knitr::opts_chunk$set(warning=FALSE, message=FALSE)

In many fields researchers are interested in assessing the population-level effect of a particular treatment on an outcome of interest. The treatment might correspond to a drug, a potentially harmful exposure exposure, or a policy intervention. Often, the treatment may not be randomized due to ethical or logistical reasons. This has sparked great interest in developing statistical methodology to estimate population-level effects from observational data. Estimation of these effects often requires accounting for putative confounding factors, that is, factors related to whether a participant receives treatment and to the participant's outcome. In many settings, researchers measure many potential confounders, for example using administrative databases or genetic sequencing technology. Due to the curse-of-dimensionality, common statistical estimation techniques are not feasible for such high-dimensional data without restrictive modeling assumptions. When these assumptions are violated, estimates of the population-level effect of a treatment may have large bias.

This has sparked an interest in using adaptive estimation techniques from the
machine learning literature to control for high-dimensional confounders when
estimating treatment effects. However, facilitating proper inference (e.g.,
confidence intervals and hypothesis tests) about estimates of effects based on
adaptive estimators is challenging. In particular, standard causal inference
techniques, including both G-computation (GCOMP) estimators
[@robins:1986:mathmod] and inverse probability of treatment weighted (IPTW)
estimators [@horvitzthompson:1952:jasa, @robins:2000:epi], fail to yield valid
inference for common estimators of effects. More complex proposals have been
developed that *are capable* of utilizing adaptive methods to control for
confounding while still yielding valid inference. These techniques include
augmented inverse probability of treatment estimation (AIPTW)
[@robins:1994:jasa] and targeted minimum loss-based estimation (TMLE)
[@vdlrubin:2006:ijb]. Under assumptions, these estimators have limiting normal
distributions with estimable variance. The asymptotic variance estimates then
serve as the basis for constructing confidence intervals and hypothesis tests.

As an intermediate step, the AIPTW and TMLE estimators require estimation of
two key nuisance parameters: the probability of treatment as a function of
confounders (the propensity score, hereafter referred to as the *PS*) and the
average outcome as a function of confounders and treatment (the outcome
regression, *OR*). In addition to their desirable inferential properties, AIPTW
and TMLE estimators also enjoy the property of double robustness. That is, the
estimators are consistent for the population-level effect of interest if either
of the PS or OR is consistently estimated. This property gives the AIPTW and
TMLE estimators a natural appeal: inconsistency in the estimation of one
nuisance parameter may be mitigated by the consistent estimation of the other.
As such, these estimators have increased in popularity in recent years.
However, recent work has shown that the double robustness property does not
necessarily extend to inferential properties about these estimators when
adaptive estimators of the PS and OR are used
[@vdl:2014:ijb,@benkeser:2017:biometrika]. Instead, valid inference requires
consistent estimation of *both* the PS *and* the OR. van der Laan (2014)
proposed new TMLE estimators have an asymptotic normal sampling distribution
with estimable variance under consistent estimation of *either* the PS *or* the
OR, paving the way for inference that is doubly-robust. Benkeser et al. (2017)
proposed modifications of these estimators with similar robustness properties
and illustrated their practical performance.

The two proposed procedures that yield doubly-robust inference are quite
involved, notably involving iterative estimation of additional, complex
nuisance parameters. The `drtmle`

package was motivated by the need for a
user-friendly tool to implement these methods. The package implements the TMLE
estimators of population-level effects proposed in van der Laan (2014) and
Benkeser et al. (2017), as well as several extensions including cross-validated
targeted minimum loss-based estimators (CVTMLE). Additionally, `drtmle`

implements an IPTW estimator that overcomes shortcomings of existing IPTW
implementations with respect to statistical inference. In particular, the
estimator allows an adaptive estimator of the PS to be used in construction of
the IPTW estimator, while yielding valid statistical inference about
population-level effects. Both the TMLE and IPTW estimators implemented in the
`drtmle`

package are capable of estimating population-level effects for
discrete-valued treatments and the package provides convenient utilities for
constructing confidence intervals and hypothesis tests about contrasts of the
mean outcome under different levels of treatment. This document explains some
of the key concepts underlying doubly-robust inference and illustrates usage
of the `drtmle`

package.

Suppose the observed data consist of $n$ independent and identically
distributed copies of the random variable $O = (W,A,Y) \sim P_0$. Here, $W$ is
a vector of baseline covariates, $A\in{0,1}$ is a binary (for the sake of
simplicity) treatment, $Y$ is an outcome, and $P_0$ is the true data-generating
distribution. The parameters of interest are \begin{equation}
\psi_0(a) = E_0{\bar{Q}(a,W)} = \int \bar{Q}*0(a,w) dQ*{W,0}(w) \ , \
\mbox{for} \ a = 0,1 \ ,
\label{parameterDef}
\end{equation}
where $\bar{Q}_0(a,w)=E_0 \left(Y \mid A=a, W=w\right)$ is the so-called
outcome regression and $Q_W(w)=Pr_0\left(W\leq w\right)$ is the distribution
function of the covariates. The value $\psi_0(a)$ represents the marginal
(i.e., population-level)treatment-specific average outcome, hereafter referred
to as the *marginal mean* under treatment $A=a$. The marginal effect of the
treatment on the outcome can be assessed by comparing marginal means under
different treatment levels. For example, the average treatment effect is often
defined as $\psi_0 := \psi_0(1) - \psi_0(0)$. Under additional assumptions,
these parameters have richer interpretations as mean counterfactual outcomes
under the different treatments and contrasts between these means define causal
effects.

An estimator of $\psi_0(a)$ may be motivated directly by (1). Suppose we have
available an estimate of the OR, $\bar{Q}*n$, and an estimate of the
distribution function of baseline covariates, $Q*{W,n}$. An estimator of
$\psi_0(a)$ may be obtained by plugging these into (1), $\psi_n(a) = \int
\bar{Q}*0(a,w) dQ*{W,n}(w).$ Often the empirical distribution function of
covariates is used so that this estimator can be equivalently written as
\begin{equation*}
\psi_{n,GCOMP}(a) = \frac{1}{n}\sum\limits_{i=1}^n \bar{Q}_n(a, W_i) \ .
\end{equation*}
This estimator is commonly referred to as the G-computation (GCOMP) estimator.
Inference for GCOMP estimators based on parametric OR estimators may be
facilitated through the nonparametric bootstrap. However, if adaptive
approaches are used to estimate the OR, inference may not be available without
further modification to the estimator.

An alternative estimator of the treatment-specific population-level mean may be
obtained by noting that (1) can be equivalently written as
\begin{align}
E_0{\bar{Q}(a,W)} &= E_0{E_0(Y \mid A = a, W)} \notag \
&= E_0\biggl{\frac{I(A=a)}{Pr_0(A = a \mid W)} E_0(Y \mid A = a,
W)\biggr} \notag \
&= E_0\biggl{\frac{I(A=a)}{Pr_0(A = a \mid W)} E_0(Y \mid A,
W)\biggr} \notag \
&= E_0\biggl{\frac{I(A=a)}{Pr_0(A = a \mid W)} Y \biggr} \ .
\label{iptwID}
\end{align}
We use $g_0(a \mid W) := Pr_0(A = a \mid W)$ to denote the propensity score.
Suppose we had available $g_n$, an estimate of the PS. Equation (2) motivates
the estimator \begin{equation*}
\psi_{n,IPTW}(a) := \frac{1}{n}\sum\limits_{i=1}^n \frac{I(A_i = a)}{g_n(a \mid
W_i)} Y_i \ ,
\end{equation*}
which is referred to as the inverse probability of treatment (IPTW) estimator.
Inference for IPTW estimators based on parametric PS estimators may be
facilitated through the nonparametric bootstrap. If adaptive approaches are
used to estimate the PS, inference may not be available without further
modification of the propensity score estimator, as we discuss below.

The GCOMP and IPTW estimators suffer from two important shortcomings. The first is a lack of robustness. That is, validity of GCOMP estimators relies on consistent estimation of the OR, while validity of the IPTW relies on consistent estimation of the PS. In practice, we may not know which of the OR or PS is simpler to consistently estimate and thus may not know which of the two estimators to prefer. Furthermore, if adaptive estimators (e.g., based on machine learning) are used to estimate the OR or PS, then the GCOMP and IPTW estimators lack a basis for statistical inference.

