp = fracpoly_mr(y, x, g, c, family = "gaussian", q = 10, d = "both", ci = "model_se", fig = TRUE)
summary(fp)
plm = piecewise_mr(y, x, g, c, family = "gaussian", q = 10, nboot = 50, fig = TRUE)
summary(plm)
Staley JR and Burgess S. Semiparametric methods for estimation of a non-linear exposure-outcome relationship using instrumental variables with application to Mendelian randomization. Genet Epidemiol 2017;41(4):341-352.