Robust evaluation of treatment effects in longitudinal studies with truncation by death or other intercurrent events

2025/09/12 by Georgi Baklicharov, Kelly Van Lancker, Baklicharov, Georgi +3
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2509.10067

openalex publication_date 2025/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Intercurrent events, such as treatment switching, rescue medication, dropout, or truncation by death, frequently complicate intention-to-treat analyses in randomized clinical trials. Existing causal inference frameworks typically target hypothetical or principal stratum estimands (e.g., survivor average causal effects), which rely on unverifiable assumptions and can be sensitive to unmeasured confounders or positivity violations. We propose a novel approach that mitigates this sensitivity by using only information measured prior to the intercurrent event. Our key idea is to compare treated and untreated individuals, matched on baseline covariates, at the most recent time point before either experiences an intercurrent event. We call these contrasts Pairwise Last Observation Time (PLOT) estimands. PLOT estimands are identified in randomized trials without structural assumptions, even under severe positivity violations. Although PLOT-based tests may theoretically be susceptible to residual selection bias, we show this bias vanishes under standard conditions and remains negligible in extensive simulations. We develop asymptotically efficient, model-free tests and treatment effect estimators using data-adaptive nuisance parameter estimation. We evaluate performance via simulation and apply the method to re-analyze the DEVOTE trial, affected by truncation by death. PLOT offers a robust, data-driven alternative for evaluating treatment efficacy in the presence of complex intercurrent events.

Citations

• Estimating treatment effects with competing intercurrent events in randomized controlled trials
• Chasing Shadows: How Implausible Assumptions Skew Our Understanding of Causal Estimands
• Instrumental Variable methods to target Hypothetical Estimands with longitudinal repeated measures data: Application to the STEP 1 trial
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• Hypothetical estimands in clinical trials: a unification of causal inference and missing data methods
• Inference for treatment-specific survival curves using machine learning
• Conditional separable effects
• Assumption-lean inference for generalised linear model parameters
• The Consquences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment

Related

• Hypothetical estimands in clinical trials: a unification of causal inference and missing data methods
• Causal Inference with Outcomes Truncated by Death and Missing Not at Random
• Adjusting for time-varying treatment switches in randomized clinical trials: the danger of extrapolation and how to avoid it
• Quantifying and Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time
• Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation