Tipping Point Sensitivity Analysis for Missing Data in Time-to-Event Endpoints: Model-Based and Ad hoc Approaches

Ajmal Oodally; Arunava Chakravartty; Craig Wang; Tim Morris; Tobias M\"utze; Zheng Li

arxiv: 2506.19988 · v3 · submitted 2025-06-24 · 📊 stat.ME

Tipping Point Sensitivity Analysis for Missing Data in Time-to-Event Endpoints: Model-Based and Ad hoc Approaches

Ajmal Oodally , Craig Wang , Zheng Li , Tim Morris , Tobias M\"utze , Arunava Chakravartty This is my paper

Pith reviewed 2026-05-19 07:49 UTC · model grok-4.3

classification 📊 stat.ME

keywords tipping point analysismissing datatime-to-event endpointsindependent censoringsensitivity analysisclinical trialsimputationtreatment policy estimand

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The pith

Tipping point analyses assess how robust time-to-event trial results remain when independent censoring is violated.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how missing data from study discontinuation in clinical trials with time-to-event outcomes can violate the usual independent censoring assumption and bias treatment policy estimands. It describes and contrasts a model-based tipping point method with two simpler ad hoc methods that impute data via landmark or percentile sampling. These approaches are applied to reconstructed data from real trials to compare their assumptions and how plausible the resulting sensitivity conclusions appear. A reader would care because regulators favor treatment policy estimands that reflect all assigned treatments, yet dropouts often relate to intercurrent events and treatment discontinuation. The work highlights how different methods affect interpretation and clinical plausibility of the robustness checks.

Core claim

Tipping-point analyses provide a structured framework to assess the robustness of trial conclusions to departures from the independent censoring assumption. Model-based and two ad hoc approaches (landmark or percentile sampling based imputation) can be contrasted for their underlying assumptions and implications for interpretation and clinical plausibility assessments, as illustrated through re-constructed examples based on real clinical trials.

What carries the argument

Tipping-point sensitivity analysis, which systematically varies assumptions about censoring or imputes missing event times to identify the point at which trial conclusions would change.

If this is right

Trial conclusions under treatment policy estimands can be evaluated for sensitivity to realistic violations of independent censoring.
Ad hoc imputation methods offer simpler alternatives that may align better with clinical judgment in some settings.
Reconstructed real-trial examples demonstrate how method choice influences assessments of result robustness.
These analyses help identify when missing data from discontinuation could materially affect regulatory conclusions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Routine inclusion of tipping point checks in trial reports could make assumptions about censoring more transparent to reviewers.
The methods might extend to other intercurrent events beyond discontinuation if the imputation logic is adapted.
Direct head-to-head comparisons with multiple imputation under missing-at-random could test consistency across sensitivity tools.

Load-bearing premise

The ad hoc tipping point approaches based on landmark or percentile sampling imputation produce clinically interpretable and plausible sensitivity results when applied to reconstructed examples from real trials.

What would settle it

Application of both model-based and ad hoc tipping point methods to a new dataset where the ad hoc methods yield sensitivity conclusions that are markedly less clinically plausible than the model-based results.

read the original abstract

Treatment policy estimands are frequently favored by regulators, as they assess the effect of treatment assignment regardless of post-randomization events. Despite best efforts, missing data due to study discontinuation cannot be fully avoided and, for time-to-event endpoints, typically manifests as right censoring. Study discontinuation is often more likely following intercurrent events, particularly when it coincides with treatment discontinuation, raising concerns about violations of the independent censoring assumption. Although the independent censoring assumption is routinely adopted for the main analyses, it may be unrealistic in practice and could lead to biased estimation of the treatment effect under the treatment policy estimand. Tipping-point analyses provide a structured framework to assess the robustness of trial conclusions to departures from the independent censoring assumption. This paper describes and contrasts model-based and two ad hoc tipping point approaches, which involve "landmark" or "percentile sampling" based imputation. We illustrate their application using re-constructed examples based on real clinical trials, highlighting their underlying assumptions and implications for interpretation and clinical plausibility assessments of different tipping point approaches.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper compares model-based and ad hoc tipping point methods for checking robustness to non-independent censoring in time-to-event trial data, using reconstructed examples to contrast assumptions and plausibility.

