Abstract

In evaluation of neuropathy, change in neurologic function over time has been assessed using validated scores such as the Neuropathy Impairment Score (NIS), the NIS plus 7 (NIS + 7) and the modified NIS + 7 (mNIS + 7). These scores can be further categorized into sub-scores (i.e., domains). Each domain is composed of multiple questionnaires (i.e., items). Scale scores that sum or average a set of questionnaire items are used to address their substantive questions. Missing data could happen at not only the item level but also at the scale level if a visit is completely missed. In addition, same types of missingness often occur in the patient-reported outcomes. Multiple imputations at both the item and the scale levels have been implemented to generate complete data for making inference. Here, we propose a fully Bayesian approach, hierarchical Bayes, to handle the item-level missing data, and to make inference on each domain and across all the domains. In addition to testing the performance of the proposed approach using simulation studies, the proposed approach is applied to a publically available data.

Authors and Affiliations