Indirect treatment comparisons (ITCs) are vital when head-to-head
clinical trials are absent. Unadjusted comparisons are biased when trial
populations differ in effect modifiers; thus, population adjustment is
required. outstandR provides a unified framework for these
methods.
MAIC is a method-of-moments weighting approach designed to match the aggregate characteristics of the comparator trial. Weights \(w_i\) for each patient \(i\) in the IPD trial are derived via a logistic regression formulation:
\[w_i=\exp(\beta^\top X_i)\]
The parameters \(\beta\) are found by minimizing a convex objective function to match target covariate means:
\[\min_{\beta\in\mathbb{R}^p}\sum_{i=1}^{n_A}\exp(\beta^\top(X_i-\overline{X}_B))\]
For an individual \(i\) with outcome \(Y_i\), treatment \(T_i \in \{0,1\}\), and baseline covariates \(X_i\), the general outcome model is:
\[g(\mathbb{E}[Y_i|X_i,T_i])=\alpha+X_i^\top\beta_0+(\beta_{trt}+X_i^\top\beta_1)\mathbb{I}(T_i=1)\]
G-computation standardizes outcomes across treatment regimens. We estimate the marginal mean outcome by simulating a pseudo-population of size \(N\) reflecting the target ALD trial, predicting outcomes, and averaging:
\[\hat{\mathbb{E}}[Y^T|\hat{\theta}]=\frac{1}{N}\sum_{i=1}^N\hat{Y_i}(T,\hat{\theta})\]
Bayesian G-computation estimates the full posterior distribution of the model parameters, offering robust uncertainty quantification. For \(M\) posterior samples \(\theta^{(m)}\), the marginal mean is computed as:
\[\hat{\mathbb{E}}[Y^T|\theta^{(m)}]=\frac{1}{N}\sum_{i=1}^N\hat{Y}_i(T,\theta^{(m)})\]
MIM conceptualizes unobserved potential outcomes as a missing data problem. Using \(M\) posterior draws, MIM imputes counterfactuals and utilizes modified Rubin’s rules to calculate total variance by subtracting within-imputation variance (\(\overline{U}\)) from between-imputation variance (\(B\)):
\[Var(\overline{Q})=B-\overline{U}\]