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Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

Abstract Background Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straight...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Rheanna M. Mainzer, Margarita Moreno-Betancur, Cattram D. Nguyen, Julie A. Simpson, John B. Carlin, Katherine J. Lee
Format: Artigo
Sprache:Inglês
Veröffentlicht: BMC 2024-09-01
Schriftenreihe:BMC Medical Research Methodology
Schlagworte:
Missing data
Causal inference
Missingness mechanism
Online-Zugang:https://doi.org/10.1186/s12874-024-02302-6
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