tsri {OneSampleMR}

Two-stage residual inclusion (TSRI) estimators

Description

An excellent description of TSRI estimators is given by Terza et al. (2008). TSRI estimators proceed by fitting a first stage model of the exposure regressed upon the instruments (and possibly any measured confounders). From this the first stage residuals are estimated. A second stage model is then fitted of the outcome regressed upon the exposure and first stage residuals (and possibly measured confounders).

Usage

tsri(
  formula,
  instruments,
  data,
  subset,
  na.action,
  contrasts = NULL,
  t0 = NULL,
  link = "identity",
  ...
)

Arguments

formula, instruments

formula specification(s) of the regression relationship and the instruments. Either instruments is missing and formula has three parts as in y ~ x1 + x2 | z1 + z2 + z3 (recommended) or formula is y ~ x1 + x2 and instruments is a one-sided formula ~ z1 + z2 + z3 (only for backward compatibility).

data

an optional data frame containing the variables in the model. By default the variables are taken from the environment of the formula.

subset

an optional vector specifying a subset of observations to be used in fitting the model.

na.action

a function that indicates what should happen when the data contain NAs. The default is set by the na.action option.

contrasts

an optional list. See the contrasts.arg of stats::model.matrix().

t0

A vector of starting values for the gmm optimizer. This should have length equal to the number of exposures plus 1.

link

character; one of "identity" (the default), "logadd", "logmult", "logit". This is the link function for the second stage model. "identity" corresponds to linear regression; "logadd" is log-additive and corresponds to Poisson / log-binomial regression; "logmult" is log-multiplicative and corresponds to gamma regression; "logit" corresponds to logistic regression.

...

further arguments passed to or from other methods.

Details

TSRI estimators are sometimes described as a special case of control function estimators.

tsri() performs GMM estimation to ensure appropriate standard errors on its estimates similar to that described that described by Clarke et al. (2015). Terza (2017) described an alternative approach.

Value

An object of class "tsri" with the following elements

fit

the fitted object of class "gmm" from the call to gmm::gmm().

estci

a matrix of the estimates with their corresponding confidence interval limits.

link

a character vector containing the specificed link function.

References

Bowden J, Vansteelandt S. Mendelian randomization analysis of case-control data using structural mean models. Statistics in Medicine, 2011, 30, 6, 678-694. doi:10.1002/sim.4138

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

Dukes O, Vansteelandt S. A note on G-estimation of causal risk ratios. American Journal of Epidemiology, 2018, 187, 5, 1079-1084. doi:10.1093/aje/kwx347

Palmer T, Thompson JR, Tobin MD, Sheehan NA, Burton PR. Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses. International Journal of Epidemiology, 2008, 37, 5, 1161-1168. doi:10.1093/ije/dyn080

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

Terza JV, Basu A, Rathouz PJ. Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling. Journal of Health Economics, 2008, 27, 3, 531-543. doi:10.1016/j.jhealeco.2007.09.009

Terza JV. Two-stage residual inclusion estimation: A practitioners guide to Stata implementation. The Stata Journal, 2017, 17, 4, 916-938. doi:10.1177/1536867X1801700409

Examples

# Two-stage residual inclusion estimator
# with second stage logistic regression
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))))
dat          <- data.frame(Z, X, Y)
tsrilogitfit <- tsri(Y ~ X | Z , data = dat, link = "logit")
summary(tsrilogitfit)