Dataset Information

Maternal immune activation: reporting guidelines to improve the rigor, reproducibility, and transparency of the model.

ABSTRACT: The 2017 American College of Neuropychopharmacology (ACNP) conference hosted a Study Group on 4 December 2017, Establishing best practice guidelines to improve the rigor, reproducibility, and transparency of the maternal immune activation (MIA) animal model of neurodevelopmental abnormalities. The goals of this session were to (a) evaluate the current literature and establish a consensus on best practices to be implemented in MIA studies, (b) identify remaining research gaps warranting additional data collection and lend to the development of evidence-based best practice design, and (c) inform the MIA research community of these findings. During this session, there was a detailed discussion on the importance of validating immunogen doses and standardizing the general design (e.g., species, immunogenic compound used, housing) of our MIA models both within and across laboratories. The consensus of the study group was that data does not currently exist to support specific evidence-based model selection or methodological recommendations due to lack of consistency in reporting, and that this issue extends to other inflammatory models of neurodevelopmental abnormalities. This launched a call to establish a reporting checklist focusing on validation, implementation, and transparency modeled on the ARRIVE Guidelines and CONSORT (scientific reporting guidelines for animal and clinical research, respectively). Here we provide a summary of the discussions in addition to a suggested checklist of reporting guidelines needed to improve the rigor and reproducibility of this valuable translational model, which can be adapted and applied to other animal models as well.

SUBMITTER: Kentner AC

PROVIDER: S-EPMC6300528 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Maternal immune activation: reporting guidelines to improve the rigor, reproducibility, and transparency of the model.

Kentner Amanda C AC Bilbo Staci D SD Brown Alan S AS Hsiao Elaine Y EY McAllister A Kimberley AK Meyer Urs U Pearce Brad D BD Pletnikov Mikhail V MV Yolken Robert H RH Bauman Melissa D MD

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 20180821 2

The 2017 American College of Neuropychopharmacology (ACNP) conference hosted a Study Group on 4 December 2017, Establishing best practice guidelines to improve the rigor, reproducibility, and transparency of the maternal immune activation (MIA) animal model of neurodevelopmental abnormalities. The goals of this session were to (a) evaluate the current literature and establish a consensus on best practices to be implemented in MIA studies, (b) identify remaining research gaps warranting additiona ...[more]

PMID: 30188509

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Project description:BackgroundImproving rigor and transparency measures should lead to improvements in reproducibility across the scientific literature; however, the assessment of measures of transparency tends to be very difficult if performed manually.ObjectiveThis study addresses the enhancement of the Rigor and Transparency Index (RTI, version 2.0), which attempts to automatically assess the rigor and transparency of journals, institutions, and countries using manuscripts scored on criteria found in reproducibility guidelines (eg, Materials Design, Analysis, and Reporting checklist criteria).MethodsThe RTI tracks 27 entity types using natural language processing techniques such as Bidirectional Long Short-term Memory Conditional Random Field-based models and regular expressions; this allowed us to assess over 2 million papers accessed through PubMed Central.ResultsBetween 1997 and 2020 (where data were readily available in our data set), rigor and transparency measures showed general improvement (RTI 2.29 to 4.13), suggesting that authors are taking the need for improved reporting seriously. The top-scoring journals in 2020 were the Journal of Neurochemistry (6.23), British Journal of Pharmacology (6.07), and Nature Neuroscience (5.93). We extracted the institution and country of origin from the author affiliations to expand our analysis beyond journals. Among institutions publishing >1000 papers in 2020 (in the PubMed Central open access set), Capital Medical University (4.75), Yonsei University (4.58), and University of Copenhagen (4.53) were the top performers in terms of RTI. In country-level performance, we found that Ethiopia and Norway consistently topped the RTI charts of countries with 100 or more papers per year. In addition, we tested our assumption that the RTI may serve as a reliable proxy for scientific replicability (ie, a high RTI represents papers containing sufficient information for replication efforts). Using work by the Reproducibility Project: Cancer Biology, we determined that replication papers (RTI 7.61, SD 0.78) scored significantly higher (P<.001) than the original papers (RTI 3.39, SD 1.12), which according to the project required additional information from authors to begin replication efforts.ConclusionsThese results align with our view that RTI may serve as a reliable proxy for scientific replicability. Unfortunately, RTI measures for journals, institutions, and countries fall short of the replicated paper average. If we consider the RTI of these replication studies as a target for future manuscripts, more work will be needed to ensure that the average manuscript contains sufficient information for replication attempts.

Project description:BackgroundFactors contributing to the lack of understanding of research studies include poor reporting practices, such as selective reporting of statistically significant findings or insufficient methodological details. Systematic reviews have shown that prognostic factor studies continue to be poorly reported, even for important aspects, such as the effective sample size. The REMARK reporting guidelines support researchers in reporting key aspects of tumor marker prognostic studies. The REMARK profile was proposed to augment these guidelines to aid in structured reporting with an emphasis on including all aspects of analyses conducted.MethodsA systematic search of prognostic factor studies was conducted, and fifteen studies published in 2015 were selected, three from each of five oncology journals. A paper was eligible for selection if it included survival outcomes and multivariable models were used in the statistical analyses. For each study, we summarized the key information in a REMARK profile consisting of details about the patient population with available variables and follow-up data, and a list of all analyses conducted.ResultsStructured profiles allow an easy assessment if reporting of a study only has weaknesses or if it is poor because many relevant details are missing. Studies had incomplete reporting of exclusion of patients, missing information about the number of events, or lacked details about statistical analyses, e.g., subgroup analyses in small populations without any information about the number of events. Profiles exhibit severe weaknesses in the reporting of more than 50% of the studies. The quality of analyses was not assessed, but some profiles exhibit several deficits at a glance.ConclusionsA substantial part of prognostic factor studies is poorly reported and analyzed, with severe consequences for related systematic reviews and meta-analyses. We consider inadequate reporting of single studies as one of the most important reasons that the clinical relevance of most markers is still unclear after years of research and dozens of publications. We conclude that structured reporting is an important step to improve the quality of prognostic marker research and discuss its role in the context of selective reporting, meta-analysis, study registration, predefined statistical analysis plans, and improvement of marker research.

Dataset Information

Maternal immune activation: reporting guidelines to improve the rigor, reproducibility, and transparency of the model.

Publications

Maternal immune activation: reporting guidelines to improve the rigor, reproducibility, and transparency of the model.

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