Oncology clinical trials are complex due to the nature of cancer progression, long follow-up times, start of further therapies, and ethical considerations. The estimand framework introduced in ICH E9(R1) provides a structured approach to align the clinical question with endpoints, intercurrent events, and analysis strategies.

Understanding the estimand framework in oncology

The estimand framework helps define what exactly a trial aims to measure, especially in the presence of intercurrent events (ICEs) that occur after treatment initiation and affect either the interpretation or existence of the outcome (like treatment discontinuation or new therapies).

Estimands need to be clearly defined in both the protocol and the Statistical Analysis Plan (SAP) using the five attributes outlined in the ICH E9(R1) addendum: population, variable (endpoint), treatment, intercurrent events and handling strategies, and population-level summary.

ICEs can complicate the estimation of treatment effects in oncology trials. Among these, the start of further anticancer therapy is particularly complex, especially when evaluating endpoints like Progression-Free Survival (PFS) and Overall Survival (OS).

Among all ICE handling strategies, two strategies are often used to handle the start of further anticancer therapy:

Hypothetical strategy

Estimate treatment effect in a world where further anticancer therapy would not exist.

• Implementation: Typically involves censoring patients at the time they start further anticancer therapy or using advanced statistical methods.
• Could be more meaningful from patient’s and prescriber’s perspective if subsequent therapies are not yet approved drugs and thus do not reflect clinical practice.
• May require additional data on baseline and/or time-dependent covariates to support modeling.

Treatment policy

Estimate treatment effect regardless of any further anticancer therapy, aiming to reflect real-world clinical practice.

• Implementation: Includes all events regardless of further anticancer therapy.
• Often considered as most relevant by regulatory authorities and other stakeholders if subsequent therapies are already approved and reflect clinical practice.
• Tend to dilute treatment effect.
• Assessments must continue beyond start of subsequent therapy.

Regulatory landscape

Historically, the FDA’s 2007 guidance leaned toward censoring at the start of new anticancer therapy — aligning with the hypothetical strategy. However, more recent guidance (2015, 2018) acknowledges both strategies, and the EMA’s 2012 guidance implicitly supports the treatment policy approach by recommending that progression should be considered even when observed after new anticancer treatment.

In Acute Myeloid Leukemia (AML), the FDA’s 2022 guidance is particularly clear: subsequent treatments like HSCT or anti-AML drugs should be considered part of the overall treatment regimen and not censored in the primary analysis.

When the hypothetical strategy may be preferable

In trials where conditions diverge significantly from routine clinical practice — such as early crossover or use of unapproved therapies — a hypothetical strategy may better capture the true clinical question.

Advanced methods like Rank Preserving Structural Failure Time (RPSFT) and Inverse Probability Censoring Weighting (IPCW) can help estimate what would have happened without treatment switching — but they come with assumptions and complexity.

Handling missing data in oncology

Effectively addressing missing data is essential for ensuring the reliability and integrity of statistical analyses in oncology trials. With regulatory agencies embracing the estimand framework, it’s essential to distinguish between ICEs and missing values, and to navigate their implications for primary and sensitivity analyses.

There is no consensus yet regarding how to handle missing tumor assessments in the primary analysis of PFS. Here’s a snapshot of key regulatory viewpoints:

According to the FDA’s 2018 guidance, “We recommend assigning the progression date to the earliest time when any progression is observed without prior missing assessments and censoring at the date when the last radiological assessment determined a lack of progression.”

The 2015 FDA NSCLC guidance offers case-based examples where progression events after two or more missed assessments are either censored or considered as events depending on the context, illustrating a cautious approach to ensure data robustness.

The 2012 EMA oncology guidelines advise against censoring for missed assessments: “The time of the progression or recurrence event is determined using the first date when there is documented evidence that the criteria have been met, even in situations where progression is observed after one or more missed visits, treatment discontinuation, or new anti-cancer treatment.”

Those different censoring rules can deeply impact PFS estimates, especially when early dropout rates are imbalanced between treatment arms.

Depending on the approach retained for the primary analysis, sensitivity analyses should be considered to assess the impact of missing tumor assessments. It may include a different set of censoring from the FDA guidance, but also interval censoring method.

Sensitivity and supplementary analyses

Understanding how different analyses relate to the primary estimand is critical for drawing robust and credible conclusions from clinical trial data. Two important analysis categories — sensitivity and supplementary analyses — serve distinct purposes and must be thoughtfully pre-specified in the SAP.

Sensitivity analyses: Testing the estimand’s foundations

According to ICH E9(R1), a sensitivity analysis is “a series of analyses conducted with the intent to explore the robustness of inferences from the main estimator to deviations from its underlying modeling assumptions and limitations in the data.”

Purpose: To verify that conclusions drawn from the primary analysis remain valid under alternative assumptions or data limitations. These analyses also probe key risks to inference, such as missing data or model specification.

Examples:

• Using an unstratified Cox model instead of a stratified one.
• Comparing investigator-assessed PFS with blinded independent central review (BICR)-assessed PFS.
• Applying alternative censoring rules (e.g., censoring after ≥2 missed tumor assessments), or interval-censored models for PFS.
• Using Restricted Mean Survival Time (RMST) to explore robustness under non-proportional hazards.

Supplementary analyses: Exploring beyond the estimand

While less explicitly defined, ICH E9(R1) describes supplementary analyses as: “Other analyses that are conducted in order to more fully investigate and understand the trial data.”

Purpose: To explore different strategies or assumptions that may be clinically or scientifically relevant.

Example: Using a different intercurrent event (ICE) strategy than the primary estimand.

Final takeaways

There’s no one-size-fits-all approach. Regulatory expectations continue to evolve, and sponsor decisions should balance regulatory guidelines, clinical practice norms, relevance to prescribers and patients, and feasibility of continued assessments.

Engaging in early discussions to align estimands with trial objectives and regulatory requirements is critical to ensuring efficient drug development and timely delivery to patients.

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