Online Program

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Thursday, January 11
Thu, Jan 11, 9:00 AM - 10:45 AM
Crystal Ballroom A
Novel Statistical Methods for Patient-Reported Outcome Measures

Investigating the Impact of Missing Data Mechanism in Patient Reported Outcome Measures: A Clinical Registry Perspective (303966)

*Olawale F Ayilara, University of Manitoba 
Eric Bohm, University of Manitoba 
Lisa M Lix, Rady Faculty of Health Sciences, University of Manitoba 
Tolulope T Sajobi, University of Calgary 
Richard Sawatzky, Trinity Western University 
Lixia Zhang, University of Manitoba 

Keywords: Missing data, registry data, joint replacement, random-effects models, pattern-mixture model, latent-variable

Clinical registries can be used to study the effects of medical interventions on a variety of outcomes, including patients’ perspectives on their own health. However, the amount of missing observations in these patient-reported outcomes (PROs) may be large, particularly in longitudinal studies due to patient drop-out. These missing data may be ignorable, but are more likely to be non-ignorable. We estimate the change in PROs from longitudinal registry data using different approaches that are based on assumptions of ignorable or non-ignorable missing data. Study data were from a clinical registry that captures > 90% of joint replacement surgeries for the province of Manitoba, Canada. Oxford Hip (OHS) and Knee (OKS) and twelve-item Short Form Survey version 2 Physical and Mental Component Summary (PCS, MCS) scores for the period from 2009-2015 were analyzed. PRO data were captured one month prior to surgery and at one year post-surgery. Change scores were estimated after adjusting for age and sex using: (a)fixed-effects model for complete cases, (b) multiple imputation model, (c) random-effects model, and (d) pattern-mixture model for non-ignorable missing data.