read the letter

The main point is that tipping point sensitivity analysis offers a way to test how much trial conclusions on treatment policy estimands might shift if independent censoring fails for survival endpoints. The authors set out a model-based route next to two ad hoc imputation variants—one using landmarks and one using percentile sampling—and walk through their application on data rebuilt from real trials. They spell out the differing assumptions and what those mean for clinical interpretation of the resulting tipping points. That side-by-side framing is the useful piece here, because it lets readers see the mechanics and trade-offs in one place rather than hunting across papers. The reconstructed examples give concrete illustrations of how the methods behave in plausible trial settings, which helps ground the discussion of plausibility. The work stays within existing ideas on sensitivity analysis and does not claim a new theoretical derivation or estimator. It relies on reconstructions rather than original datasets, so the illustrations show application but not performance against known truth. There is also limited discussion of how sensitive the ad hoc results are to the exact choice of landmark times or percentile cutoffs, which could matter in practice. This is aimed at trial statisticians who handle missing data and estimand questions for regulatory submissions. Readers already working with survival analyses and sensitivity tools will get the most direct value from the explicit contrasts. It deserves a serious referee because it engages a real practical issue with usable comparisons, even though the core ideas are not new.

Referee Report

0 major / 2 minor

Summary. The paper describes and contrasts model-based and ad hoc tipping point approaches (using landmark or percentile sampling based imputation) for sensitivity analysis of missing data in time-to-event endpoints under the treatment policy estimand. It illustrates their application on reconstructed examples from real clinical trials, highlighting assumptions, implications for interpretation, and clinical plausibility assessments of different tipping point approaches.

Significance. If the results hold, this work supplies a coherent and practical framework for probing departures from the independent censoring assumption in treatment-policy estimands for time-to-event data. The explicit contrast between model-based and ad hoc methods, together with discussion of their imputation mechanics and clinical plausibility in reconstructed trial contexts, offers regulators and trialists a structured way to assess robustness of conclusions; the emphasis on interpretability strengthens its utility for sensitivity analyses that are increasingly expected in regulatory submissions.

minor comments (2)

The abstract states that the ad hoc approaches 'produce clinically interpretable and plausible sensitivity results' when applied to reconstructed examples; a brief sentence clarifying the criteria used to judge clinical plausibility would help readers evaluate this claim without needing the full results section.
Section describing the percentile sampling imputation: the mechanics of how the sampling distribution is constructed and how the tipping point is located should be stated more explicitly (e.g., whether the percentile is fixed or varied continuously) to support reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and constructive review, which accurately summarizes the paper's focus on contrasting model-based and ad hoc tipping-point approaches for assessing robustness to departures from independent censoring in time-to-event analyses under the treatment-policy estimand. We appreciate the recognition of its potential utility for regulators and trialists and are pleased to receive a recommendation for minor revision.

Circularity Check

0 steps flagged

No significant circularity in methodological comparison

full rationale

The paper presents a comparative framework for tipping-point sensitivity analyses in time-to-event data with right censoring from study discontinuation. It explicitly describes the model-based approach alongside two ad hoc methods (landmark and percentile sampling imputation), contrasts their assumptions and imputation mechanics, and applies them to reconstructed examples from real trials to discuss clinical plausibility. No equations, derivations, or first-principles results are shown that reduce outputs to fitted parameters by construction, invoke self-citations as load-bearing uniqueness theorems, or rename known results as new predictions. The work remains self-contained as an illustrative methodological contrast using external data reconstructions, with independent content in the assumption contrasts and application examples.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the domain assumption that independent censoring is routinely adopted yet potentially unrealistic, with no free parameters or invented entities introduced in the abstract description.

axioms (1)

domain assumption Independent censoring assumption is routinely adopted for main analyses but may be unrealistic when study discontinuation coincides with treatment discontinuation.
Directly stated in the abstract as raising concerns about biased estimation of the treatment effect.

pith-pipeline@v0.9.0 · 5738 in / 1216 out tokens · 36785 ms · 2026-05-19T07:49:18.604642+00:00 · methodology

Tipping Point Sensitivity Analysis for Missing Data in Time-to-Event Endpoints: Model-Based and Ad hoc Approaches

Core claim

What carries the argument

If this is right

Where Pith is reading between the lines

Load-bearing premise

What would settle it

discussion (0)