Introduction

The ICH E9(R1) addendum proposed a framework for the formulation of scientific questions and treatment effects in clinical trials. The framework emphasized the importance of formulating precisely the scientific questions of interest in clinical trials in the form of an estimand. An estimand describes the treatment effect of interest via five attributes, which include treatment, population, variable (endpoint), intercurrent event and population-level summary.

Recent FDA guidance (2023) on Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products for Industry discussed various considerations when prospectively specifying covariate-adjusted analysis, including the distinction between unconditional vs. conditional average treatment effects.

This vignette provides

1. A brief explanation of the different concepts on average treatment effect covered in the package.
2. An analysis example using the package to estimate an average treatment effect in a clinical trial with covariate adjustment.
3. A comparison with other implementations of the methods.

General concepts

What is an average treatment effect?

In general, an average treatment effect is the effect of moving a population from untreated (control treatment) to treated (experimental treatment). An average treatment effect can be estimated either with or without covariate adjustment.

Different flavours of average treatment effect exist, depending on the specific population that is moved from untreated to treated. For an overall superpopulation (denoted $$S$$), and for a given sample of patients in the trial (who are assumed to be randomly sampled from the superpopulation), we define the following potential estimands.

1. Population Average Treatment Effect $$\mathrm{PATE}^S$$. The average outcome of everyone in the superpopulation if they were treated with the experimental treatment vs. the average outcome if they were treated with the control treatment.
2. Conditional Average Treatment Effect, $$\mathrm{CATE}^S(x)$$. The average outcome of everyone in the superpopulation with covariate value $$x$$ if they were treated with the experimental treatment vs. the average outcome if they were treated with the control treatment.
3. Conditional Population Average Treatment Effect $$\mathrm{CPATE}^S$$. The average of $$\mathrm{CATE}^S(x_i)$$ for the patients $$i=1,\ldots,N$$ in the trial sample. Can also be thought of as the expected difference in outcome means (experimental vs. control), conditional on the baseline covariates $$x_1,\ldots,x_N$$.
4. Sample Average Treatment Effect $$\mathrm{SATE}$$. The average outcome of everyone in the trial if they were treated with the experimental treatment vs. the average outcome if they were treated with the control treatment.

In the FDA guidance, and in other recent discussions on covariate adjustment, average treatment effects are often classified as either unconditional or conditional. (Sometimes marginal is used interchangeably with unconditional). In this context, what is usually meant by “conditional” is $$\mathrm{CATE}^S(x)$$. That is, a subgroup specific average treatment effect. Often, an assumption is made that subgroup specific average treatment effects are identical across subgroups. If this assumption is wrong, then interpretation can be challenging. What is usually meant by “unconditional” (or “marginal”) is an average of subgroup-specific average treatment effects. This second averaging step gives corresponding estimation procedures a level of robustness to model misspecification. Hence the following distinction in the FDA guidance:

1. Sponsors can perform covariate-adjusted estimation and inference for an unconditional treatment effect in the primary analysis of data from a randomized trial.
2. Sponsors should discuss with the relevant review divisions specific proposals in a protocol or statistical analysis plan containing nonlinear regression to estimate conditional treatment effects for the primary analysis.

Under the coarse classification of average treatment effects as either “unconditional / marginal” or “conditional”, the $$\mathrm{PATE}^S$$, $$\mathrm{CPATE}^S$$ and $$\mathrm{SATE}$$ can be viewed as “unconditional / marginal”, while the $$\mathrm{CATE}^S(x)$$ can be viewed as “conditional”.

How to estimate “unconditional / marginal” average treatment effect?

For a binary endpoint, an average treatment effect, whether it be a $$\mathrm{PATE}^S$$, $$\mathrm{CPATE}^S$$ or $$\mathrm{SATE}$$, can be estimated from an adjusted logistic regression using the g-computation method. That is, by estimating the average counterfactual outcomes on each arm based on predictions from the working model. Based on these average counterfactual outcomes, different summary measure such as risk difference, relative risk or odds ratio can be evaluated.

Regardless of whether the estimand is $$\mathrm{PATE}^S$$, $$\mathrm{CPATE}^S$$ or $$\mathrm{SATE}$$, the same point estimator can be used.

How to estimate the variance of the g-computation estimator of average treatment effect?

To avoid ambiguity regarding how the variance of the g-computation estimator should be calculated, it is necessary to be precise about whether one is estimating the $$\mathrm{PATE}^S$$, $$\mathrm{CPATE}^S$$ or $$\mathrm{SATE}$$.

{beeca} implements variance estimation for $$\mathrm{PATE}^S$$ based on the methods of Ye et al. (2023), and $$\mathrm{CPATE}^S$$ based on Ge et al. (2011).