This motivates a new class of estimators that combine both the GCOMP and IPTW
estimators. One example is the augmented IPTW (AIPTW) estimator
\begin{equation*}
\psi_{n,AIPTW}(a) = \psi_{n,IPTW}(a) + \frac{1}{n} \sum\limits_{i=1}^n \biggl{
1 - \frac{I(A_i = a)}{g_n(a \mid W_i)} \biggr} \ \bar{Q}_n(a, W_i) \ ,
\end{equation*}

which adds an augmentation term to the IPTW estimator. Equivalently, the
estimator can be viewed as adding an augmentation to the GCOMP estimator
\begin{equation} \label{oneStepEst}
\psi_{n,AIPTW}(a) = \psi_{n,GCOMP}(a) + \frac{1}{n} \sum\limits_{i=1}^n
\frac{I(A_i = a)}{g_n(a \mid W_i)} {Y_i - \bar{Q}_n(a,W_i)} \ .
\end{equation}
The AIPTW estimator overcomes the two limitations of the GCOMP and IPTW
estimators. The estimator exhibits double-robustness in that it is consistent
for $\psi_0(a)$ if either $\bar{Q}_n$ is consistent for the true OR *or* $g_n$
is consistent for the true PS. Furthermore, if both the OR and PS are
consistently estimated and regularity conditions hold, one can establish a
limiting normal distribution for the AIPTW estimator. Simple estimators of the
variance of this limiting distribution can be derived that may be used to
construct Wald-style confidence intervals and hypothesis tests.

A more recent proposal to improve robustness and inferential properties of the
GCOMP and IPTW estimators utilizes targeted minimum loss-based estimation
(TMLE). TMLE is a general methodology that may be used to modify an estimator
of a regression function in such a way so as to ensure that the modified
estimator satisfies user-selected equations. In the present problem, one can
show that if an estimator of the OR $\bar{Q}_n^*$ satisfies \begin{equation}
\label{meanEIFZero}
\frac{1}{n} \sum\limits_{i=1}^n \frac{I(A_i = a)}{g_n(a \mid W_i)} {Y_i -
\bar{Q}_n^*(a, W_i)} = 0 \ ,
\end{equation}
then the GCOMP estimator based on $\bar{Q}_n^*$ will have the same asymptotic
properties as the AIPTW estimator. That is, the estimator will be doubly-robust
and, provided both the OR and PS are consistently estimated and regularity
conditions are satisfied, will have a normal limiting distribution. TMLE
provides a means of mapping an initial estimator of the OR into an estimator
that satisfies (4). In addition to possessing the desirable asymptotic
properties, TMLE estimators have been shown to outperform AIPTW estimators in
finite samples [@porter:2011:ijb]. We refer readers to van der Laan and Rose
(2011) for more about TMLE [@tlbook:2011]

van der Laan (2014) demonstrated that the double robustness property of AIPTW and TMLE estimators does not extend to their normal limiting distribution if adaptive estimators of the OR and PS are used to construct the estimators. However, in settings with high-dimensional and continuous-valued covariates, adaptive estimation is likely necessary in order to obtain a consistent estimate of either the OR or PS, and thus may be necessary to obtain minimally biased estimates of the marginal means of interest. van der Laan (2014) derived modified TMLE estimators that are doubly-robust not only with respect to consistency, but also with respect to asymptotic normality. Furthermore, he provided closed-form, doubly-robust variance estimators that may be used to construct doubly-robust confidence intervals and hypothesis tests. Benkeser et al. (2017) proposed simplifications of the van der Laan (2014) estimators and demonstrated the practical performance of estimators via simulation.

The key to the theory underlying these results is finding estimators of the OR
and PS that simultaneously satisfy equation (4) in addition to two additional
equations. These equations involve additional regression parameters that we
refer to as the reduced outcome regression (R-OR) and the reduced propensity
score (R-PS), defined respectively as \begin{align*}
\bar{Q} {r,0n}(a,w) &:= E{0}\bigl{Y - \bar{Q}n(W) \mid A = a,
g_n(W)=g_n(w)\bigr} \ , \ \mbox{and} \
g{r,0n}(a \mid w) &:= Pr_0\left{A = a \mid \bar{Q}_n(W)=\bar{Q}_n(w),
g_n(W)=g_n(w)\right} \ .
\end{align*}
In words, the R-OR is the regression of the residual from the outcome
regression onto the estimated PS amongst observations with $A = a$, while the
R-PS is the propensity for treatment $A = a$ given the estimated OR and PS. We
note that a key feature of these reduced-dimension regressions is that they are
low-dimensional regardless of the dimension of $W$ and they do not depend on
the true OR nor PS. Thus, these regressions may be consistently estimated at
reasonably fast rates irrespective of the dimension of $W$ and irrespective of
whether the OR or PS are consistently estimated. Based on these
reduced-dimension regressions, van der Laan (2014) showed that if estimates of
the OR $\bar{Q}

Benkeser et al. (2017) demonstrated alternative equations that can be satisfied
to achieve this form of double-robustness. In particular, they formulated an
alternative version of the R-PS, \begin{align*}
g_{r,0,1}(a \mid w) := Pr_0{A = a \mid \bar{Q}_n(W) = \bar{Q}_n(w) } \ ,
\end{align*}

which we refer to as R-PS1. They also introduced an additional regression of a
weighted residual from the PS on the OR, \begin{align*}
g_{r,0,2}(a \mid w) := E_0\biggl{\frac{I(A = a) - g_n(a \mid W)}{g_n(a \mid
W)} \ \bigg\vert \ \bar{Q}_n(W) = \bar{Q}_n(w) \biggr} \ ,
\end{align*}

which we refer to as R-PS2. The key feature of R-PS1 and R-PS2 is that they are
both univariate regressions, while R-PS is a bivariate regression. Benkeser and
colleagues suggested that this may make R-PS1 and R-PS2 easier to estimate
consistently than R-PS. Further, they showed that if estimators or the OR, PS,
and R-OR satisfy equations (5)-(6), and if additionally, estimators of the OR,
R-PS-1 $g_{r,n,1}^*$, and R-PS-2 $g_{r,n,2}^*$ satisfy $$
\frac{1}{n} \sum\limits_{i=1}^n \frac{I(A_i = a)}{g_{r,n,1}^*(a \mid W_i)}
g_{r,n,2}^*(a \mid W_i) \ {Y_i - \bar{Q}_n^*(a,W_i)} = 0 \ ,
$$
then the GCOMP estimator based on this $\bar{Q}_n^*$ also enjoys a
doubly-robust limiting distribution. Benkeser et al. (2017) provided a TMLE
algorithm that produced such estimates and provided closed-form, doubly-robust
variance estimates.

\textbf{Remark: } In the previous section, we introduced AIPTW and TMLE as two methods for constructing doubly-robust (with respect to consistency) estimators of our parameter of interest. Theoretically, these two frameworks are closely related and practically the two estimators are generally seen as competitors for estimating marginal means. In equation (3), we were able to generate a doubly-robust estimator by adding a term to the GCOMP estimator. This term is exactly the left-hand-side of the key equation (4), the key equation that is solved by the standard TMLE. This is no coincidence; this term is crucial in studying the asymptotic properties of the GCOMP, AIPTW, and TMLE estimators (for discussion, see Benkeser et al. (2017) Section 2.2). Recall that a TMLE estimator achieves doubly-robust consistency by satisfying equation (4), while the AIPTW achieves doubly-robust consistency by combining the GCOMP estimator with the left-hand-side of this term. Now, we have argued that a TMLE estimator may achieve doubly-robust limiting behavior if it additionally satisfies equations (6) and (7). It is thus sensible to wonder whether an AIPTW-style estimator could be constructed by combining the left-hand-side of these two additional equations with the AIPTW estimator. Benkeser and colleagues showed that, surprisingly, this AIPTW-style estimator does not achieve the same doubly-robust properties as the TMLE estimator. Thus, while AIPTW and TMLE are generally competitors for producing estimators doubly-robust with respect to consistency, for doubly-robust inference, TMLE is preferred.

