vignettes/riskregression.Rmd
riskregression.Rmd
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
\[\theta(v) = \operatorname{arctanh} \big(RD(v)\big).\]
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 <- lvm(a ~ x,
lp.target ~ 1,
lp.nuisance ~ x+z)
m <- 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(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(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 <- 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.
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
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
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.
The syntax for fitting the risk-difference model is similar. To illustrate this we simulate some new data from this model
m2 <- binomial.rd(m, response="y", exposure="a", target.model="lp.target", nuisance.model="lp.nuisance")
d2 <- 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) 0.9858 0.02022 0.9462 1.025 0
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
sessionInfo()
#> R version 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
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#> time zone: UTC
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] targeted_0.4 lava_1.7.3
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