Abstract
Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framework to address missing continuous outcome data in a network of interventions and gain knowledge about the missingness process in different trials and interventions. We extend the hierarchical network meta-analysis model for one aggregate continuous outcome to incorporate a missingness parameter that measures the departure from the missing at random assumption. We consider various effect size estimates for continuous data, and two informative missingness parameters, the informative missingness difference of means and the informative missingness ratio of means. We incorporate our prior belief about the missingness parameters while allowing for several possibilities of prior structures to account for the fact that the missingness process may differ in the network. The method is exemplified in two networks from published reviews comprising a different amount of missing continuous outcome data.
Highlights
Binary outcomes have drawn methodologically more attention for being the most prevalent in systematic reviews[1,2] and rather straightforward to handle.[3]
Ratio of means For the ratio of means (RoM), the link function is the following hik 1⁄4 uiedi;k1Ifk61⁄41g where di;k1 is the RoM in the logarithmic scale in trial i. This effect measure is less prevalent in systematic reviews as compared to mean difference (MD) and standardised mean difference (SMD);[2] we have considered it for completeness
The analysis plan should comprise the description of the one-stage model with respect to the proper assumptions and prior structure of the missingness parameter
Summary
Binary outcomes have drawn methodologically more attention for being the most prevalent in systematic reviews[1,2] and rather straightforward to handle.[3]. Continuous outcomes are prone to missing outcome data (MOD). In a systematic review on respiratory rehabilitation in chronic obstructive pulmonary disease, Ebrahim et al.[4] observed a MOD rate ranging from 0% to 38% across 31 included trials. In a collection of 190 Cochrane systematic reviews published between 2009 and 2012 in three mental health Cochrane Groups, 27 out of 140 selected meta-analyses considered a continuous primary outcome; of those, 14 meta-analyses reported the total number of MOD in each arm of every trial.[5] In another collection of systematic reviews with at least three interventions published between 2009 and 2017, 92 out of 387 systematic reviews investigated a continuous primary outcome;[6] of these, only five reported the total number of MOD in each arm of every included trial.
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