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Abstract. Structural equation modeling (SEM) is increasingly being chosen by researchers as a framework for gaining scientific insights from the quantitative analyses of data. New ideas and methods emerging from the study of causality, influences from the field of graphical modeling, and advances in statistics are expanding the rigor, capability, and even purpose of SEM. Guidelines for implementing the expanded capabilities of SEM are currently lacking. In this paper we describe new developments in SEM that we believe constitute a third-generation of the methodology. Most characteristic of this new approach is the generalization of the structural equation model as a causal graph. In this generalization, analyses are based on graph theoretic principles rather than analyses of matrices. Also, new devices such as metamodels and causal diagrams, as well as an increased emphasis on queries and probabilistic reasoning, are now included. Estimation under a graph theory framework permits the use of Bayesian or likelihood methods. The guidelines presented start from a declaration of the goals of the analysis. We then discuss how theory frames the modeling process, requirements for causal interpretation, model specification choices, selection of estimation method, model evaluation options, and use of queries, both to summarize retrospective results and for prospective analyses.The illustrative example presented involves monitoring data from wetlands on Mount Desert Island, home of Acadia National Park. Our presentation walks through the decision process involved in developing and evaluating models, as well as drawing inferences from the resulting prediction equations. In addition to evaluating hypotheses about the connections between human activities and biotic responses, we illustrate how the structural equation (SE) model can be queried to understand how interventions might take advantage of an environmental threshold to limit Typha invasions.The guidelines presented provide for an updated definition of the SEM process that subsumes the historical matrix approach under a graph-theory implementation. The implementation is also designed to permit complex specifications and to be compatible with various estimation methods. Finally, they are meant to foster the use of probabilistic reasoning in both retrospective and prospective considerations of the quantitative implications of the results.

Lung surfactant is deficient in patients with acute respiratory distress syndrome (ARDS). We performed a randomized, prospective, controlled, open-label clinical study of administration of a bovine surfactant to patients with ARDS to obtain preliminary information about its safety and efficacy. Patients received either surfactant by endotracheal instillation in addition to standard therapy or standard therapy only. Three different groups of patients receiving surfactant were studied: patients receiving up to eight doses of 50 mg phospholipids/kg, those receiving up to eight doses of 100 mg phospholipids/kg, and those receiving up to four doses of 100 mg phospholipids/kg. Outcome measures included ventilatory support parameters, arterial blood gases, organ system failures, bronchoalveolar lavage (BAL) analyses, immunologic analyses, survival, and adverse events during the 28-d study period. Fifty-nine study patients were evaluable; 43 in the surfactant group and 16 in the control group. The FI(O2) at 120 h after treatment began was significantly decreased only for patients who received up to four doses of 100 mg phospholipids/kg surfactant as compared with control patients (p = 0.011). Mortality in the same group of patients was 18.8%, as compared with 43.8% in the control group (p = 0.075). The surfactant instillation was generally well tolerated, and no safety concerns were identified. This pilot study presents preliminary evidence that surfactant might have therapeutic benefit for patients with ARDS, and provides rationale for further clinical study of this agent.

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