Improving precision of cumulative incidence estimates in randomized controlled trials with external controls

2025-06-23

AI Summary

To address truncation bias due to competing risks (e.g., death) and insufficient control-arm sample size in randomized controlled trials (RCTs), this paper proposes a novel method that integrates external control data to improve estimation accuracy of cumulative incidence functions (CIFs). We introduce a condition-specific risk transportability assumption, derive the semiparametric efficiency bound for causal CIFs, and construct a triply robust estimator. External and RCT data are calibrated and fused via time-specific weighting within a martingale integral framework. Simulation studies and empirical analysis using cardiovascular clinical trial data demonstrate that the proposed method substantially reduces standard errors and enhances statistical power, while maintaining both theoretical efficiency optimality and practical robustness.

Technology Category

Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningIntelligent Robots: State Estimation

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSecurity and Privacy: Large-scale security measurementsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Augmenting the control arm in clinical trials with external data can improve statistical power for demonstrating treatment effects. In many time-to-event outcome trials, participants are subject to truncation by death. Direct application of methods for competing risks analysis on the joint data may introduce bias, for example, due to covariate shifts between the populations. In this work, we consider transportability of the conditional cause-specific hazard of the event of interest under the control treatment. Under this assumption, we derive semiparametric efficiency bounds of causal cumulative incidences. This allows for quantification of the theoretical efficiency gain from incorporating the external controls. We propose triply robust estimators that can achieve the efficiency bounds, where the trial controls and external controls are made comparable through time-specific weights in a martingale integral. We conducted a simulation study to show the precision gain of the proposed fusion estimators compared to their counterparts without utilizing external controls. As a real data application, we used two cardiovascular outcome trials conducted to assess the safety of glucagon-like peptide-1 agonists. Incorporating the external controls from one trial into the other, we observed a decrease in the standard error of the treatment effects on adverse non-fatal cardiovascular events with all-cause death as the competing risk.

Problem

• Improving precision of cumulative incidence estimates in trials
• Addressing bias in competing risks analysis with external controls
• Enhancing efficiency of treatment effect estimation using transportability

Innovation

• Transportability of conditional cause-specific hazard
• Semiparametric efficiency bounds derivation
• Triply robust estimators with time-specific weights