Evidence map›Paper›PMID 35725267›Full record

ArticleBMJ open2022

Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine.

Jie Xu, Yi Guo, Fei Wang, Hua Xu, Robert Lucero, Jiang Bian, Mattia Prosperi

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors at 5 institutions in 1 country.

Jie XuDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.ORCID 0000-0001-5291-5198
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.ORCID 0000-0003-0587-4105
Fei WangDepartment of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York City, New York, USA.
Hua XuSchool of Biomedical Informatics, University of Texas Health Science at Houston, Houston, Texas, USA.
Robert LuceroSchool of Nursing, University of California - Los Angeles, Los Angeles, California, USA.ORCID 0000-0002-8089-466X
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.ORCID 0000-0002-2238-5429
Mattia ProsperiDepartment of Epidemiology, University of Florida, Gainesville, Florida, USA m.prosperi@ufl.edu.
University of Florida Health · USCornell University · USThe University of Texas Health Science Center at Houston · USUniversity of California, Los Angeles · USUniversity of Florida · US

Funding

Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning frameworkR01AI145552 · NIAID · UNIVERSITY OF FLORIDA · PI Simone Marini, Mattia Prosperi · 2020 to 2026
$3.6M
Developing Computational Methods for Surveillance of Antimicrobial Resistant AgentsR01AI141810 · NIAID · UNIVERSITY OF FLORIDA · PI BOUCHER, CHRISTINA, PROSPERI, MATTIA · 2019 to 2023
$2.1M
Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE)R33AG062884 · NIA · UNIVERSITY OF FLORIDA · PI INGIBJARGARDOTTIR BJARNADOTTIR, RAGNHILDUR, LUCERO, ROBERT J · 2021 to 2023
$2.1M
The benefits and harms of lung cancer screening in FloridaR01CA246418 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2023
$1.7M
Using Real-world Data to Assess the Burden of Diabetes in Children and Adolescents in FloridaU18DP006512 · DP · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2024
$1.4M
Using Electronic Health Records from a Large Clinical Data Research Network to Understand Cancer Burden and Cancer Risks Among Transgender and Gender Nonconforming (TGNC) IndividualsR21CA245858 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2021
$773k
Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical TrialsR21AG068717 · NIA · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2021
$419k
Optimizing the Population Representativeness of Older Adults in Cancer TrialsR21CA253394 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2021 to 2021
$392k
ACL HHS U18DP006512NCCDPHP CDC HHS U18 DP006512NCI NIH HHS R01 CA246418NCI NIH HHS R21 CA245858NCI NIH HHS R21 CA253394NIAID NIH HHS R01 AI141810NIAID NIH HHS R01 AI145552NIA NIH HHS R21 AG068717NIA NIH HHS R33 AG062884
6 · The paper itself

Abstract

introductionWhile there are guidelines for reporting on observational studies (eg, Strengthening the Reporting of Observational Studies in Epidemiology, Reporting of Studies Conducted Using Observational Routinely Collected Health Data Statement), estimation of causal effects from both observational data and randomised experiments (eg, A Guideline for Reporting Mediation Analyses of Randomised Trials and Observational Studies, Consolidated Standards of Reporting Trials, PATH) and on prediction modelling (eg, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis), none is purposely made for deriving and validating models from observational data to predict counterfactuals for individuals on one or more possible interventions, on the basis of given (or inferred) causal structures. This paper describes methods and processes that will be used to develop a Reporting Guideline for Causal and Counterfactual Prediction Models (PRECOG). METHODS AND ANALYSIS: PRECOG will be developed following published guidance from the Enhancing the Quality and Transparency of Health Research (EQUATOR) network and will comprise five stages. Stage 1 will be meetings of a working group every other week with rotating external advisors (active until stage 5). Stage 2 will comprise a systematic review of literature on counterfactual prediction modelling for biomedical sciences (registered in Prospective Register of Systematic Reviews). In stage 3, a computer-based, real-time Delphi survey will be performed to consolidate the PRECOG checklist, involving experts in causal inference, epidemiology, statistics, machine learning, informatics and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline based on the results from the prior stages. Stage 5 will seek the peer-reviewed publication of the guideline, the scoping/systematic review and dissemination. ETHICS AND DISSEMINATION: The study will follow the principles of the Declaration of Helsinki. The study has been registered in EQUATOR and approved by the University of Florida's Institutional Review Board (#202200495). Informed consent will be obtained from the working groups and the Delphi survey participants. The dissemination of PRECOG and its products will be done through journal publications, conferences, websites and social media.

Indexed as

ChecklistResearch DesignCausalityGuidelines as TopicHumansSystematic Reviews as TopicHealth informaticsInformation technologyProtocols & guidelines

Identifiers

PMID35725267
PMCPMC9214357
OpenAlexW4283167950

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.