In recent work, we have proposed
estimators of the average treatment effect to be used in settings where this
effect is weakly identifiable, as evidenced by small estimated propensity scores.
In these settings doubly robust estimators may exhibit non-robust behavior
in small samples. The proposed solution was to target an alternative quantity
in the propensity estimation step. If we are interested in estimating $\psi_0(a)$
for $a = 0, 1$, we can estimate $Pr_0(A = a \mid \bar{Q}_0(1, W), \bar{Q}_0(0, W))$,
which describes the conditional probability of receiving treatment $A = a$ given
the value of the two outcome regressions. For this reason, we call this quantity
an *outcome-adaptive propensity score*. We have shown that all of the usual
asymptotics for TMLE carry through when an estimator of this quantity is
substituted for the true propensity score. The resultant TMLE is *super-efficient*
meaning that it will generally have greater asymptotic efficiency than a standard
TMLE. Simulations indicate that this approach also delivers more stable behavior
in settings where estimated propensities become very small. However, the estimator
is not doubly robust -- the asymptotics rely on the outcome regression being
consistently estimated at $n^{-1/4}$ rate. In this sense, this estimator trades off
robustness in the name of achieving greater stability and efficiency. See our
paper for further discussion.

The main function of the package is the eponymous `drtmle`

function. This
function estimates the treatment-specific marginal mean for user-specified
levels of a discrete-valued treatment and computes a doubly-robust covariance
matrix for these estimates. The `drtmle`

function implements either the
estimators of van der Laan (2014) (if `reduction = "bivariate"`

) or the
improved estimators of Benkeser et al. (2017) (`reduction = "univariate"`

). The
package includes utility functions for combining these estimates into estimates
of average treatment effects, as well as other contrasts, as discussed below.

The main data arguments for `drtmle`

are `W, A, Y`

, which, as above, represent
the covariates, a discrete-valued treatment, and an outcome, respectively.
Missing data are allowed in `A`

and `Y`

; this is discussed further in a section
below. Beyond the data arguments, it is necessary to specify how to estimate
each of the OR, PS, R-OR, and R-PS (or R-PS1, R-PS2). The package provides
flexibility in how these regression parameters may be estimated. Once the user
has specified the data arguments and how to estimate each regression parameter,
the function proceeds as follows:
1. estimate the OR via the internal `estimateQ`

function;
2. estimate the PS via the internal `estimateG`

function;
3. estimate reduced-dimension regression via `estimateQrn`

and
`estimategrn`

functions;
4. iteratively modify initial estimates of the OR and PS via the `fluctuateQ`

and `fluctuateG`

functions until equations (5)-(7) are approximately
solved;
5. compute the TMLE estimates and covariance estimates based on the final OR.
We refer interested readers to Benkeser et al. (2017) for a theoretical
derivation of how the iterative modification of initial OR and PS is performed.

In addition to returning doubly-robust parameter and covariance estimates, the
`drtmle`

function returns estimates of the GCOMP, AIPTW, standard TMLE, and the
modified AIPTW estimator discussed in the remark above. While doubly-robust
inference based on these estimators is not available, the estimators are
provided for comparison. In the following subsections, we look at various
options for making calls to `drtmle`

.

library(drtmle) set.seed(1234) N <- 1e6 W <- data.frame(W1 = runif(N), W2 = rbinom(N, 1, 0.5)) Y1 <- rbinom(N, 1, plogis(W$W1 + W$W2)) Y0 <- rbinom(N, 1, plogis(W$W1)) EY1 <- mean(Y1) EY0 <- mean(Y0)

While the estimators computed by `drtmle`

are consistent and asymptotically
normal under inconsistent estimation of either the OR or PS, it is nevertheless
advisable to strive for consistent estimation of *both* the OR and PS. If both
of the regression parameters are consistently estimated then the estimators
computed by `drtmle`

achieve the nonparametric efficiency bound, resulting in
narrower confidence intervals and more powerful hypothesis tests. In order that
the estimates of the OR and PS have the greatest chance of consistency, the
`drtmle`

function facilitates estimation using adaptive estimation techniques.
In fact, there are three ways to estimate each of the regression parameters:
generalized linear models, super learning, and a user-specified estimation
technique. We demonstrate each of these approaches in turn on a simple
simulated data set. The true parameter values of interest are $\psi_0(0)=$ ```
r
round(EY0, 2)
```

and $\psi_0(1)=$ `r round(EY1, 2)`

.

set.seed(1234) n <- 200 W <- data.frame(W1 = runif(n), W2 = rbinom(n, 1, 0.5)) A <- rbinom(n, 1, plogis(W$W1 + W$W2)) Y <- rbinom(n, 1, plogis(W$W1 + W$W2*A))

`glm`

to estimate regressionsGeneralized linear models are perhaps the most popular means of estimating
regression functions. Because of this legacy, these estimators have been
included as an option for estimating the regression parameters. The user
specifies the right-hand-side of a regression formula as a character in the
`glm_Q`

, `glm_g`

, `glm_Qr`

, and `glm_gr`

options to estimate the OR, PS, R-OR,
and R-PS. These regressions are fit by calling the `glm`

function in base R.
Thus, the model specification should be able to be parsed as a valid formula,
as required by `glm`

. Recall that the R-OR parameter involves a regression onto
the estimated propensity. Thus, the formula that is input via `glm_Qr`

should
assume data with a single column named `"gn"`

. Similarly, the R-PS1 and R-PS2
that are computed for the estimators of Benkeser et al. (2017) (the default
estimators, computed whenever `reduction = "univariate"`

) involve regressions
onto the estimated outcome regression. Thus, the formula `glm_gr`

should assume
data with a single column named `"Qn"`

if `reduction = "univariate"`

. If
instead, the estimators of van der Laan (2014) are preferred, we can set the
option `reduction = "bivariate"`

and input a `glm_gr`

formula that assumes data
with two columns named `"Qn"`

and `"gn"`

.

The code below illustrates how generalized linear models can be used to
estimate the regression parameters. In this call to `drtmle`

, we specify
`family = binomial()`

so that logistic regression is used to estimate the OR.
We specify `stratify = FALSE`

to indicate that we wish to fit a single OR to
estimate $\bar{Q}_0(A,W)$, as opposed to fitting two separate regressions to
estimate $\bar{Q}_0(1,W)$ and $\bar{Q}_0(0,W)$. If the former fitting is
specified, then the OR regression is fit using a data frame that contains `W`

and `A`

. Thus, both `colnames(W)`

and `"A"`

may appear in `glm_Q`

. However, if
`stratify = TRUE`

then the OR model is fit separately in observations with $A =
1$ and $A = 0$. In this case, the `glm_Q`

option may not include `"A"`

. We
illustrate non-stratified regression for both the univariate and bivariate
reductions.

glm_fit_uni <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_g = "W1 + W2", glm_Q = "W1 + W2*A", glm_gr = "Qn", glm_Qr = "gn", stratify = FALSE) glm_fit_uni glm_fit_biv <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_g = "W1 + W2", glm_Q = "W1 + W2*A", glm_gr = "Qn", glm_Qr = "gn", stratify = FALSE, reduction = "bivariate") glm_fit_biv

`SuperLearner`

to estimate regressions```
library(SuperLearner)
```

Super learning is a loss-based ensemble machine learning technique
[@vdl:2007:statapp] that is a generalization of stacking and stacked regression
[@wolpert:1992:nnet]breiman:1996:ml}. The user specifies many potential
candidate regression estimators, referred to as a *library* of candidate
estimators, and the super learner algorithm estimates the convex combination of
regression estimators that minimizes $V$-fold cross-validated risk based on a
user-selected loss function. Oracle inequalities show that the super learner
estimate performs essentially as well as the unknown best convex combination of
the candidate estimators [@vdvaart:2006:statdec]. Thus, the methodology may be
seen as an asymptotically optimal way of performing estimator selection.
Practically, the super learner provides the best chance for obtaining a
consistent estimator for both the OR and PS, and thereby an efficient estimator
of the marginal means of interest.

Super learning is implemented in the R package `SuperLearner`

[@SuperLearnerPackage] and `drtmle`

provides the option to utilize super
learning for estimation of the OR, PS, and reduced-dimension regressions via
the `SL_Q`

, `SL_g`

, `SL_Qr`

, and `SL_gr`

options. When calling `drtmle`

either
the generalized linear model options for regression modeling or the super
learner options should be invoked. If both are called, the function will
default to the super learner option. The inputs to the super learner options
are passed to the `SL.library`

argument of the `SuperLearner`

function of the
`SuperLearner`

package. We refer readers to the `SuperLearner`

help file for
information on how to properly specify a super learner library. In the code
below, we illustrate a call to `drtmle`

with a relatively simple library. For
the OR and PS, the library includes a main-terms generalized linear model (via
the function `SL.glm`

from `SuperLearner`

) and an unadjusted
generalized linear model (`SL.mean`

). For the reduced dimension regressions we
use a super learner library with a main terms generalized linear model
(`SL.glm`

) and regression splines (`SL.gam`

, which utilizes the `gam`

package [@gamPackage]).

set.seed(1234) sl_fit <- drtmle(W = W, A = A, Y = Y, family = binomial(), SL_g = c("SL.glm","SL.mean"), SL_Q = c("SL.glm","SL.mean"), SL_gr = c("SL.glm", "SL.gam"), SL_Qr = c("SL.glm", "SL.gam"), stratify = FALSE) sl_fit

The `drtmle`

package also allows users to pass in their own functions for
estimating regression parameters via the `SL_`

options. These functions must be
formatted properly for compatibility with `drtmle`

. The necessary formatting is
identical the formatting required for writing a `SuperLearner`

wrapper (see
`SuperLearner::write.SL.template()`

). The `drtmle`

package includes `SL.npreg`

,
which is an example of how a user can specify a function for estimating
regression parameters. This functions fits a kernel regression estimator using
the `np`

package [@npPackage]. Below we show the source code for this function.

SL.npreg

In the above code, we see that a user-specified function should have options
`Y, X, newX, family, obsWeights, ...`

, along with other options specific to the
function. The option `Y`

corresponds to the outcome of the regression and `X`

to the variables onto which the outcome is regressed. The option `newX`

should
be of the same class and dimension as `X`

and is meant to contain new values of
variables at which to evaluate the regression fit. The `family`

and
`obsWeights`

options are not strictly necessary (indeed, they are not used by
`SL.npreg`

), they are useful to include if one wishes to utilize the function
as part of a super learner library. The `SL.npreg`

function proceeds by
checking whether there is sufficient variation in `Y`

to even both fitting the
kernel regression (as specified by option `rangeThresh`

), and if so, fits a
kernel regression with cross-validated bandwidth selection via the `npregbw`

function. The output is then formatted in a specific way so that `drtmle`

is
able to retrieve the appropriate objects from the fitted regression. In
particular the output should be a list with named objects `pred`

and `fit`

; the
former including predictions made from the fitted regression on `newX`

, the
latter including any information that may be needed to predict new values based
on the fitted object. A class is assigned to the `fit`

object, so that an S3
`predict`

method may later be used to predict new values based on the fitted
regression object. The user must also specify this `predict`

method. Use
`drtmle:::predict.SL.npreg`

to view the simple `predict`

method for `SL.npreg`

.

