Evidence map›Paper›PMID 39656740›Full record

ArticleBiometrics2024

A generalized logrank-type test for comparison of treatment regimes in sequential multiple assignment randomized trials.

Anastasios A Tsiatis, Marie Davidian

Abstract readComparative Study
In one paragraph

Article in Biometrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Anastasios A TsiatisDepartment of Statistics, North Carolina State University, Raleigh, NC 27695-8203, United States.
Marie DavidianDepartment of Statistics, North Carolina State University, Raleigh, NC 27695-8203, United States.ORCID 0000-0002-9202-6341

Funding

Next-generation SMARTs for Discovery and Evaluation of Sequential Cancer Therapeutic StrategiesR01CA280970 · NCI · DUKE UNIVERSITY · PI Eric Benjamin Laber · 2024 to 2026
$1.1M
NCI NIH HHS R01 CA280970NIH HHS R01CA280970
6 · The paper itself

Abstract

The sequential multiple assignment randomized trial (SMART) is the ideal study design for the evaluation of multistage treatment regimes, which comprise sequential decision rules that recommend treatments for a patient at each of a series of decision points based on their evolving characteristics. A common goal is to compare the set of so-called embedded regimes represented in the design on the basis of a primary outcome of interest. In the study of chronic diseases and disorders, this outcome is often a time to an event, and a goal is to compare the distributions of the time-to-event outcome associated with each regime in the set. We present a general statistical framework in which we develop a logrank-type test for comparison of the survival distributions associated with regimes within a specified set based on the data from a SMART with an arbitrary number of stages that allows incorporation of covariate information to enhance efficiency and can also be used with data from an observational study. The framework provides clarification of the assumptions required to yield a principled test procedure, and the proposed test subsumes or offers an improved alternative to existing methods. We demonstrate performance of the methods in a suite of simulation studies. The methods are applied to a SMART in patients with acute promyelocytic leukemia.

Indexed as

Computer SimulationRandomized Controlled Trials as TopicBiometryData Interpretation, StatisticalHumansModels, StatisticalResearch DesignSurvival AnalysisTreatment Outcomeaugmented inverse probability weightingdynamic treatment regimeestimating functionpotential outcomes

Identifiers

PMID39656740
PMCPMC11636965

What OpenQuestion holds

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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.