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Correction to: Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes

Evan Mayo-Wilson1, Susan Hutfless2,3, Tianjing Li2

  • 1Center for Clinical Trials and Evidence Synthesis, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA. emw@jhu.edu.

Alzheimer'S Research & Therapy
|February 18, 2018
PubMed
Abstract

Insights

This protocol outlines a method for integrating multiple data sources (MUDS) to enhance meta-analysis for patient-centered outcomes research. The approach aims to improve the quality and applicability of research findings.

Area of Science:

  • Health Services Research
  • Biostatistics
  • Clinical Informatics

Background:

  • Patient-centered outcomes research (PCOR) is crucial for healthcare decision-making.
  • Existing meta-analysis methods face challenges in integrating diverse data types.
  • A standardized protocol is needed to leverage multiple data sources effectively.

Purpose of the Study:

  • To present a protocol for a methodological study on integrating multiple data sources (MUDS) for meta-analysis.
  • To enhance patient-centered outcomes research (PCOR) by improving meta-analytic techniques.
  • To establish a framework for synthesizing heterogeneous data in health research.

Main Methods:

  • The study proposes a protocol for a methodological investigation.
  • It details procedures for data extraction, harmonization, and statistical analysis from varied sources.
  • The protocol emphasizes the integration of real-world data and clinical trial data.

Main Results:

  • This is a protocol paper; main results will be generated upon study completion.
  • The proposed methodology is expected to yield more robust and comprehensive meta-analytic results.
  • Anticipated outcomes include improved precision and generalizability of findings in PCOR.

Conclusions:

  • The MUDS protocol offers a novel approach to meta-analysis in PCOR.
  • Successful implementation can lead to more reliable evidence synthesis.
  • This methodological advancement supports better-informed healthcare decisions.

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