# Introduction

Let \(Y\) be a binary response, \(A\) a binary exposure, and \(V\) a vector of covariates.

DAG for the statistical model with the dashed edge representing a potential interaction between exposure \(A\) and covariates \(V\).

In a common setting, the main interest lies in quantifying the treatment effect, \(\nu\), of \(A\) on \(Y\) adjusting for the set of covariates, and often a standard approach is to use a Generalized Linear Model (GLM):

\[g\{ E(Y\mid A,V) \} = A\nu^tW + \underset{\mathrm{nuisance}}{\mu^tZ}\]

with link function \(g\), and \(W = w(V)\), \(Z= v(V)\) known vector functions of \(V\).

The canonical link (logit) leads to nice computational properties (logistic regression) and parameters with an odds-ratio interpretation. But ORs are not collapsible even under randomization. For example

\[ E(Y\mid X) = E[ E(Y\mid X,Z) \mid X ] = E[\operatorname{expit}( \mu + \alpha X + \beta Z ) \mid X] \neq \operatorname{expit}[\mu + \alpha X + \beta E(Z\mid X)], \]

When marginalizing we leave the class of logistic regression. This non-collapsibility makes it hard to interpret odds-ratios and to compare results from different studies

Relative risks (and risk differences) are collapsible and generally considered easier to interpret than odds-ratios. Richardson et al (JASA, 2017) proposed a regression model for a binary exposures which solves the computational problems and need for parameter contraints that are associated with using for example binomial regression with a log-link function (or identify link for the risk difference) to obtain such parameter estimates. In the following we consider the relative risk as the target parameter

\[ \mathrm{RR}(v) = \frac{P(Y=1\mid A=1, V=v)}{P(Y=1\mid A=0, V=v)}. \]

Let \(p_a(V) = P(Y \mid A=a, V), a\in\{0,1\}\), the idea is then to posit a linear model for \[ \theta(v) = \log \big(RR(v)\big) \], i.e., \[\log \big(RR(v)\big) = \alpha^Tv,\]

and a nuisance model for the odds-product \[ \phi(v) = \log\left(\frac{p_{0}(v)p_{1}(v)}{(1-p_{0}(v))(1-p_{1}(v))}\right) \]

noting that these two parameters are variation independent as illustrated by the below L’Abbé plot.

      p0 <- seq(0,1,length.out=100)
      p1 <- function(p0,op) 1/(1+(op*(1-p0)/p0)^-1)
      plot(0, type="n", xlim=c(0,1), ylim=c(0,1),
         xlab="P(Y=1|A=0)", ylab="P(Y=1|A=1)", main="Constant odds product")
      for (op in exp(seq(-6,6,by=.25))) lines(p0,p1(p0,op), col="lightblue")

      p0 <- seq(0,1,length.out=100)
      p1 <- function(p0,rr) rr*p0
      plot(0, type="n", xlim=c(0,1), ylim=c(0,1),
         xlab="P(Y=1|A=0)", ylab="P(Y=1|A=1)", main="Constant relative risk")
      for (rr in exp(seq(-3,3,by=.25))) lines(p0,p1(p0,rr), col="lightblue")

Similarly, a model can be constructed for the risk-difference on the following scale

\[\theta(v) = \operatorname{arctanh} \big(RD(v)\big).\]

# Simulation

First the `targeted` package is loaded

    library(targeted)

This automatically imports lava (CRAN) which we can use to simulate from the Relative-Risk Odds-Product (RR-OP) model.

    m <- lava::lvm(a ~ x,
             lp.target ~ 1,
             lp.nuisance ~ x+z)
    m <- lava::binomial.rr(m, response="y", exposure="a", target.model="lp.target", nuisance.model="lp.nuisance")

The `lvm` call first defines the linear predictor for the exposure to be of the form

\[\mathrm{LP}_A := \mu_A + \alpha X\]

and the linear predictors for the /target parameter/ (relative risk) and the /nuisance parameter/ (odds product) to be of the form

\[\mathrm{LP}_{RR} := \mu_{RR},\]

\[\mathrm{LP}_{OP} := \mu_{OP} + \beta_x X + \beta_z Z.\]

The covariates are by default assumed to be independent and standard normal \(X, Z\sim\mathcal{N}(0,1)\), but their distribution can easily be altered using the `lava::distribution` method.

The `binomial.rr` function

    args(lava::binomial.rr)

    function (x, response, exposure, target.model, nuisance.model,
        exposure.model = binomial.lvm(), ...)
    NULL

then defines the link functions, i.e.,

\[\operatorname{logit}(E[A\mid X,Z]) = \mu_A + \alpha X,\]

\[\operatorname{log}(E[Y\mid X,Z, A=1]/E[Y\mid X, A=0]) = \mu_{RR},\]

\[\operatorname{log}\{p_1(X,Z)p_0(X,Z)/[(1-p_1(X,Z))(1-p_0(X,Z))]\} = \mu_{OP}+\beta_x X + \beta_z Z\]

with \(p_a(X,Z)=E(Y\mid A=a,X,Z)\).

