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

Pooling CINAHL Mendelian Randomization Clinical study design EconLit Research Design
DOI: 10.1371/journal.pone.0250778 Publication Date: 2021-04-29T17:40:50Z
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- individual-level exposure variables, better enabling assessment rare exposures diseases. Empirical studies leveraging such methods when randomization is unethical or impractical have grown in health sciences recent years. The adoption so-called “causal” to account both/either measured and/or unmeasured confounders an important addition methodological toolkit understanding distribution, progression, consequences infectious diseases (IDs) interventions on IDs. In face Covid-19 pandemic absence systematic interventions, value these even more apparent. Yet our knowledge, no assessed how causal involving pooling individual-level, are being applied ID-related research. this review, we assess used reported research over last 10 Findings will facilitate evaluation trends ID lead concrete recommendations apply where gaps rigor identified. Methods analysis We MeSH text terms identify relevant from EBSCO (Academic Search Complete, Business Source Premier, CINAHL, EconLit with Full Text, PsychINFO), EMBASE, PubMed, Web Science. Eligible those that confounding assessing effects intervention outcome using pooled, 2 longitudinal, observational studies. Titles, abstracts, full-text articles, be independently screened by two reviewers Covidence software. Discrepancies resolved a third reviewer. This review protocol has been registered PROSPERO (CRD42020204104).
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