See Magirr et al. (2024) for discussion of the estimands $$\mathrm{PATE}^S$$ and $$\mathrm{CPATE}^S$$, and the corresponding methodology for variance estimation.

Analysis example

In the following example, we will use an example CDISC clinical trial dataset in ADaM format (?trial02_cdisc). We will only include the two treatment arms of interest for the analysis: “Xanomeline High Dose” and “Placebo”. Below we show how to describe and perform an adjusted analysis targeting marginal risk difference between the two arms.

A logistic regression model will be used to estimate the average treatment effect between Xanomeline High Dose and Placebo. The model will adjust for baseline values of patient sex, race and age. Difference in marginal response proportions with p-value and corresponding 95% confidence interval will be estimated from the logistic regression model using the methodology described in Ge et al. 2011 with sandwich variance estimator (Liu et al. 2023). The sandwich estimator provides robustness to model-misspecification.

library(beeca)
library(dplyr)
## Prepare the dataset for input
dat <- trial02_cdisc %>%
## In this case we are only interested in comparing two arms: Placebo vs Xanomeline High Dose
dplyr::filter(TRTP %in% c("Placebo", "Xanomeline High Dose")) %>%
## Treatment variable must be coded as a factor
dplyr::mutate(TRTP = factor(TRTP))

## Fit the logistic regression model adjusting for SEX, RACE and AGE
fit <- glm(AVAL ~ TRTP + SEX + RACE + AGE, family = "binomial", data = dat)

## Calculate the marginal treatment effect estimate and associated variance for a difference contrast
## using the Ge et al. method with robust HC0 sandwich variance
marginal_fit <- get_marginal_effect(fit,
trt = "TRTP",
method = "Ge",
type = "HC0",
contrast = "diff",
reference = "Placebo"
)

The results are summarised in ARD format (Analysis Results Dataset) for ease of subsequent reporting.

## View the ARD summary
marginal_fit$marginal_results ## # A tibble: 12 × 8 ## TRTVAR TRTVAL PARAM ANALTYP1 STAT STATVAL ANALMETH ANALDESC ## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> ## 1 TRTP Xanomeline High Dose AVAL DESCRIP… N 84 count Compute… ## 2 TRTP Xanomeline High Dose AVAL DESCRIP… n 61 count Compute… ## 3 TRTP Xanomeline High Dose AVAL DESCRIP… % 72.6 percent… Compute… ## 4 TRTP Xanomeline High Dose AVAL INFEREN… risk 0.722 g-compu… Compute… ## 5 TRTP Xanomeline High Dose AVAL INFEREN… risk… 0.0493 Ge - HC0 Compute… ## 6 TRTP Placebo AVAL DESCRIP… N 86 count Compute… ## 7 TRTP Placebo AVAL DESCRIP… n 29 count Compute… ## 8 TRTP Placebo AVAL DESCRIP… % 33.7 percent… Compute… ## 9 TRTP Placebo AVAL INFEREN… risk 0.344 g-compu… Compute… ## 10 TRTP Placebo AVAL INFEREN… risk… 0.0510 Ge - HC0 Compute… ## 11 TRTP diff: Xanomeline High … AVAL INFEREN… diff 0.378 g-compu… Compute… ## 12 TRTP diff: Xanomeline High … AVAL INFEREN… diff… 0.0714 Ge - HC0 Compute… The results can then be passed on to the prespecified testing strategy as required. Below we provide a simple example where normal approximation is applied: ## Extract results marginal_results <- marginal_fit$marginal_results
diff_est <- marginal_results[marginal_results$STAT == "diff", "STATVAL"][[1]] diff_se <- marginal_results[marginal_results$STAT == "diff_se", "STATVAL"][[1]]

## 95% confidence interval
ci_l <- diff_est - (qnorm(0.975) * diff_se)
ci_u <- diff_est + (qnorm(0.975) * diff_se)

## Two-sided p-value
z_score <- diff_est / diff_se
p_value <- 2 * (1 - pnorm(abs(z_score)))

sprintf("The risk difference is %s with 95%% CI: (%s - %s)", round(diff_est, 2), round(ci_l, 2), round(ci_u, 2))
## [1] "The risk difference is 0.38 with 95% CI: (0.24 - 0.52)"
sprintf("p-value: %s", formatC(p_value, format = "e", digits = 2))
## [1] "p-value: 1.15e-07"

Comparing different implementations

To illustrate the usage of {beeca} and for purposes of results validation, we compare {beeca} with other available implementations of Ge et al. (2011) and Ye et al. (2023) methods in R and SAS. We demonstrate equivalence of results while highlighting the simple user interface of {beeca}. Our lightweight package has been developed with a focus on facilitating quick industry adoption including compliance with GxP validation requirements with minimal dependencies.