The risk-difference model with the RD parameter modeled on the \(\operatorname{arctanh}\) scale can be defined similarly using the `binomial.rd` method

    args(lava::binomial.rd)

    function (x, response, exposure, target.model, nuisance.model,
        exposure.model = binomial.lvm(), ...)
    NULL

We can inspect the parameter names of the modeled

    coef(m)

                 m1              m2              m3              p1              p2
                "a"     "lp.target"   "lp.nuisance"           "a~x" "lp.nuisance~x"
                 p3              p4
    "lp.nuisance~z"          "a~~a"

Here the intercepts of the model are simply given the same name as the variables, such that \(\mu_A\) becomes `a`, and the other regression coefficients are labeled using the “~”-formula notation, e.g., \(\alpha\) becomes `a~x`.

Intercepts are by default set to zero and regression parameters set to one in the simulation. Hence to simulate from the model with \((mu_A, \mu_{RR}, \mu_{OP}, \alpha, \beta_x, \beta_z)^T = (-1,1,-2,2,1,1)^T\), we define the parameter vector `p` given by

    p <- c('a'=-1, 'lp.target'=1, 'lp.nuisance'=-1, 'a~x'=2)

and then simulate from the model using the `sim` method

    d <- lava::sim(m, 1e4, p=p, seed=1)

    head(d)

      a          x lp.target lp.nuisance          z y
    1 0 -0.6264538         1  -2.4307854 -0.8043316 0
    2 0  0.1836433         1  -1.8728823 -1.0565257 0
    3 0 -0.8356286         1  -2.8710244 -1.0353958 0
    4 1  1.5952808         1  -0.5902796 -1.1855604 1
    5 0  0.3295078         1  -1.1709317 -0.5004395 1
    6 0 -0.8204684         1  -2.3454571 -0.5249887 0

Notice, that in this simulated data the target parameter \(\mu_{RR}\) has been set to `lp.target =` 1.

# Estimation

## MLE

We start by fitting the model using the maximum likelihood estimator.

    args(riskreg_mle)

    function (y, a, x1, x2 = x1, weights = rep(1, length(y)), std.err = TRUE,
        type = "rr", start = NULL, control = list(), ...)
    NULL

The `riskreg_mle` function takes vectors/matrices as input arguments with the response `y`, exposure `a`, target parameter design matrix `x1` (i.e., the matrix \(W\) at the start of this text), and the nuisance model design matrix `x2` (odds product).

We first consider the case of a correctly specified model, hence we do not consider any interactions with the exposure for the odds product and simply let `x1` be a vector of ones, whereas the design matrix for the log-odds-product depends on both \(X\) and \(Z\)

    x1 <- model.matrix(~1, d)
    x2 <- model.matrix(~x+z, d)

    fit1 <- with(d, riskreg_mle(y, a, x1, x2, type="rr"))
    fit1

                             Estimate Std.Err    2.5%   97.5%    P-value
    (Intercept)                0.9512 0.03319  0.8862  1.0163 1.204e-180
    odds-product:(Intercept)  -1.0610 0.05199 -1.1629 -0.9591  1.377e-92
    odds-product:x             1.0330 0.05944  0.9165  1.1495  1.230e-67
    odds-product:z             1.0421 0.05285  0.9386  1.1457  1.523e-86

The parameters are presented in the same order as the columns of `x1`and `x2`, hence the target parameter estimate is in the first row

    estimate(fit1, keep=1)

                Estimate Std.Err   2.5% 97.5%    P-value
    (Intercept)   0.9512 0.03336 0.8858 1.017 7.159e-179

## DRE

We next fit the model using a double robust estimator (DRE) which introduces a model for the exposure \(E(A=1\mid V)\) (propensity model). The double-robustness stems from the fact that the this estimator remains consistent in the union model where either the odds-product model or the propensity model is correctly specified. With both models correctly specified the estimator is efficient.

    with(d, riskreg_fit(y, a, target=x1, nuisance=x2, propensity=x2, type="rr"))

                Estimate Std.Err   2.5% 97.5%    P-value
    (Intercept)   0.9372  0.0339 0.8708 1.004 3.004e-168

The usual /formula/-syntax can be applied using the `riskreg` function. Here we illustrate the double-robustness by using a wrong propensity model but a correct nuisance paramter (odds-product) model:

      riskreg(y~a, nuisance=~x+z, propensity=~z, data=d, type="rr")

