Current trends in the application of causal inference methods to pooled longitudinal observational infectious disease studies

• Verfasst von: Hufstedler, Heather [VerfasserIn]
• Verfasst von: Matthay, Ellicott C. [VerfasserIn]
• Verfasst von: Rahman, Sabahat [VerfasserIn]
• Verfasst von: Jong, Valentijn M. T. de [VerfasserIn]
• Verfasst von: Campbell, Harlan [VerfasserIn]
• Verfasst von: Gustafson, Paul [VerfasserIn]
• Verfasst von: Debray, Thomas [VerfasserIn]
• Verfasst von: Jänisch, Thomas [VerfasserIn]
• Verfasst von: Maxwell, Lauren [VerfasserIn]
• Verfasst von: Bärnighausen, Till [VerfasserIn]
• Titel: Current trends in the application of causal inference methods to pooled longitudinal observational infectious disease studies
• Titelzusatz: a protocol for a methodological systematic review
• Verf.angabe: Heather Hufstedler, Ellicott C. Matthay, Sabahat Rahman, Valentijn M.T. de Jong, Harlan Campbell, Paul Gustafson, Thomas Debray, Thomas Jaenisch, Lauren Maxwell, Till Bärnighausen
• E-Jahr: 2021
• Jahr: April 29, 2021
• Umfang: 7 S.
• Fussnoten: Gesehen am 21.09.2021
• Titel Quelle: Enthalten in: PLOS ONE
• Ort Quelle: San Francisco, California, US : PLOS, 2006
• Jahr Quelle: 2021
• Band/Heft Quelle: 16(2021), 4, Artikel-ID e0250778, Seite 1-7
• ISSN Quelle: 1932-6203
• DOI: doi:10.1371/journal.pone.0250778
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Database searching
• Sach-SW: Infectious diseases
• Sach-SW: Observational studies
• Sach-SW: Political aspects of health
• Sach-SW: Public and occupational health
• Sach-SW: Randomized controlled trials
• Sach-SW: Systematic reviews
• Sach-SW: Virus testing
• K10plus-PPN: 1771299770

Abstract

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).