A Unified Three-State Model Framework for Analysis of Treatment Crossover in Survival Trials

Speaker: Ray Bai

Date: June 20th, 2025

Paper: Statistics in Biopharmaceutical Research

Session Overview:

This seminar will present a unified approach using three-state models for analyzing treatment crossover scenarios in survival trials, providing a comprehensive framework for handling complex switching patterns.

Key Topics:

• Three-state model methodology
• Treatment crossover analysis
• Survival trial design considerations
• Unified analytical framework
• Application to oncology clinical trials

Treatment Switching TSD 24 Overview

Speaker: Nick Latimer

Date: February 11th, 2025

Session Overview:

Nick Latimer joined us to give an overview on the recent updates to the Technical Support Document on treatment switching (TSD 24).

Resources:

• TSD 24 Document: Download from University of Sheffield
• Technical Support Document on treatment switching methodology
• Recent updates and enhancements to guidance

Adjusting for Time-Varying Treatment Switches in Randomized Clinical Trials: The Danger of Extrapolation and How to Address It

Speaker: Hege Michiels, Argenx

Date: April 3rd, 2024

Paper: Statistics in Biopharmaceutical Research

Abstract:

When choosing estimands and estimators in randomized clinical trials, caution is warranted, as intercurrent events, such as patients who switch treatment after disease progression, are often strongly associated with patients' time-varying prognostic factors. Consequently, for patients who did experience intercurrent events, there are typically no comparable patients who did not. Statistical analyses may then easily lure one into making large implicit extrapolations, which often go unnoticed.

We illustrate this problem of implicit extrapolations using a real oncology case study, with a right-censored time-to-event endpoint, in which patients can cross over from the control to the experimental treatment after disease progression, for ethical reasons. We address this by developing an estimator for the survival risk ratio contrasting the survival probabilities at each time t if all patients would take experimental treatment with the survival probabilities at those times t if all patients would take control treatment up to time t, using randomization as an instrumental variable to avoid reliance on no unmeasured confounders assumptions.

Key Topics:

• Time-varying treatment switches
• Extrapolation dangers in clinical trials
• Doubly robust estimators
• Instrumental variable approaches
• Right-censored survival analysis
• Real oncology case study applications

Structural Cumulative Survival Models for Estimation of Treatment Effects Accounting for Treatment Switching in Randomized Experiments

Speaker: Professor Eric Tchetgen Tchetgen, University of Pennsylvania

Date: February 27th, 2024 | Time: 1:30 – 3:00 PM EST

Paper: Biometrics | Oxford Academic

Abstract:

Treatment switching in a randomized controlled trial occurs when a patient in one treatment arm switches to another arm during follow-up. This can occur at the point of disease progression, whereby patients in the control arm may be offered the experimental treatment. It is widely known that failure to account for treatment switching can seriously bias the estimated treatment causal effect.

In this paper, we aim to account for the potential impact of treatment switching in a reanalysis evaluating the treatment effect of nucleoside reverse transcriptase inhibitors (NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in participants receiving a new antiretroviral regimen that either included or omitted NRTIs in the optimized treatment that includes or omits NRTIs trial.

We propose an estimator of a treatment causal effect for a censored time to event outcome under a structural cumulative survival model that leverages randomization as an instrumental variable to account for selective treatment switching. We establish that the proposed estimator is uniformly consistent and asymptotically Gaussian, with a consistent variance estimator.

Key Topics:

• Structural cumulative survival models
• Treatment switching in randomized controlled trials
• Instrumental variable approaches
• Causal inference for time-to-event outcomes
• Application to antiretroviral therapy trials