In the code below, we demonstrate a call to `drtmle`

with `SL.npreg`

used to
estimate each nuisance parameter. Here, we also illustrate the use of ```
stratify
= TRUE
```

, which fits a separate kernel regression of $Y$ onto $W$ in
observations with $A = 1$ and $A = 0$.

npreg_fit <- drtmle(W = W, A = A, Y = Y, family = binomial(), SL_g = "SL.npreg", SL_Q = "SL.npreg", SL_gr = "SL.npreg", SL_Qr = "SL.npreg", stratify = TRUE) npreg_fit

Some users may find it unsettling that when applying the super learner in real data analysis is that the results are dependent on the random seed that is set. To remove this dependence, one can instead average results over repeated super learner runs; more repeats will lead to less dependence on the random seed. There are generally two approaches that can be used: (i) averaging on the scale of the nuisance parameters and (ii) averaging on the scale of the point estimates.

For (i), suppose that we have $n_{SL}$ estimated super
learners for the OR, $\bar{Q}*{n,j}, j = 1, \dots, n*{SL}$, and the PS
$g_{n,j}, j = 1, \dots, n_{SL}$. We can take the average over these fits, [
\bar{Q}*n = \frac{1}{n*{SL}} \sum_{j = 1}^{n_{SL}} \bar{Q}_{n,j}
]
as our estimate of the OR (similarly for the PS). These averaged estimates are then used to
compute the AIPTW or TMLE, as described above.

For(ii), suppose for example that we have $n_{SL}$ TMLE (similarly, AIPTW) estimates $\psi_{n,j}(1)$ of $\psi_0(1)$, each obtained based on one (or more) super learner of the OR and/or PS. We can take the average over these point estimates, [ \psi_n(1) = \frac{1}{n_{SL}} \sum_{j=1}^{n_{SL}} \psi_{n,j}(1) ] as our estimate of $\psi_{0}(1)$.

These feature is implemented via the `n_SL`

and `avg_over`

options, where the former specifies the
number of super learners to repeat and the latter specifies the scale(s) to average over. For example, if
`avg_over = "drtmle"`

and `n_SL = 2`

, then two point estimates are computed, each based on a single
super learner, and averaged to obtain a final estimate. If instead `avg_over = "SL"`

and `n_SL = 2`

,
then two super learners are fit and averaged before a final point estimate is obtained. Finally, if
`avg_over = c("drtmle", "SL")`

and `n_SL = 2`

then averaging on *both* scales is performed. That is,
the final estimate is the average of two point estimates, each obtained using OR and PS estimates that
are averaged over two super learners.

Not surprisingly, averaging over multiple super learners can add considerable computational expense
to calls to `drtmle`

. The package is currently parallelized at the level of individual calls to
`SuperLearner`

. With parallelization over enough cores, repeated super
learning should not add too much to the computational burden relative to a single
call to `SuperLearner`

.

If the outcome-adaptive PS is desired, the user may specify this via
the `adapt_g`

option. In this case, the additional targeting steps needed for doubly
robust inference are "turned off" (i.e., `guard = NULL`

), since, as discussed above,
this estimator is *not* doubly robust. Note that when the `adapt-g = TRUE`

the data
used for the PS estimation will be a `data.frame`

with column names
`QaW`

where `a`

takes all the values specified in `a_0`

. For example if `a_0 = c(0,1)`

,
then the PS estimation data frame will have two columns called `Q0W`

and `Q1W`

. This may be relevant for users writing their own `SuperLearner`

wrappers
or those who make use of the `glm_g`

setting, as illustrated below.

# outcome adaptive propensity score fit using glms adaptg_fit <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_Q = ".^2", glm_g = "Q1W + Q0W", adapt_g = TRUE) # outcome adaptive propensity score fit using super learner adaptg_sl_fit <- drtmle(W = W, A = A, Y = Y, family = binomial(), SL_Q = c("SL.glm", "SL.mean"), SL_g = c("SL.glm", "SL.mean"), adapt_g = TRUE)

The `drtmle`

package also allows users to pass in their own estimated OR and PS
via the `Qn`

and `gn`

options, respectively.
If `Qn`

is specified by the user, `drtmle`

will ignore the nuisance parameter
estimation routines specified by `SL_Q`

and `glm_Q`

-- similarly for `gn`

and
`SL_g`

, `glm_g`

. As noted in the package documentation, there is a specific format
necessary for inputting `Qn`

and `gn`

. For `Qn`

, the entries in the list should
correspond to the OR evaluated at A and the observed values of `W`

,
with order determined by the input to `a_0`

. For example, if `a_0 = c(0, 1)`

then
`Qn[[1]]`

should be OR at $A = 0$ and `Qn[[2]]`

should be outcome
regression at $A = 1$. The example below illustrates this concept.

# fit a GLM for outcome regression outside of drtmle out_glm_fit <- glm(Y ~ . , data = data.frame(A = A, W), family = binomial()) # generate Qn list Qn10 <- list( # first entry is predicted values setting A = 1 predict(out_glm_fit, newdata = data.frame(A = 1, W), type = "response"), # second entry is predicted values setting A = 0 predict(out_glm_fit, newdata = data.frame(A = 0, W), type = "response") ) # pass this list to drtmle to avoid internal estimation of # outcome regression (note propensity score and reduced-dimension # regressions are still estimated internally) out_fit1 <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_g = ".", glm_Qr = "gn", glm_gr = "Qn", Qn = Qn10, # crucial to set a_0 to match Qn's construction! a_0 = c(1,0)) out_fit1 # to be completely thorough let's re-order Qn10 and re-run Qn01 <- list(Qn10[[2]], Qn10[[1]]) out_fit2 <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_g = ".", glm_Qr = "gn", glm_gr = "Qn", # use re-ordered Qn list Qn = Qn01, # a_0 has to be reordered to match Qn01! a_0 = c(0,1)) # should be the same as out_fit1, but re-ordered out_fit2

Similarly, for `gn`

we need to pass in properly ordered lists. For example,
if `a_0 = c(0, 1)`

then `gn[[1]]`

should be propensity for receiving $A = 0$
and `gn[[2]]`

should be the propensity for receiving $A = 1$. The example below
illustrates this concept.

# fit a GLM for propensity score outside of drtmle out_glm_fit <- glm(A ~ . , data = data.frame(W), family = binomial()) # get P(A = 1 | W) by calling predict gn1W <- predict(out_glm_fit, newdata = data.frame(W), type = "response") # generate gn list gn10 <- list( # first entry is P(A = 1 | W) gn1W, # second entry is P(A = 0 | W) = 1 - P(A = 1 | W) 1 - gn1W ) # pass this list to drtmle to avoid internal estimation of # propensity score (note reduced-dimension regressions are # still estimated internally) out_fit3 <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_Qr = "gn", glm_gr = "Qn", Qn = Qn10, gn = gn10, # crucial to set a_0 to match Qn and gn's construction! a_0 = c(1,0)) out_fit3 # to be completely thorough let's re-order gn10 and re-run gn01 <- list(gn10[[2]], gn10[[1]]) out_fit4 <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_Qr = "gn", glm_gr = "Qn", # use re-ordered Qn/gn list Qn = Qn01, gn = gn01, # a_0 has to be reordered to match Qn01! a_0 = c(0,1)) # should be the same as out_fit3, but re-ordered out_fit4

This flexibility allows users to use any regression technique to externally
obtain initial estimates of nuisance parameters. The key step is making sure
that the ordering of `Qn`

and `gn`

is consistent with `a_0`

.

This section contains some more advanced mathematical material and may be omitted by users interested only in implementation. For simplicity, we focus this discussion on the standard TMLE/AIPTW estimators. The ideas generalize immediately to the DRTMLE estimator, but the formulas are more complex.

Reported standard error estimates are based on an estimate the asymptotic variance of
the estimators. This variance can be characterized by the estimators influence function.
For example, let $\psi_{n}(1)$ be an AIPTW estimate of $\psi_0(1)$. Under regularity
conditions, [
\psi_{n}(1) - \psi_0(1) = \frac{1}{n} \sum_{i=1}^n D^*(\bar{Q} 0, g_0, Q{W,0})(O_i) + o_{p}(n^{-1/2}) \ ,
]
where [
D^*(\bar{Q}

For TMLE-based estimators, it is standard practice to construct $D_i$ above using the *targeted*
version of the OR (denoted by $\bar{Q}_n^*$ above). Recall that such estimates are obtained by modifying
an initial OR estimate in such a way as to ensure a particular (set of) equation(s) is (are) solved.
However, simulations have shown that better standard error estimates for TMLE may be achieved by instead
using the *initial* OR estimate in the computation of standard errors, as described in the previous
subsection. The `drtmle`

function includes the `targeted_se`

option to this end.

glm_fit_uni_nontargeted_se <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_g = "W1 + W2", glm_Q = "W1 + W2*A", glm_gr = "Qn", glm_Qr = "gn", stratify = FALSE, targeted_se = FALSE) # compare to original call list( glm_fit_uni$drtmle$cov, # based on targeted OR glm_fit_uni_nontargeted_se$drtmle$cov # untargeted OR )

In simulations, we often find that when employing machine learning to estimate the OR and/or PS,
the estimated standard errors are *too small* in small to moderate-sized samples, leading to
under-coverage of confidence intervals and possibly too-large type I errors of hypothesis tests that
are based on these standard error estimates. This should not be too surprising when studying the form
of the standard error estimates. In particular, they will include a term that looks like
a (weighted) mean squared-error (MSE) of the OR. Because machine learning algorithms often set out
to minimize empirical MSE as part of their training, we often find that the empirical MSE is biased
too small relative to the true MSE. Thus, in order to more accurately estimate MSE of a machine learning
algorithm, we typically rely on cross-validation. The same trick can be used here to obtain a more
conservative standard error estimate.

Suppose we use $V$-fold cross-validation to obtained $V$ training-sample-specific estimates of the OR,
$\bar{Q}*{n,v}, v = 1, \dots, V$ and the PS, $g*{n,v}, v = 1, \dots, V$. For $i = 1, \dots, n$, let $\bar{Q}*{n,i}^{cv}(1)$ denote the estimate of $\bar{Q}*{0}(a, W_i)$ obtained when observation $i$ was in the validation fold. That is, $\bar{Q}*{n,i}^{cv}$ is an out-of-sample prediction of $Y_i$. We define
$g*{n,i}^{cv}$ similarly. Using these estimates, we can define [
D_i^{cv} = \frac{I(A_i = 1)}{g_{n,i}^{cv}} { Y_i - \bar{Q}*{n,i}^{cv}(1)} + \bar{Q}*{n,i}^{cv}(1) - \frac{1}{n} \sum_{i=1}^n \bar{Q}*{n,i}^{cv}(1)
]
and compute an estimate of $\sigma^2_0$ using [
\sigma^2*{n,cv} = \frac{1}{n} \sum_{i=1}^n \left{ D_i^{cv} - \frac{1}{n} \sum_{j=1}^n D_j^{cv} \right}^2 \ .
]

Cross-validated standard errors can be obtained by setting `se_cv = "full"`

and `se_cvFolds`

to a number greater than 1.

glm_fit_uni_cv <- drtmle(W = W, A = A, Y = Y, family = binomial(), glm_g = "W1 + W2", glm_Q = "W1 + W2*A", glm_gr = "Qn", glm_Qr = "gn", se_cv = "full", se_cvFolds = 2, stratify = FALSE, targeted_se = FALSE) # needed for this to work # compare variance to previously obtained list(glm_fit_uni$drtmle$cov, glm_fit_uni_cv$drtmle$cov)

Obtaining fully cross-validated standard errors can be fairly time intensive when super learner is
used to estimate the OR and PS. In `drtmle`

we give an option to compute standard error estimates that
are *partially cross-validated* and do not require any additional fitting. Here we describe briefly
how these estimates are obtained and show calls to `drtmle`

to obtain these estimates.

Suppose we have $K$ candidate regressions in our super learner library for both the OR and PS
(in practice, the number could be different for the OR as for the PS). Recall that a super learner
estimate of for example $\bar{Q}*0(a, w)$ is obtained as a weighted combination of estimates
from each candidate regression. Let $\bar{Q}*{n,k}$ denote the estimate of $\bar{Q}*{0}$ obtained
by training algorithm $k$ using the full data set. The super learner estimate of $\bar{Q}*{0}(a,w)$ is [
\bar{Q}*{n,SL}(a, w) = \sum*{k = 1}^K \alpha_{n,k} \bar{Q}*{n,k}(a, w) \ ,
]
where $\alpha_n = (\alpha*{n,1}, \dots, \alpha_{n,K})$ is a weight vector that is selected by minimizing
a cross-validated risk criteria. That is, each of the $K$ algorithms is trained according to a $V$-fold
cross-validation scheme, resulting in $V + 1$ fits of each algorithm (one in each of $V$ training folds
plus one obtained by fitting the algorithm using the full data). Let $\bar{Q}*{n,k,v}$ denote the $k$-th
algorithm trained using the $v$-th training fold. For $i = 1,\dots,n$, let $\bar{Q}*{n,k,i}^{cv}(1)$
denote the estimate of $\bar{Q}*0(1, W_i)$ implied by the fitted algorithm $\bar{Q}*{n,k,v}$, which
was obtained when observation $i$ was in the validation sample. That is, for observations with
$A_i = 1$, $\bar{Q}*{n,k,i}^{cv}(1)$ is an out of sample prediction of $Y_i$ based on the
$k$-th algorithm. Now, we can construct a partially cross-validated super learner prediction as follows. For $i = 1, \dots, n$ let [
\bar{Q}*{n,SL,i}^{cv}(1) = \sum_{k = 1}^K \alpha_{n,k} \bar{Q}

Note that is not a proper cross-validated estimate, since observation $i$ is used to compute the weight vector $\alpha_n$. However, such estimates can be obtained based on the output of a single round of super learning, thereby providing improved standard error estimates at minimal additional computational cost.

Here, we demonstrate how to obtain such estimates using `drtmle`

.

set.seed(1234) sl_fit_pcv <- drtmle(W = W, A = A, Y = Y, family = binomial(), SL_g = c("SL.glm","SL.mean"), SL_Q = c("SL.glm","SL.mean"), SL_gr = c("SL.glm", "SL.gam"), SL_Qr = c("SL.glm", "SL.gam"), stratify = FALSE, se_cv = "partial") # compare estimates # pt estimates should be same # variance should be larger for pcv list( sl_fit, # no cv sl_fit_pcv # partial cv )

The `drtmle`

package contains methods that compute doubly-robust confidence
intervals. The `ci`

method computes confidence intervals about the estimated
marginal means in a fitted `drtmle`

object, in addition to contrasts between
those means. By default the method gives confidence intervals about each mean.

