Evidence map›Paper›PMID 32242973›Full record

ReviewStatistics in medicine2020

Matching with time-dependent treatments: A review and look forward.

Laine E Thomas, Siyun Yang, Daniel Wojdyla, Douglas E Schaubel

Open access · hybridAbstract readReview
In one paragraph

Review in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
5.3field-weighted citation impact, top 3% of its field
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

29 citing papers in PubMed, 55 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Laine E ThomasDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.ORCID 0000-0002-5340-8742
Siyun YangDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.ORCID 0000-0003-2895-532X
Daniel WojdylaDuke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA.
Douglas E SchaubelDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-9792-4474
Duke University · USUniversity of Pennsylvania · US

Funding

Survival Analysis Methods for Organ Failure DataR01DK070869 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI DOUGLAS EARL SCHAUBEL · 2005 to 2026
$4.7M
Matching Methods for Comparative Effectiveness Studies of Longitudinal TreatmentsR03HS024310 · AHRQ · DUKE UNIVERSITY · PI THOMAS, LAINE ELLIOTT · 2015 to 2016
$100k
AHRQ HHS R03 HS024310NHLBI NIH HHS HHSN268201500001CNHLBI NIH HHS HHSN268201500001INIDDK NIH HHS R01 DK070869
6 · The paper itself

Abstract

Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data.

Indexed as

Research DesignBiasCohort StudiesCross-Sectional StudiesHumansPropensity Scorelongitudinal matchingnew-user designreal-world evidencetime-dependent confoundingtime-varying treatment

Identifiers

PMID32242973
PMCPMC7384144
OpenAlexW3014940504

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.