Maximum likelihood estimation of multilevel structural equation model (MLSEM) parameters is a preferred approach to probe theories involving latent variables in multilevel settings. Although maximum likelihood has many desirable properties, a major limitation is that it often fails to converge and can incur significant bias when implemented in studies with a small to moderate multilevel sample (e.g., fewer than 100 organizations with 10 or less individuals/organization). To address similar limitations in single-level SEM, literature has developed Croon’s bias-corrected factor score path analysis estimator that converges more regularly than maximum likelihood and delivers less biased parameter estimates with small to moderate sample sizes. We derive extensions to this framework for MLSEMs and probe the degree to which the estimator retains these advantages with small to moderate multilevel samples. The estimator emerges as a useful alternative or complement to maximum likelihood because it often outperforms maximum likelihood in small to moderate multilevel samples in terms of convergence, bias, error variance, and power. The proposed estimator is implemented as a function in R using lavaan and is illustrated using a multilevel mediation example.
Designs that facilitate inferences concerning both the total and indirect effects of a treatment potentially offer a more holistic description of interventions because they can complement “what works” questions with the comprehensive study of the causal connections implied by substantive theories. Mapping the sensitivity of designs to detect these effects is of critical importance because it directly governs the types of evidence researchers can bring to bear on theories of action under realistic sample sizes. In this study, we develop closed-form expressions to estimate the variance of and the power to detect causally defined indirect effects in two-level group-randomized studies examining individual-level mediators (i.e., 2-1-1 mediation). We formulate our approach within the purview of typical multilevel mediation models and anchor their interpretation in the potential outcomes framework. The results provide power analysis formulas that reduce calculations to simple functions of the primary path coefficients (e.g., treatment–mediator and mediator–outcome relationships) and common summary statistics (e.g., intraclass correlation coefficients). Probing these formulas suggests that group-randomized designs can be well powered to detect indirect effects when carefully planned. The power formulas are implemented in the PowerUp software (causalevaluation.org).
Septic Arthritis is an orthopedic emergency that requires prompt diagnosis and treatment. Staphylococcus aureus is the most common pathogen causing septic arthritis. This review seeks to describe clinical characteristics and outcomes associated with patients with methicillin-resistant (MRSA) vs. methicillin-sensitive staphylococcus aureus (MSSA) septic arthritis. The review showed that those infected with MRSA were older, had more chronic medical conditions, and higher values of inflammatory markers. MRSA septic arthritis was also associated with more complications, longer duration of antibiotics, and increased mortality. Thus, health care providers should maintain a high index of suspicion, diagnose, and treat aggressively to prevent adverse outcomes.
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