Mapping and systematic appraisal of umbrella reviews in epidemiological research: a protocol for a meta-epidemiological study.

Introduction: Umbrella review is one of the terms used to describe an overview of systematic reviews. During the last years, a rapid increase in the number of umbrella reviews on epidemiological studies has been observed, but there is no systematic assessment of their methodological and reporting characteristics. Our study aims to fill this gap by performing a systematic mapping of umbrella reviews in epidemiological research.

Conducting umbrella reviews.

In this article, Lazaros Belbasis and colleagues explain the rationale for umbrella reviews and the key steps involved in conducting an umbrella review, using a working example.

Reproducibility of prediction models in health services research.

The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research practices for health services research is important for evidence-based decision making.

Multivariable prediction models for health care spending using machine learning: a protocol of a systematic review.

Background: With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies.

Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis.

Introduction: The individual prognostic factors for coronavirus disease 2019 (COVID-19) are unclear. For this reason, we aimed to present a state-of-the-art systematic review and meta-analysis on the prognostic factors for adverse outcomes in COVID-19 patients.

Methods: We systematically reviewed PubMed from 1 January 2020 to 26 July 2020 to identify non-overlapping studies examining the association of any prognostic factor with any adverse outcome in patients with COVID-19.