Current trends in the application of causal inference methods to pooled longitudinal observational infectious disease studies—A protocol for a methodological systematic review

Autor: Thomas Jaenisch, Thomas P. A. Debray, Sabahat Rahman, Till Bärnighausen, Lauren Maxwell, Harlan Campbell, Paul Gustafson, Ellicott C. Matthay, Heather Hufstedler, Valentijn M.T. de Jong
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Drug Research and Development
Systematic Reviews
Science
Pooling
MEDLINE
CINAHL
Research and Analysis Methods
01 natural sciences
Communicable Diseases
010104 statistics & probability
03 medical and health sciences
Database and Informatics Methods
0302 clinical medicine
Medical Conditions
Meta-Analysis as Topic
Diagnostic Medicine
Registered Report Protocol
Medicine and Health Sciences
Humans
Clinical Trials
Public and Occupational Health
030212 general & internal medicine
Longitudinal Studies
0101 mathematics
Database Searching
Virus Testing
Protocol (science)
Pharmacology
Multidisciplinary
Actuarial science
COVID-19
Research Assessment
Randomized Controlled Trials
3. Good health
Causality
Systematic review
Infectious Diseases
Research Design
Meta-analysis
Causal inference
Observational Studies
Medicine
Observational study
Clinical Medicine
Psychology
Systematic Reviews as Topic
Zdroj: PLoS ONE
PLoS ONE, Vol 16, Iss 4, p e0250778 (2021)
ISSN: 1932-6203
4202-0204
Popis: Introduction Pooling (or combining) and analysing observational, longitudinal data at the individual level facilitates inference through increased sample sizes, allowing for joint estimation of study- and individual-level exposure variables, and better enabling the assessment of rare exposures and diseases. Empirical studies leveraging such methods when randomization is unethical or impractical have grown in the health sciences in recent years. The adoption of so-called “causal” methods to account for both/either measured and/or unmeasured confounders is an important addition to the methodological toolkit for understanding the distribution, progression, and consequences of infectious diseases (IDs) and interventions on IDs. In the face of the Covid-19 pandemic and in the absence of systematic randomization of exposures or interventions, the value of these methods is even more apparent. Yet to our knowledge, no studies have assessed how causal methods involving pooling individual-level, observational, longitudinal data are being applied in ID-related research. In this systematic review, we assess how these methods are used and reported in ID-related research over the last 10 years. Findings will facilitate evaluation of trends of causal methods for ID research and lead to concrete recommendations for how to apply these methods where gaps in methodological rigor are identified. Methods and analysis We will apply MeSH and text terms to identify relevant studies from EBSCO (Academic Search Complete, Business Source Premier, CINAHL, EconLit with Full Text, PsychINFO), EMBASE, PubMed, and Web of Science. Eligible studies are those that apply causal methods to account for confounding when assessing the effects of an intervention or exposure on an ID-related outcome using pooled, individual-level data from 2 or more longitudinal, observational studies. Titles, abstracts, and full-text articles, will be independently screened by two reviewers using Covidence software. Discrepancies will be resolved by a third reviewer. This systematic review protocol has been registered with PROSPERO (CRD42020204104).
Databáze: OpenAIRE
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