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Characterization of missing data patterns and mechanisms in longitudinal composite outcome trial in rheumatoid arthritis

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Scott, David L 
Prevost, Andrew Toby 


jats:secjats:titleBackground:</jats:title>jats:p Composite measures, like the Disease Activity Score for 28 joints (DAS28), are key primary outcomes in rheumatoid arthritis (RA) trials. DAS28 combines four different components in a continuous measure. When one or more of these components are missing the overall composite score is also missing at intermediate or trial endpoint assessments. </jats:p></jats:sec>jats:secjats:titleObjectives:</jats:title>jats:p This study examined missing data patterns and mechanisms in a longitudinal RA trial to evaluate how best to handle missingness when analysing composite outcomes. </jats:p></jats:sec>jats:secjats:titleDesign:</jats:title>jats:p The Tumour-Necrosis-Factor Inhibitors against Combination Intensive Therapy (TACIT) trial was an open label, pragmatic randomized multicentre two arm non-inferiority study. Patients were followed up for 12 months, with monthly measurement of the composite outcome and its components. Active RA patients were randomized to conventional disease modifying drugs (cDMARDs) or Tumour Necrosis Factor-α inhibitors (TNFis). </jats:p></jats:sec>jats:secjats:titleMethods:</jats:title>jats:p The TACIT trial was used to explore the extent of missing data in the composite outcome, DAS28. Patterns of missing data in components and the composite outcome were examined graphically. Longitudinal multivariable logistic regression analysis assessed missing data mechanisms during follow-up. </jats:p></jats:sec>jats:secjats:titleResults:</jats:title>jats:p Two hundred and five patients were randomized: at 12 months 59/205 (29%) had unobserved composite outcome and 146/205 (71%) had an observed DAS28 outcome; however, 34/146 had one or more intermediate assessments missing. We observed mixed missing data patterns, especially for the missing composite outcome due to one component missing rather than patient not attending thier visit. Age and gender predicted missingness components, providing strong evidence the missing observations were unlikely to be Missing Completely at Random (MCAR). </jats:p></jats:sec>jats:secjats:titleConclusion:</jats:title>jats:p Researchers should undertake detailed evaluations of missing data patterns and mechanisms at the final and intermediate time points, whether or not the outcome variable is a composite outcome. In addition, the impact on treatment estimates in patients who only provide data at milestone assessments need to be assessed. </jats:p></jats:sec>jats:secjats:titleTrial Registration ISRCTN Number:</jats:title>jats:p 37438295 </jats:p></jats:sec>


Peer reviewed: True


Original Research, disease activity, imputation, missing completely at random, missing data, rheumatoid arthritis

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Therapeutic Advances in Musculoskeletal Disease

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SAGE Publications
national institutes of health (RP-PG-0610-10066)