Evidence map›Paper›PMID 34772754›Full record

ArticleBMJ open2021

Current trends in the application of causal inference methods to pooled longitudinal non-randomised data: a protocol for a methodological systematic review.

Edmund Yeboah, Nicole Sibilla Mauer, Heather Hufstedler, Sinclair Carr, Ellicott C Matthay, Lauren Maxwell, Sabahat Rahman, Thomas Debray, Valentijn M T de Jong, Harlan Campbell and 3 more

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Exploring human biology with N-of-1 clinical trials.Cambridge prisms. Precision medicine · 2023
    Article
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

13 authors.

Edmund YeboahHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
Nicole Sibilla MauerHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.ORCID 0000-0002-0465-2300
Heather HufstedlerHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany heather.hufstedler@uni-heidelberg.de.
Sinclair CarrHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
Ellicott C MatthayCenter for Health and Community, University of California San Francisco, San Francisco, California, USA.
Lauren MaxwellHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
Sabahat RahmanUniversity of Massachusetts Medical School, University of Massachusetts, Worchester, Massachusetts, USA.
Thomas DebrayJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Valentijn M T de JongJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
Harlan CampbellDepartment of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.
Paul GustafsonDepartment of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0002-2375-5006
Thomas JänischHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
Till BärnighausenHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCausal methods have been adopted and adapted across health disciplines, particularly for the analysis of single studies. However, the sample sizes necessary to best inform decision-making are often not attainable with single studies, making pooled individual-level data analysis invaluable for public health efforts. Researchers commonly implement causal methods prevailing in their home disciplines, and how these are selected, evaluated, implemented and reported may vary widely. To our knowledge, no article has yet evaluated trends in the implementation and reporting of causal methods in studies leveraging individual-level data pooled from several studies. We undertake this review to uncover patterns in the implementation and reporting of causal methods used across disciplines in research focused on health outcomes. We will investigate variations in methods to infer causality used across disciplines, time and geography and identify gaps in reporting of methods to inform the development of reporting standards and the conversation required to effect change. METHODS AND ANALYSIS: We will search four databases (EBSCO, Embase, PubMed, Web of Science) using a search strategy developed with librarians from three universities (Heidelberg University, Harvard University, and University of California, San Francisco). The search strategy includes terms such as 'pool*', 'harmoniz*', 'cohort*', 'observational', variations on 'individual-level data'. Four reviewers will independently screen articles using Covidence and extract data from included articles. The extracted data will be analysed descriptively in tables and graphically to reveal the pattern in methods implementation and reporting. This protocol has been registered with PROSPERO (CRD42020143148). ETHICS AND DISSEMINATION: No ethical approval was required as only publicly available data were used. The results will be submitted as a manuscript to a peer-reviewed journal, disseminated in conferences if relevant, and published as part of doctoral dissertations in Global Health at the Heidelberg University Hospital.

Indexed as

Delivery of Health CareResearch DesignCausalityHumansSan FranciscoSystematic Reviews as Topicpublic healthsocial medicinestatistics & research methods

Identifiers

PMID34772754
PMCPMC8593733

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.