```
ci(npreg_fit)
```

Alternatively the `contrast`

option allows users to specify a linear
combination of marginal means, which can be used to estimate the average
treatment effect as we show below.

ci(npreg_fit, contrast = c(1,-1))

The `contrast`

option can also be input as a list of functions in order to
compute a confidence interval for a user-specified contrast. For example, we
might be interested in the risk ratio $\psi_0(1) / \psi_0(0)$. When
constructing Wald style confidence intervals for ratios, it is common to
compute the interval on the log scale and back-transform. That is, the
confidence interval is of the form
$$
\mbox{exp}\biggl[\mbox{log}\biggl{ \frac{\psi_0(1)}{\psi_0(0)} \biggr} \pm
z_{1-\alpha/2} \sigma^{\text{log}}_n \biggr] \ ,
$$
where $\sigma^{\text{log}}_n$ is the estimated standard error on the log-scale.
By the delta method, an estimate of this standard error is given by $$
\sigma^{\text{log}}_n = \nabla g(\psi_n)^T \Sigma_n \nabla g(\psi_n),
$$
where $\Sigma_n$ is the doubly-robust covariance matrix estimate output from
`drtmle`

and $\nabla g(\psi)$ is the gradient of
$\mbox{log}{\psi(1)/\psi(0)}$. To obtain this confidence interval, we may use
the following code.

riskRatio <- list(f = function(eff){ log(eff) }, f_inv = function(eff){ exp(eff) }, h = function(est){ est[1]/est[2] }, fh_grad = function(est){ c(1/est[1],-1/est[2]) }) ci(npreg_fit, contrast = riskRatio)

More generally this method of inputting the `contrast`

option to the `ci`

method can be used to compute confidence intervals of the form
$$
f^{-1}\bigl{ f(h(\psi_n)) \pm z_{1-\alpha/2} \nabla f(h(\psi_n))^T \Sigma_n
\nabla f(h(\psi_n)) \bigr} \ ,
$$
where $f$ (`contrast$f`

) is the transformation of the confidence interval,
$f^{-1}$ (`contrast$f_inv`

) is the back-transformation of the interval, $h$
(`contrast$h`

) is the contrast of marginal means, and $\nabla f(h(\psi_n))$
(`contrast$fh_grad`

) defines the gradient of the transformed contrast at the
estimated marginal means.

We note that this method of computing confidence intervals can also be used to obtain a transformed confidence interval for a particular marginal mean. For example, in our running example $Y$ is binary. Thus, we might wish to enforce that our confidence interval be computed on a scale which ensures that the confidence limits are bounded between zero and one. To that end, we can consider an interval of the form construct an interval on the logit scale and back-transform, $$ \mbox{expit}\biggl[ \mbox{log}\biggl{ \frac{\psi_n(1)}{1 - \psi_n(1)} \biggr} \pm z_{1-\alpha/2} \sigma^{\text{logit}}_n \biggr] \ . $$ This confidence interval may be implemented as follows.

logitMean <- list(f = function(eff){ qlogis(eff) }, f_inv = function(eff){ plogis(eff) }, h = function(est){ est[1] }, fh_grad = function(est){ c(1/(est[1] - est[1]^2), 0) }) ci(npreg_fit, contrast = logitMean)

Doubly-robust Wald-style hypothesis tests may be implemented using the
`wald_test`

method. As with confidence intervals, there are three options for
testing. First, we may test the null hypothesis that the marginal means equal a
fixed value (or values). Below, we test the null hypothesis that $\psi_0(1) =
0.5$ and that $\psi_0(0) = 0.6$ by inputting a vector to the `null`

option.

wald_test(npreg_fit, null = c(0.5, 0.6))

As with the `ci`

method, the `wald_test`

method allows users to test linear
combinations of marginal means by inputting a vector of ones and negative ones
in the `contrast`

option. We can use this to test the null hypothesis of no
average treatment effect or to test the null hypothesis that the average
treatment effect equals 0.05 (by specifying the `null`

option)

wald_test(npreg_fit, contrast = c(1, -1)) wald_test(npreg_fit, contrast = c(1, -1), null = 0.05)

The `wald_test`

method also allows for user-specified contrasts to be tested
using syntax similar to the `ci`

method. The only difference syntactically is
that it is not strictly necessary to specify `f_inv`

, as the hypothesis test is
performed on the transformed scale. That is, we can generally test the null
hypothesis that $f(h(\psi_0))$ equals $f_0$ (the function $f$ applied to the
value passed to `null`

) using the Wald statistic $$
Z_n := \frac{{f(h(\psi_n)) - f_0}}{\nabla f(h(\psi_n))^T \Sigma_n \nabla
f(h(\psi_n))} \ .
$$
We can use the `riskRatio`

contrast defined previously to test the null
hypothesis that $\psi_0(1)/\psi_0(0) = 1$.

wald_test(npreg_fit, contrast = riskRatio, null = 1)

The `drtmle`

function supports missing data in `A`

and `Y`

. Such missing data
are common in practice and, if ignored, can bias estimates of the marginal
means of interest. The estimators previously discussed can be modified to allow
this missingness to depend on covariates. Let $\Delta_A$ and $\Delta_Y$ be
indicators that $A$ and $Y$ are observed, respectively. We can redefine the OR
to be $\bar{Q}_n(a,w) := E(\Delta_Y Y \mid \Delta_A A = a, \Delta_A = 1,
\Delta_Y = 1, W = w)$ and similarly redefine the PS to be \begin{align}
g_0(a \mid w) &= Pr_0(\Delta_A = 1, \Delta_A A = a, \Delta_Y = 1 \mid W = w)
\notag \
&= Pr_0(\Delta_A = 1 \mid W) Pr_0(\Delta_A A = a \mid \Delta_A = 1, W = w)
\label{modPS} \
&\hspace{1.5in} Pr_0(\Delta_Y = 1 \mid \Delta_A = 1, \Delta_A A = a, W = w) \ \notag.
\end{align}
We introduce missing values to `A`

and `Y`

in our running example.

DeltaA <- rbinom(n, 1, plogis(2 + W$W1)) DeltaY <- rbinom(n, 1, plogis(2 + W$W2 + A)) A[DeltaA == 0] <- NA Y[DeltaY == 0] <- NA

The only modification of the call to `drtmle`

is in how the PS estimation is
performed. The options `glm_g`

and `SL_g`

are still used to fit generalized
linear models or a super learner, respectively. However, the code allows for
separate regression fits for each of the three components of the PS in (8). To
perform separate generalized linear model fits for each of the three
components, `glm_g`

should be a named list with entries `"DeltaA"`

, `"A"`

, and
`"DeltaY"`

. Respectively these should provide a regression formula for the
regression of $\Delta_A$ on $W$, $A$ on $W$ in observations with $\Delta_A =
1$, and a regression of $\Delta_Y$ on $W$ and $A$ in observations with
$\Delta_A = \Delta_Y = 1$ (if `stratify = FALSE`

) or a regression of $\Delta_Y$
on $W$ to be fit in observations with $\Delta_A = \Delta_Y = 1$ and $A = a$ for
each $a$ specified in option `a_0`

(if `stratify = TRUE`

). If only a single
formula is passed to `glm_g`

, it is used for each of the three regressions. We
illustrate a call to `drtmle`

with missing data and generalized linear model
fits below.

glm_fit_wNAs <- drtmle(W = W, A = A, Y = Y, stratify = FALSE, glm_g = list(DeltaA = "W1 + W2", A = "W1 + W2", DeltaY = "W1 + W2 + A"), glm_Q = "W1 + W2*A", glm_Qr = "gn", glm_gr = "Qn", family = binomial()) glm_fit_wNAs

It is possible to mix generalized linear models and super learning when fitting
each of the components of the PS. In the below example we use the wrapper
function `SL.glm`

, which fits a main terms logistic regression, to fit the
regression of $\Delta_A$ on $W$, use `SL.npreg`

to fit the regression of $A$ on
$W$, and use a super learner with library `SL.glm`

, `SL.mean`

, and `SL.gam`

to
fit the regression of $Delta_Y$ on $W$ and $A$.

