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A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Population-Adjusted Indirect Treatment Comparison with th...
[Submitted on 12 Jan 2026 (v1), last revised 9 Jun 2026 (this ve · 2026-06-10 · via stat updates on arXiv.org

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Abstract:Indirect treatment comparisons (ITCs) are essential in Health Technology Assessment (HTA) when head-to-head clinical trials are absent. A common challenge arises when attempting to compare a treatment with available individual patient data (IPD) against a competitor with only reported aggregate-level data (ALD), particularly when trial populations differ in effect modifiers. While methods such as Matching-Adjusted Indirect Comparison (MAIC) exist to adjust for these cross-trial differences, they are increasingly being superseded by regression-based marginalization methods. Historically, software implementations for these methods have often been fragmented or limited in scope. This article introduces outstandR, an R package designed to provide a comprehensive and unified framework for population-adjusted indirect comparison (PAIC). outstandR implements advanced G-computation methods - within both maximum likelihood and Bayesian frameworks, and Multiple Imputation Marginalization (MIM) to address non-collapsibility. By streamlining the workflow of covariate simulation, model standardization, and contrast estimation, outstandR enables robust and compatible evidence synthesis in complex decision-making scenarios.

Submission history

From: Nathan Green Dr [view email]
[v1] Mon, 12 Jan 2026 13:33:15 UTC (128 KB)
[v2] Thu, 15 Jan 2026 15:36:18 UTC (127 KB)
[v3] Tue, 9 Jun 2026 13:40:11 UTC (124 KB)