Throughout the comparisons, we use the dataset trial01 included in the {beeca} package and focus on the risk difference contrast.

Ge et al (2011)

# pre-process trial01 dataset to convert treatment arm to a factor and handle missing value
data01 <- trial01 |>
transform(trtp = as.factor(trtp)) |>
dplyr::filter(!is.na(aval))
fit <- glm(aval ~ trtp + bl_cov, family = "binomial", data = data01)
beeca_ge <- get_marginal_effect(object = fit, trt = "trtp", method = "Ge",
contrast = "diff", reference = "0", type = "model-based")
cat("Point estimate", beeca_ge$marginal_est, "\nStandard error estimate", beeca_ge$marginal_se)
## Point estimate -0.06836399
## Standard error estimate 0.06071641

Alternative version 1: code provided in Ge et al. (2011) paper. Note the corresponding SAS code is also available in the paper.

ge_var_paper <- function(glmfit, trt) {
pder <- function(ahat, vc, x) {
#### ahat: logistic regression parameters
#### vc: variance-covariance matrix of ahat
#### x: full model matrix of the logistic regression
#### return mean of phat on x and its se
phat <- plogis(x %*% ahat)
pbar <- mean(phat)
pderiv <- t(phat * (1 - phat)) %*% x / nrow(x)
sepbar <- sqrt(pderiv %*% vc %*% t(pderiv))
return(list(pbar = pbar, sepbar = sepbar, pderiv = pderiv))
}

difP <- function(glmfit) {
#### estimate the proportion difference and its standard error
df <- glmfit$model vc <- vcov(glmfit) df[, trt] <- 1 mat <- model.matrix(glmfit$formula, data = df)
pderT <- pder(coef(glmfit), vc, mat)
df[, trt] <- 0
mat <- model.matrix(glmfit$formula, data = df) pderC <- pder(coef(glmfit), vc, mat) difb <- pderT$pbar - pderC$pbar sedif <- sqrt((pderT$pderiv - pderC$pderiv) %*% vc %*% t(pderT$pderiv - pderC$pderiv)) return(list( pT = pderT$pbar,
pC = pderC$pbar, dif = difb, sedif = sedif, var = sedif**2 )) } return(list(est = difP(glmfit)$dif, se = difP(glmfit)$sedif[[1]])) } paper_ge <- ge_var_paper(fit, "trtp") cat("Point estimate", paper_ge$est, "\nStandard error estimate", paper_ge$se) ## Point estimate -0.06836399 ## Standard error estimate 0.06071641 Version 2: using {margins} package if (requireNamespace("margins", quietly = T)) { margins_ge <- margins::margins(model = fit, variables = "trtp", vcov = vcov(fit)) cat("Point estimate", summary(margins_ge)$AME, "\nStandard error estimate", summary(margins_ge)$SE) } ## Point estimate -0.06836399 ## Standard error estimate 0.06071641 Version 3: using {marginaleffects} package if (requireNamespace("marginaleffects", quietly = T)) { marginaleffects_ge <- marginaleffects::avg_comparisons(fit, variables = "trtp") cat("Point estimate", marginaleffects_ge$estimate, "\nStandard error estimate", marginaleffects_ge$std.error) } ## Point estimate -0.06836399 ## Standard error estimate 0.06071641 Version 4: using SAS %Margins macro %Margins(data = myWork.trial01, class = trtp, classgref = first, /*Set reference to first level*/ response = avaln, roptions = event='1', dist = binomial, model = trtp bl_cov, margins = trtp, options = cl diff reverse, link = logit); ** Store output data sets ; data myWork.margins_trt_estimates; set work._MARGINS; run; data myWork.margins_trt_diffs; set work._DIFFSPM; run; cat("Point estimate", margins_trial01$Estimate, "\nStandard error estimate", margins_trial01$StdErr) ## Point estimate -0.06836399 ## Standard error estimate 0.06071641 Ye et al (2023) beeca_ye <- get_marginal_effect(object = fit, trt = "trtp", method = "Ye", contrast = "diff", reference = "0") cat("Point estimate", beeca_ye$marginal_est, "\nStandard error estimate", beeca_ye$marginal_se) ## Point estimate -0.06836399 ## Standard error estimate 0.0608836 Version 1: from RobinCar package if (requireNamespace("RobinCar", versionCheck = list(name = "RobinCar", op = "==", version = "0.3.0"), quietly = T)) { robincar_ye <- RobinCar::robincar_glm(data.frame(fit$data), response_col = as.character(fit$formula[2]), treat_col = "trtp", formula = fit$formula, g_family = fit$family, contrast_h = "diff")$contrast$result cat("Point estimate", robincar_ye$estimate, "\nStandard error estimate", robincar_ye\$se)
}
## Point estimate -0.06836399
## Standard error estimate 0.0608836