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Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future.

ABSTRACT: Prognostic markers help to stratify patients for treatment by identifying patients with different risks of outcome (e.g. recurrence of disease), and are important tools in the management of cancer and many other diseases. Systematic review and meta-analytical approaches to identifying the most valuable prognostic markers are needed because (sometimes conflicting) evidence relating to markers is often published across a number of studies. To investigate the practicality of this approach, an empirical investigation of a systematic review of tumour markers for neuroblastoma was performed; 260 studies of prognostic markers were identified, which considered 130 different markers. The reporting of these studies was often inadequate, in terms of both statistical analysis and presentation, and there was considerable heterogeneity for many important clinical/statistical factors. These problems restricted both the extraction of data and the meta-analysis of results from the primary studies, limiting feasibility of the evidence-based approach.Guidelines for reporting the results of primary prognostic marker studies in cancer, and other diseases, are given in order to facilitate both the interpretation of individual studies and the undertaking of systematic reviews, meta-analysis and, ultimately, evidence-based practice. General availability of full individual patient data is a necessary step forward and would overcome the majority of problems encountered, including poorly reported summary statistics and variability in cutoff level, outcome assessed and adjustment factors used. It would also limit the problem of reporting bias, although publication bias will remain a concern until studies are prospectively registered. Such changes in practice would help important evidence-based reviews to be conducted in order to establish the most appropriate prognostic markers for clinical use, which should ultimately improve patient care.

SUBMITTER: Riley RD

PROVIDER: S-EPMC2747576 | biostudies-other | 2003 Apr

REPOSITORIES: biostudies-other

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Project description:BackgroundThe reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements.ObjectiveWe aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies.MethodsWe conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items.ResultsIn total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting.ConclusionsOur findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.

Project description:ObjectiveClinical guidelines are designed to optimise patient care and provide efficient approaches for therapy. Epilepsy is a chronic brain disorder that continues to experience a considerable treatment gap due to non-standard recommendations. We assessed the reporting quality of clinical practice guidelines on epilepsy over the past 5 years to generate a reporting specification for this study.SettingSeven databases were searched in May 2018 focusing on the period from 2013 to 2018. These included Medline, EMBASE, PubMed, Cumulative Index to Nursing and Allied Health Literature, China National Knowledge Infrastructure, Wanfang and Chinese Science and Technology Journal Database (VIP). Reporting quality of epilepsy guidelines was assessed by two independent authors using the Reporting Items for practice Guidelines in HealThcare (RIGHT) approach. Spearman's correlation was used to assess inter-rater reliability.ParticipantsParticipants with epilepsy or seizure, not limited by age, gender, course of disease or cause of epilepsy, were included.InterventionsThere were no limitations with regard to intervention.Primary and secondary outcome measuresThe outcome was the ability of the RIGHT tool to measure reporting quality.ResultsTwelve relevant guidelines were included in this study. The reporting quality was not high in any of the included guidelines. The highest reporting quality included a 'yes' proportion of 77.1%, whereas the worst included a corresponding proportion of 37.1%. Overall evaluation results showed that 16.7% of the included guidelines were of high quality, 75% were of medium quality and 8.3% were of low quality. The correlation between the two estimators was credible (ρ>0.7).ConclusionsAppraisal of these guidelines using the RIGHT tool revealed that the quality of reporting varied among guidelines. Items that exhibited low quality in most included guidelines were healthcare questions, rationale/explanation for recommendations, quality assurance, funding source(s) and role(s) of the funder, and limitations of the guideline. Thus, these aspects should receive greater attention in future guideline reporting.

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Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future.

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