Pairwise joint modeling of clustered and high-dimensional outcomes with covariate missingness in pediatric pneumonia care

Abstract

Multiple outcomes reflecting different aspects of routine care are a common phenomenon in health care research. A common approach of handling such outcomes is multiple univariate analyses, an approach which does not allow for answering research questions pertaining to joint inference. In this study, we sought to study associations among nine pediatric pneumonia care outcomes spanning assessment, diagnosis and treatment domains of care, while circumventing the computational challenge posed by their clustered and high-dimensional nature and incompletely recorded covariates. We analyzed data from a cluster randomized trial conducted in 12 Kenyan hospitals. There were varying degrees of missingness in the covariates of interest, and these were multiply imputed using latent normal joint modeling. We used the pairwise joint modeling strategy to fit a correlated random effects joint model for the nine outcomes. This entailed fitting 36 bivariate generalized linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We also analyzed the nine outcomes separately before and after multiple imputation. We observed joint effects of patient-, clinician- and hospital-level factors on pneumonia care indicators before and after multiple imputation of missing covariates. In both pairwise joint modeling and separate univariate analysis methods, enhanced audit and feedback improved documentation and adherence to recommended clinical guidelines over time in six and five pneumonia care indicators, respectively. Additionally, multiple imputation improved precision of parameter estimates compared to complete case analysis. The strength and direction of association among pneumonia outcomes varied within and across the three domains of pneumonia care.

Keywords: multiple imputation; pairwise joint modeling; pediatric care; pneumonia; pseudo-likelihood.

Figure 1

Odds ratios (dots) and 95% confidence intervals (horizontal bars) under complete case analysis and after multiple imputation of missing covariates: Pairwise joint modeling of nine pneumonia care outcomes

Figure 2

Odds ratios (dots) and 95% confidence intervals (horizontal bars) under complete case analysis and after multiple imputation of missing covariates: Separate univariate analysis of nine pneumonia care outcomes

Figure 3

Results (component loadings for the first and second principal components) of a principal components analysis on correlation matrix of the random intercepts of model under complete case analysis (panel a) and after multiple imputation (panel b)