Protocol for Development of a Reporting Guideline for Causal and Counterfactual Prediction Models

[Introduction] While there are protocols for reporting on observational studies (e.g., STROBE, RECORD), estimation of causal effects from both observational data and randomized experiments (e.g., AGREMA, CONSORT), and on prediction modelling(e.g., TRIPOD), none is purposely made for assessing the ability and reliability of models to predict counterfactuals for individuals upon 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(tentative acronym: PRECOG). [Materials and Methods] PRECOG will be developed following published guidance from the EQUATOR network, and will comprise five stages. Stage 1 will be bi-weekly meetings of a working group with external advisors (active until stage 5). Stage 2 will comprise a scoping/systematic review of literature on counterfactual prediction modelling for biomedical sciences(registered in PROSPERO). In stage 3, we will perform a computer-based, real-time Delphi survey to consolidate the PRECOGchecklist, involving experts in causal inference, statistics, machine learning, prediction modelling and protocols/standards. Stage 4 will involve the write-up of the PRECOG guideline (including its checklist) based on the results from the prior stages. In stage 5, we will work on the publication of the guideline and of the scoping/systematic review as peer-reviewed, open-access papers, and on their dissemination through conferences, websites, and social media. [Conclusions] PRECOG can help researchers and policymakers to carry out and critically appraise causal and counterfactual prediction model studies. PRECOG will also be useful for designing interventions, and we anticipate further expansion of the guideline for specific areas, e.g., pharmaceutical interventions.

PRISMA-Ethics – Reporting Guideline for Systematic Reviews on Ethics Literature: development, explanations and examples

Systematic reviews (SR) are very well elaborated and established for synthesizing statistical information, for example of clinical studies, for determining whether a clinical intervention is effective. SRs are also becoming more and more popular in bioethics. However, the established approach of conducting and reporting a SR cannot be transferred to corresponding work on ethically sensible questions directly. This is because the object of investigation is not statistical information, but conceptual or normative information, e.g., ethical norms, principles, arguments or conclusions. There is some evidence that the quality of reporting of SRs on ethics literature could be improved in many regards. Although insufficient reporting is not a problem specific to bioethics, as poor study reports are also very common in SRs in e.g. medicine, authors of such SRs have the possibility to follow a reporting guideline – the well-established statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). For SRs on ethics literature, the PRISMA Statement can only be partially adopted due to the different type of information searched and analyzed.Thus, an international group of authors with years of experience in conducting and reviewing SRs on ethics literature adapted PRISMA for its application in the field of bioethics (“PRISMA-Ethics”). As methods stemming from qualitative research are often used for analysis and synthesis in SRs on ethics literature, also elements of the ENTREQ Guideline were incorporated in PRISMA-Ethics. The resulting reporting guideline has 22 items and is intended to provide authors of SRs on ethics literature with all information necessary for an adequate reporting of their SRs. It also allows readers, reviewers and journal editors critically evaluating the presented results and conclusions made. In this paper, we explain the rationale and give examples for each item. While we acknowledge heterogeneity on how to conduct a SR on ethics literature, we still maintain that there is a need for general reporting standards for improving transparency, understandability and verifiability of such SRs. We invite authors of SRs on ethics literature to test PRISMA-Ethics and to evaluate its usefulness. We hope for a critical discussion of the guideline and welcome its broad implementation.

Sustainability Reporting Guideline for Small Medium Enterprises (SMEs): Case Study from 25 SMEs in Indonesia

The sustainability reporting trend has been increasing in Indonesia for the past few years, with the government’s plan to make sustainability reporting mandatory for all companies, including Small and Medium-sized Enterprises (SMEs) that have significant impacts on national economics. So far, there are no specific guidelines for SME’s sustainability report. There are many indicators in the global sustainability reporting guidelines that are not suitable for SMEs since they are limited in the resources and knowledge they can disseminate. Therefore, this study aims to develop a sustainable reporting guideline for SMEs. This study analyzes the sustainable development reporting practices of Indonesia and other countries through literature review, interviews and surveys, in order to build a sustainable development reporting guide for SMEs. The reporting guidelines recommended in this study contain 25 indicators: 12 general information indicators, 1 economic indicator, 6 environmental indicators, and 6 social indicators. Based on a survey of 25 small and medium-sized enterprises, this guideline is suitable for preparing SMEs’ sustainability reports.

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.

What you need to know about scoping reviews

The increasing amount of evidence has caused an increasing amount of literature reviews. There are different types of reviews —systematic reviews are the best known—, and every type of review has different purposes. The scoping review is a recent model that aims to answer broad questions and identify and expose the available evidence for a broader question, using a rigorous and reproducible method. In the last two decades, researchers have discussed the most appropriate method to carry out scoping reviews, and recently the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ for scoping reviews (PRISMA-ScR) reporting guideline was published. This is the fifth article of a methodological collaborative series of narrative reviews about general topics on biostatistics and clinical epidemiology. This review aims to describe what scoping reviews are, identify their objectives, differentiate them from other types of reviews, and provide considerations on how to carry them out.