Abstract
Through exploring specific conditions (diabetes, heart failure, related vascular/metabolic diagnoses) and their multimorbidities, I develop a more thorough means to adjust confounders of clinical targets within main or interactive contexts in epidemiological panel studies. Regression-based multiple indicators-multiple causes (MIMIC) models combine multiple or moderated regression and confirmatory factor analysis. In a novel specification, each of twenty depressive symptoms is both a “formative” (causal) indicator and a “reflective” (effect) indicator of a latent trait (Depression). Although both indicators provide identical information (under different variable names), formative indicators provide “exogenous” information (outside the model) to estimate, within groups or subgroups, “endogenous” effects (recovered by the model) from the latent trait and its reflective indicators. Formative indicators within the multiple regressions constitute comprehensive proxies for unspecified confounders by completely mediating all unspecified confounder effects on the endogenous latent trait and its reflective indicators, the latter estimated through confirmatory factor analysis. Findings of symptom clusters of Depression in these specific conditions, and in subgroups that capture their synergies, corroborate parallel MIMIC models with instrumental variables that specify several known confounders, but suggest some confounding biases remain. All multimorbidities involve synergy from co-occurring diabetes and heart failure. There may be opportunities to target screening and optimize metformin treatment for these co-occurring conditions. This strategy avoids the need to specify all confounders, which may not be possible or verifiable.
Highlights
In order to specify multiple indicators-multiple causes (MIMIC) models that test a panel of metabolites, biomarkers, or symptoms, this study will compare two competing alternatives I derived
Analyses based on the formative indicators approach developed in the current article. When these latter MIMIC analyses include formative indicators for all twenty CES-D items of depression, this comprehensive adjustment for unspecified confounders parallels the comprehensive adjustment for specified confounders in the explanatory MIMIC analyses of Table 1
In contrast to the covariance-based MIMIC model, which depends on the analysis of a covariance matrix to generate a unidimensional latent trait, the regression-based MIMIC model relies on the availability of the actual responses on each predictor (x variable) across observations
Summary
CFA assesses the effect of the disease group or subgroup on each of the measurement items while simultaneously controlling for the influence of the latent factor on each measurement item. This allows CFA to adjust for measurement error and unreliability. PCA measurement loadings may be inflated due to the lack of similar adjustments. Evidence from Monte Carlo analyses [3,4] suggests measurement loadings for the same symptoms tend to be higher (inflated) in PCA compared to CFA (see Appendix A, note 1)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
