Analysis Ofmultivariate Hierarchical data With Missingness- anapplication to in-patient Paediatric Pneumonia Care
Abstract
Routine health data are used to monitor quality of care and to inform interventions
to improve patient care. However, statistical analysis of such data presents
several challenges related to handling missing data and multiple responses in the
presence of complex data structures.
In this study we sought to: i) Analyze multilevel clustered data accounting for covariate
missingness. ii) Explore appropriate strategies for handling missing data
when the outcome is a composite of partially observed components. iii) Examine
sensitivity of results to departures from the commonly assumed missing at random
(MAR) mechanism. iv) Simultaneously estimate joint covariate effects and
association amongst multiple correlated outcomes.
We analysed routine data collected during a cluster randomized trial in 12 Kenyan
hospitals between March and November 2016. There were 2127 children admitted
by 378 clinicians ascross the study sites. The outcomes of interest were 12
pneumonia quality of care indicators spanning assessment, diagnosis and classification
and treatment domains of care. For the first three objectives, we constructed
Paediatric Admission Quality of Care (PAQC) score, an ordinal composite
outcome using 12 pneumonia care indicators. Covariates of interest included :
trial arm and follow-up time, hospital, clinician and patient-level variables. Missing
data occurred in patient and clinician level variables. Missing data in covariates
were imputed using latent normal joint modelling approach assuming MAR
mechanism. Random-effects and marginal models were the substantive models
of interest. To explore appropriate strategies of handling missing PAQC score
subcomponents, we conducted a simulation study. Multiple imputation (MI) at
subcomponent level versus the conventional method where missing PAQC score
subcomponents were scored with value 0. We assessed departure form MAR
assumption within pattern mixture models. Elicited experts’ opinions were incorporated
into the imputation models in the form of prior distributions and
delta adjustment parameters to create missing not at random imputed values. In
the fourth objective, we analyzed 9 binary pneumonia care indicators under the
correlated random effects joint model, by applying pairwise fitting and pseudolikelihood
methods before and after MI of missing covariates.
From results, trial intervention was associated with higher uptake of the paediatric
pneumonia guidelines during the trial period. Parameter estimates were
precise after MI of covariates compared to complete case analysis. In a range
of simulation scenarios, multiple imputation of missing PAQC score elements at
item level produced minimally biased estimates compared to the conventional
method. Our inferences were insensitive to departures from MAR assumption
using either sensitivity analysis approach. Lastly, there was a significant joint interaction
effect between intervention arm and follow-up time on pneumonia care
indicators. The strength and direction of association amongst outcomes varied
within and across domains care.
This study demonstrates the practical utility of advanced biostatistical analyses
methods with an aim to promote their use while answering substantive health
research questions. Uptake of such methods can improve analysis and reportiv
ing of health data used to inform policies and in the long run enhance optimal
utilization of limited resources while promoting better patients’ outcomes.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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