Huling, Jared D.

Abstract Recent work in causally interpretable meta-analysis (CIMA) has bridged the gap between traditional meta-analysis and causal inference. Whereas traditional meta-analysis results generally do not apply to any well-defined population, CIMA approaches specify a target population to which meta-analytic treatment effect estimates are transported. Although theoretically attractive, these approaches currently have some practical limitations. Most assume that all studies in the meta-analysis have individual participant data (IPD), which is rare in practice because most trials share only aggregate data. We propose a method to perform CIMA using a combination of aggregate data and IPD. This method borrows information from studies with IPD to augment the aggregate data and create aggregate-matched synthetic IPD (AMSIPD), which can be used readily in the existing CIMA framework. By allowing use of both aggregate data and IPD, the method opens CIMA to more applications and can avoid biases arising from using only studies with IPD. We present a case study and simulations showing the AMSIPD approach is promising and merits further investigation as an advancement of CIMA.

AbstractMeta‐analysis is commonly used to combine results from multiple clinical trials, but traditional meta‐analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently‐introduced causally interpretable meta‐analysis methods and apply their treatment effect estimators to two individual‐participant data sets. These estimators transport estimated treatment effects from studies in the meta‐analysis to a specified target population using the individuals' potentially effect‐modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated‐data meta‐analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta‐analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.

AbstractObjectivesIn this study we aimed to describe and compare groups formed by a rules‐based algorithm to prospectively identify clients at risk of poor outcomes in order to guide tailored public health nursing (PHN) intervention approaches.DesignData‐driven methods using standardized Omaha System PHN documentation.SampleClients ages 13–40 who received PHN home visiting services for both the Caretaking/parenting and Mental health problems (N = 4109).MeasurementWe applied a theory‐based algorithm consisting of six rules using existing Omaha System data. We examined the groups formed by the algorithm using standard descriptive, inferential statistics, and Latent Class Analysis.ResultsClients (N = 4109) were 25.1 (SD = 5.9) years old and had an average of 7.3 (SD = 3.2) problems, 250 (SD = 319) total interventions, and 32 (SD = 44) Mental health interventions. Overall outcomes improved after PHN interventions (p < .001 for all) and having more Mental health signs/symptoms was negatively associated with outcome scores (p < .001 for all).ConclusionsThis algorithm may be helpful in identifying high‐risk clients during a baseline assessment who may benefit from more intensive mental health interventions. Findings show there is value using the Omaha System for PHN documentation and algorithm clinical decision support development. Future research should focus on algorithm implementation in PHN clinical practice.