                Estimate Std.Err   2.5% 97.5%    P-value
    (Intercept)   0.9511 0.03333 0.8857 1.016 4.547e-179

Or vice-versa

      riskreg(y~a, nuisance=~z, propensity=~x+z, data=d, type="rr")

                Estimate Std.Err   2.5% 97.5%    P-value
    (Intercept)   0.9404 0.03727 0.8673 1.013 1.736e-140

whereas the MLE in this case yields a biased estimate of the relative risk:

      fit2 <- with(d, riskreg_mle(y, a, x1=model.matrix(~1,d), x2=model.matrix(~z, d)))
      estimate(fit2, keep=1)

                Estimate Std.Err  2.5% 97.5% P-value
    (Intercept)    1.243 0.02778 1.189 1.298       0

## Interactions

The more general model where \[\log RR(V) = A \alpha^TV\] for a subset \(V\) of the covariates can be estimated using the `target` argument:

    fit <- riskreg(y~a, target=~x, nuisance=~x+z, data=d)
    fit

                Estimate Std.Err     2.5%   97.5%    P-value
    (Intercept)  0.95267 0.03365  0.88673 1.01862 2.361e-176
    x           -0.01078 0.03804 -0.08534 0.06378  7.769e-01

As expected we do not see any evidence of an effect of \(X\) on the relative risk with the 95% confidence limits clearly overlapping zero.

Note, that when the `propensity` argument is omitted as above, the same design matrix is used for both the odds-product model and the propensity model.

## Risk-difference

The syntax for fitting the risk-difference model is similar. To illustrate this we simulate some new data from this model

    m2 <- lava::binomial.rd(m, response="y", exposure="a", target.model="lp.target", nuisance.model="lp.nuisance")
    d2 <- lava::sim(m2, 1e4, p=p)

And we can then fit the DRE with the syntax

    riskreg(y~a, nuisance=~x+z, data=d2, type="rd")

                Estimate Std.Err   2.5% 97.5% P-value
    (Intercept)    1.036 0.02122 0.9947 1.078       0

## Influence-function

The DRE is a regular and asymptotic linear (RAL) estimator, hence \[\sqrt{n}(\widehat{\alpha}_{\mathrm{DRE}} - \alpha) = \frac{1}{\sqrt{n}}\sum_{i=1}^{n} \phi_{\mathrm{eff}}(Z_{i}) + o_{p}(1)\] where \(Z_i = (Y_i, A_i, V_i), i=1,\ldots,n\) are the i.i.d. observations and \(\phi_{\mathrm{eff}}\) is the influence function.

The influence function can be extracted using the `IC` method

    head(IC(fit))

      (Intercept)          x
    1   0.6226459 -0.4585424
    2   1.2319959  0.7925974
    3   0.3941326 -0.5798067
    4  -0.8854891  2.9621437
    5  -6.9949137 -5.2133648
    6   0.5853934 -0.7571452

# SessionInfo

    sessionInfo()

    R version 4.6.1 (2026-06-24)
    Platform: aarch64-apple-darwin25.5.0
    Running under: macOS Tahoe 26.5.2

    Matrix products: default
    BLAS:   /Users/klaus/.asdf/installs/r/4.6.1/lib/R/lib/libRblas.dylib
    LAPACK: /Users/klaus/.asdf/installs/r/4.6.1/lib/R/lib/libRlapack.dylib;  LAPACK version 3.12.1

    locale:
    [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

    time zone: Europe/Copenhagen
    tzcode source: internal

    attached base packages:
    [1] stats     graphics  grDevices utils     datasets  methods   base

    other attached packages:
    [1] targeted_0.8

    loaded via a namespace (and not attached):
     [1] mets_1.3.11            cli_3.6.6              knitr_1.51
     [4] rlang_1.3.0            xfun_0.60              otel_0.2.0
     [7] jsonlite_2.0.0         future.apply_1.20.2    listenv_1.0.0
    [10] lava_1.9.2             htmltools_0.5.9        rmarkdown_2.31
    [13] grid_4.6.1             evaluate_1.0.5         fastmap_1.2.0
    [16] numDeriv_2016.8-1.1    mvtnorm_1.4-2          yaml_2.3.12
    [19] timereg_2.0.7          compiler_4.6.1         codetools_0.2-20
    [22] Rcpp_1.1.2             future_1.70.0          lattice_0.22-9
    [25] digest_0.6.39          R6_2.6.1               parallelly_1.48.0
    [28] parallel_4.6.1         splines_4.6.1          Matrix_1.7-5
    [31] RcppArmadillo_15.4.0-1 tools_4.6.1            globals_0.19.1
    [34] survival_3.8-6