Evidence map›Paper›PMID 40676466›Full record

ArticleTherapeutic innovation & regulatory science2025

Estimand Endpoints for Longitudinal Measures of Continuous Disease Progression with an Alzheimer's Disease Example.

Haoyan Hu, Miroslaw Brys, Stephen J Ruberg, Yongming Qu

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Article in Therapeutic innovation & regulatory science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Haoyan HuGlobal Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, 46285, USA. hu_haoyan@lilly.com.
Miroslaw BrysEli Lilly and Company, Indianapolis, IN, USA.
Stephen J RubergAnalytix Thinking, LLC, Indianapolis, IN, USA.
Yongming QuGlobal Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, 46285, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ICH E9 (R1) Addendum provides a framework to define an estimand and perform sensitivity analysis. The clinical endpoint (i.e., variable, response, outcome) is one of the important estimand attributes. In our opinion, the selection of the endpoint in Alzheimer's disease requires more exploration beyond what is used currently. The change in a cognitive and functional assessment scale from baseline to a specific time point of interest is often used as a primary or key secondary endpoint in clinical trials. However, such a change from baseline to the time point of interest may not reflect the benefit of the treatment over the course of treatment duration and may be difficult to intuitively understand by patients and clinicians. For two patients with the same change from baseline, the patient with rapid disease progression in the beginning is considered to have overall worse quality of life compared to the other patient with slow disease progression in the beginning but rapid progression toward the end. We explore time-averaged measurement (TAM) as a new endpoint and propose using the relative change to quantify the treatment difference. Estimands under the ICH E9 (R1) Addendum were considered by using various strategies in handling intercurrent events and used corresponding methods for handling missing data. We illustrate the use of TAM and compare the results with other commonly used estimand endpoints (the change from baseline, the relative disease progression model, and the slope of disease progression) for different estimands and imputation methods from retrospective analyses of a historical study.

Indexed as

Alzheimer DiseaseDisease ProgressionEndpoint DeterminationHumansLongitudinal StudiesQuality of LifeAlzheimer’s DiseaseDisease ProgressionEstimandTime-averaged Measurement

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.