How Do Unobserved Confounding Mediators and Measurement Error Impact Estimated Mediation Effects and Corresponding Statistical Inferences? Introducing the R Package ConMed for Sensitivity Analysis

Autor/inn/en	Lin, Qinyun; Nuttall, Amy K.; Zhang, Qian; Frank, Kenneth A.
Titel	How Do Unobserved Confounding Mediators and Measurement Error Impact Estimated Mediation Effects and Corresponding Statistical Inferences? Introducing the R Package ConMed for Sensitivity Analysis
Quelle	28 (2023) 2, S.339-358 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lin, Qinyun) ORCID (Nuttall, Amy K.) ORCID (Zhang, Qian) ORCID (Frank, Kenneth A.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Causal Models; Mediation Theory; Error of Measurement; Statistical Inference; Statistical Analysis; Statistical Bias; Robustness (Statistics); Psychological Studies; Research Tools; Research Design + Suchen Sie Ihr Suchwort? Kausalanalyse; Mediationsverfahren; Messfehler; Inferential statistics; Schließende Statistik; Statistische Analyse; Widerstandsfähigkeit; Forschungsmittel; Forschungsdesign
Abstract	Empirical studies often demonstrate multiple causal mechanisms potentially involving simultaneous or causally related mediators. However, researchers often use simple mediation models to understand the processes because they do not or cannot measure other theoretically relevant mediators. In such cases, another potentially relevant but unobserved mediator potentially confounds the observed mediator, thereby biasing the estimated direct and indirect effects associated with the observed mediator and threatening corresponding inferences. Additionally, researchers may not know the extent to which their measures are reliable, and accordingly, measurement error may bias estimated effects and mislead statistical inferences. Given these threats, we explore how the omission of an unobserved mediator and/or using variables with measurement error biases estimates and affects inferences associated with the observed mediator. Then, building off Frank's impact threshold for a confounding variable (ITCV), we propose a correlation-based sensitivity analysis. Lastly, we provide an R package ConMed to assess the robustness of mediation inferences given the omission of an unobserved, confounding mediator and/or measurement error. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01