mixed_fit_wNAs <- drtmle(W = W, A = A, Y = Y, stratify = FALSE, SL_g = list(DeltaA = "SL.glm", A = "SL.npreg", DeltaY = c("SL.glm", "SL.mean", "SL.gam")), glm_Q = "W1 + W2*A", glm_Qr = "gn", glm_gr = "Qn", family = binomial()) mixed_fit_wNAs

We can also fit the PS regressions externally and pass in via `gn`

, though this
process is slightly more complicated than when no missing data are present.
The basic idea is to fit one regression for (i) the indicator of missing $A$,
(ii) $A$ itself, and (iii) the indicator of missing the outcome. The three PS's
are then multiplied together and an appropriately ordered list is constructed
as before. We illustrate with the following simple example.

# first regress indicator of missing A on W fit_DeltaA <- glm(DeltaA ~ . , data = W, family = binomial()) # get estimated propensity for observing A ps_DeltaA <- predict(fit_DeltaA, type = "response") # now regress A on W | DeltaA = 1 fit_A <- glm(A[DeltaA == 1] ~ . , data = W[DeltaA == 1, , drop = FALSE], family = binomial()) # get estimated propensity for receiving A = 1 ps_A1 <- predict(fit_A, newdata = W, type = "response") # propensity for receiving A = 0 ps_A0 <- 1 - ps_A1 # now regress DeltaY on A + W | DeltaA = 1. Here we are pooling over # values of A (i.e., this is what drtmle does if stratify = FALSE). # We could also fit two regressions, one of DeltaY ~ W | DeltaA = 1, A = 1 # and one of DeltaY ~ W | DeltaA = 1, A = 0 (this is what drtmle does if # stratify = TRUE). fit_DeltaY <- glm(DeltaY[DeltaA == 1] ~ . , data = data.frame(A = A, W)[DeltaA == 1, ], family = binomial()) # get estimated propensity for observing outcome if A = 1 ps_DeltaY_A1 <- predict(fit_DeltaY, newdata = data.frame(A = 1, W), type = "response") # get estimated propensity for observing outcome if A = 0 ps_DeltaY_A0 <- predict(fit_DeltaY, newdata = data.frame(A = 0, W), type = "response") # now combine all results into a single propensity score gn <- list( # propensity for DeltaA = 1, A = 1, DeltaY = 1 ps_DeltaA * ps_A1 * ps_DeltaY_A1, # propensity for DeltaA = 1, A = 0, DeltaY = 1 ps_DeltaA * ps_A0 * ps_DeltaY_A0 ) # pass in this gn to drtmle out_fit_ps <- drtmle(W = W, A = A, Y = Y, stratify = FALSE, # make sure a_0/gn are ordered properly! gn = gn, a_0 = c(1, 0), glm_Q = "W1 + W2*A", glm_Qr = "gn", glm_gr = "Qn", family = binomial()) out_fit_ps

library(drtmle) set.seed(1234) N <- 1e6 W <- data.frame(W1 = runif(N), W2 = rbinom(N, 1, 0.5)) Y2 <- rbinom(N, 1, plogis(W$W1 + 2*W$W2)) Y1 <- rbinom(N, 1, plogis(W$W1 + W$W2)) Y0 <- rbinom(N, 1, plogis(W$W1)) EY1 <- mean(Y1) EY0 <- mean(Y0) EY2 <- mean(Y2)

So far in our examples, we have only considered binary treatments. However,
`drtmle`

is equipped to handle treatments with an arbitrary number of discrete
values. Suppose that $A$ assumes values in a set $\mathcal{A}$, the PS
regression estimation is modified to ensure that
$$
\sum\limits_{a \in \mathcal{A}} g_n(a \mid w) = 1 \ \mbox{for all $w$} \ .
$$
If this condition is true, we say that the estimates of the PS are compatible.
Many regression methodologies that have been developed work well with binary
outcomes, but have not been extended to multi-level outcomes. To ensure that
users of `drtmle`

have access to the large number of binary regression
techniques when estimating the PS, we have taken an alternative approach to
estimation of the propensity for each level of the treatment. Specifically,
rather than fitting a single multinomial-type regression, we fit a series of
binomial regressions. As a concrete example, consider the case of no missing
data and where $\mathcal{A} = {0,1,2}$. We can ensure compatible estimates of
the PS by generating an estimate $g_n(0 \mid w)$ of $Pr_0(A = 0 \mid W = w)$
and $\tilde{g}_n(1 \mid w)$ of $Pr_0(A = 1 \mid A > 0, W = w)$. The latter
estimate may be generated by regression $I(A = 1)$ on $W$ in observations with
$A > 0$. Because the outcome is binary, this regression may be estimated using
any technique suitable for binary outcomes. By simple rules of conditional
probability, we know that $g_0(1 \mid w) = Pr_0(A = 1 \mid A > 0, W = w){1 -
Pr_0(A = 0 \mid W)}$ and thus an estimate of $g_0(1 \mid w)$ can be computed
as $g_n(1 \mid w) = \tilde{g}_n(1 \mid w) {1 - g_n(0 \mid w)}$. Finally, we
let $g_n(2 \mid w) = 1 - g_n(0 \mid w) - g_n(1 \mid w)$, which ensures
compatibility of the estimates for each level of covariates. This approach
generalizes to an arbitrary number of treatment levels. Below, we provide an
illustration using simulated data with a treatment that has three levels. The
true parameter values in this simulation are $\psi_0(0)=$ `r round(EY0,2)`

,
$\psi_0(1)=$ `r round(EY1, 2)`

, and $\psi_0(2)=$ `r round(EY2,2)`

.

set.seed(1234) n <- 200 W <- data.frame(W1 = runif(n), W2 = rbinom(n, 1, 0.5)) A <- rbinom(n, 2, plogis(W$W1 + W$W2)) Y <- rbinom(n, 1, plogis(W$W1 + W$W2*A))

The multi-level PS can still be estimated using any of the three techniques discussed previously. Here, we illustrate with simple generalized linear models.

glm_fit_multiA <- drtmle(W = W, A = A, Y = Y, stratify = FALSE, glm_g = "W1 + W2", glm_Q = "W1 + W2*A", glm_gr = "Qn", glm_Qr = "gn", family = binomial(), a_0 = c(0,1,2)) glm_fit_multiA

Above, we used the option `a_0`

to specify the levels of the treatment at which
we were interested in estimating marginal means. There is no need for `a_0`

to
contain all unique values of `A`

. For example, certain levels of the treatment
may not be of scientific interest, but nevertheless we would like to use these
data to help estimate the OR and PS (by setting `stratify = FALSE`

).

The confidence interval and testing procedures extend to multi-level treatments
with minor modifications. We can still obtain a confidence interval and
hypothesis test for each of the marginal means by not specifying a `contrast`

.

ci(glm_fit_multiA) wald_test(glm_fit_multiA, null = c(0.4, 0.5, 0.6))

Alternatively, we can specify a `contrast`

to estimate confidence intervals
about the average treatment effect of $A=1$ vs. $A=0$. Calls to `wald_test`

can
also be made.

ci(glm_fit_multiA, contrast = c(-1, 1, 0))

Similarly, we can compute confidence intervals comparing the average treatment effect of $A = 2$ vs. $A = 0$.

ci(glm_fit_multiA, contrast = c(-1, 0, 1))

The user-specified contrasts are also available. For example, we can modify the
`riskRatio`

object we used previously for a two-level treatment to compute the
risk ratio comparing $A=1$ to $A=0$ and comparing $A = 2$ to $A = 0$.

riskRatio_1v0 <- list(f = function(eff){ log(eff) }, f_inv = function(eff){ exp(eff) }, h = function(est){ est[2]/est[1] }, fh_grad = function(est){ c(1/est[2], -1/est[1], 0) }) ci(glm_fit_multiA, contrast = riskRatio_1v0)

Similarly, we can compute confidence intervals for the risk ratio comparing the marginal mean for $A=2$ to $A=1$.

riskRatio_2v0 <- list(f = function(eff){ log(eff) }, f_inv = function(eff){ exp(eff) }, h = function(est){ est[3]/est[1] }, fh_grad = function(est){ c(0, -1/est[1], 1/est[3]) }) ci(glm_fit_multiA, contrast = riskRatio_2v0)

When considering adaptive estimation techniques, one runs the risk of
overfitting the initial regressions. While the cross validation used by super
learning attempts to guard against overfitting, there are no guarantees in
finite-samples. To definitively avoid such overfitting, one can use
cross-validated estimates of the regression parameters. Theoretically, the use
of cross-validated regression weakens the assumptions necessary to achieve
asymptotic normality [@zheng:2010:working]. This is true of the regular TMLE and
AIPTW, as well as of the TMLE estimator with doubly-robust inference. The
`drtmle`

function implements cross-validated estimation of the regression
parameters through the `cvFolds`

option, as shown in the code below, where we
continue with the multi-level treatment example.

cv_sl_fit <- drtmle(W = W, A = A, Y = Y, family = binomial(), SL_g = c("SL.glm", "SL.glm.interaction", "SL.gam"), SL_Q = c("SL.glm", "SL.glm.interaction", "SL.gam"), SL_gr = c("SL.glm", "SL.mean"), SL_Qr = c("SL.glm", "SL.mean"), stratify = FALSE, cvFolds = 2, a_0 = c(0, 1, 2)) cv_sl_fit

Cross-validation may significantly increase the computation time necessary for
running `drtmle`

. Parallelized regression fitting is available by setting the
option `use_future = TRUE`

. Since, internally, all computations are carried out
using futures (with the API provided by the "future" R package), setting this
option amounts only to parallelizing with futures [@futurePackage] via
`future.apply::lapply`

. Selecting this option results in parallelization of the
estimation of the OR, PS, and reduced-dimension regressions.

If no adjustments are made prior to invoking the `drtmle`

function, futures are
computed sequentially. It is up to the user to specify appropriate system-specific
future back-ends that may better suit their problem (e.g., using the `future.batchtools`

package [@futurebatchtoolsPackage]). Below we
demonstrate registration using an ad-hoc cluster on the local system. The choice
of back-end is flexible, and submission of the job to a wide variety
of schedulers (e.g., SLURM, TORQUE, etc.) is also permitted. Interested readers
are invited to consult the documentation of the `future.batchtools`

package for
further information.

## Commented out to avoid build errors # library(future.batchtools) # library(parallel) # cl <- makeCluster(2, type = "SOCK") # plan(cluster, workers = cl) # clusterEvalQ(cl, library("SuperLearner")) # pcv_sl_fit <- drtmle(W = W, A = A, Y = Y, family = binomial(), # SL_g = c("SL.glm", "SL.glm.interaction","SL.gam"), # SL_Q = c("SL.glm", "SL.glm.interaction","SL.gam"), # SL_gr = c("SL.glm", "SL.gam", "SL.mean"), # SL_Qr = c("SL.glm", "SL.gam", "SL.mean"), # stratify = FALSE, a_0 = c(0,1,2), # cvFolds = 2, use_future = TRUE)

Doubly-robust estimators of marginal means are appealing in their robustness and
efficiency properties. However, researchers may prefer the IPTW estimator for
its intuitive derivation and implementation. Heretofore, inference for IPTW
estimators precluded the use of adaptive estimators of the PS. However, van der
Laan (2014) proposed modified IPTW estimators that allow for adaptive PS
estimation. Similarly as above, this estimator is based on an additional
regression parameter, $\tilde{Q}_{r,0n}(a,w) := E_0{Y \mid A = a, g_n(W) =
g_n(w)}$, that is the regression of the outcome $Y$ on the estimated propensity
amongst observations with $A=a$. The author showed that if $g_n^*(a \mid w)$, an
estimator of the propensity for treatment $A=a$, satisfied that
$$ \frac{1}{n} \sum\limits_{i=1}^n \frac{\tilde{Q}_{r,0}(a,W_i)}{g_n(a \mid W =
W_i)} \ { I(A_i = a) - g_n(a \mid W = W_i)} = 0 \ ,$$
then the IPTW estimator based on $g_n^*$ has an asymptotic normal distribution
with an estimable variance. In fact, van der Laan (2014) proposed a TMLE
algorithm that mapped an initial PS estimate into an estimate that satisfies
this key equation and variance estimators that can be used to generate
Wald-style confidence intervals and hypothesis tests.

An estimator with equivalent asymptotic properties based on a PS estimate,
$g_n(a \mid w)$, can be computed as
$$
\psi_{n,IPTW}(a) = \psi_{n,IPTW}(a) - \frac{1}{n} \sum\limits_{i=1}^n
\frac{\tilde{Q}_{r,0}(a,W_i)}{g_n(a \mid W = W_i)} \ { I(A_i = a) - g_n(a
\mid W = W_i)} \ .
$$
Both this estimator and the TMLE-based IPTW estimator above are implemented in
`drtmle`

.

The IPTW estimators based on adaptive estimation of the PS are implemented in
the `adaptive_iptw`

function. The `adaptive_iptw`

function shares many options
with `drtmle`

. However, as the IPTW estimator does not rely on an estimate of
the OR, these options are omitted. We continue with the multilevel treatment
example.

npreg_iptw_multiA <- adaptive_iptw(W = W, A = A, Y = Y, stratify = FALSE, SL_g = "SL.npreg", SL_Qr = "SL.npreg", family = binomial(), a_0 = c(0, 1, 2)) npreg_iptw_multiA

The `"adaptive_iptw"`

class contains identical methods for computing confidence
intervals and Wald tests as the `"drtmle"`

class and we refer readers back to
confidence intervals and hypothesis tests section for reminders on the various
options for these tests. By default, these methods are implemented for the
`iptw_tmle`

estimator of van der Laan (2014). We expect that this estimator
will have improved finite-sample behavior relative to `iptw_os`

(the
alternative estimator described in the previous subsection), though further
study is needed.

As with `drtmle`

, the `adaptive_iptw`

function allows missing data in `A`

and
`Y`

. Similarly, `adaptive_iptw`

also allows for parallelized, cross-validated
estimation of the regression parameters via `cvFolds`

and `parallel`

options.
As with the estimators implemented in `drtmle`

, cross-validation allows for
valid inference under weaker assumptions.

The `drtmle`

package was designed to provide a user-friendly implementation of
the TMLE algorithm that facilitates doubly-robust inference about marginal
means and average treatment effects. While the theory underlying doubly-robust
inference is complex, the implementation of the methods only requires users to
provide the data and specify how to estimate regressions. While estimation of
the OR and PS is familiar to those familiar with causal inference-related
estimation, the reduced-dimension regressions may appear strange. Indeed, it is
difficult to provide intuition for these parameters and more difficult to
determine what scientific background knowledge might guide users in how to
estimate these regression parameters. Therefore, we recommended estimating
these quantities adaptively, e.g., with the super learner. The super learner
library should contain several relatively smooth estimators such as `SL.mean`

,
`SL.glm`

, and `SL.gam`

. In particular, including `SL.mean`

(an unadjusted
generalized linear model) may be important due to the fact that, when the OR
and PS are consistently estimated, the reduced-dimension regressions are
identically equal to zero. Thus, this unadjusted regression estimator may do a
good job estimating the reduced-dimension regressions in situations where the
OR and PS are estimated well. In addition to relatively smooth estimators, we
recommend additionally including several adaptive estimators such as `SL.npreg`

or `SL.gam`

.

Planned extensions of the `drtmle`

package include extensions of longitudinal
settings involving treatments at multiple time points and inclusion of the
collaborative targeted maximum likelihood estimator (CTMLE) of van der Laan
(2014) that also facilitates collaborative doubly-robust inference.

```
sessionInfo()
```

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