Characterizing 16-Week Responder Profiles Using Group-Based Trajectory Modeling in Over 4300 Clinical Trial Participants Receiving Pharmaceutical Treatment for Moderate to Severe Osteoarthritis - Schnitzer, Thomas J.; Bonfanti, Gianluca; Atkinson, Joanna; Donevan, Sean; Viktrup, Lars; Barroso, Joana; Whalen, Edward; Edwards, Roger A. (Advances in therapy, 2022)
Introduction We sought to identify and characterize distinct responder profiles among osteoarthritis (OA) subjects treated with tanezumab, nonsteroidal anti-inflammatory drugs (NSAIDs), or placebo. Methods Subject-level data were derived from three randomized, double-blind, placebo- or NSAID-controlled trials of tanezumab in subjects with moderate-to-severe OA. Subjects received subcutaneous tanezumab (2.5 mg, n = 1527; 5 mg, n = 1279) every 8 weeks, oral NSAIDs ( n = 994) daily, or placebo ( n = 513). Group-based trajectory modeling (GBTM, an application of finite mixture statistical modeling that uses response trajectory to identify and summarize complex patterns in longitudinal data) was used to identify subgroups of subjects following similar patterns of response in each treatment arm, based on daily pain intensity scores from baseline through Week 16. We then examined whether subject-related variables were associated with any of the subgroups using multinomial logistic regression. Results A three-subgroup/four-inflection point trajectory model was selected based on clinical and statistical considerations. The subgroups were high responders (substantial pain improvement and a large majority of members achieved ≥ 30% improvement before Week 16), medium responders (gradual pain improvement and a majority of members achieved ≥ 30% improvement by Week 16), and non-responders (little to no pain improvement over 16 weeks). Across all treatments, fluctuation in pain intensity in the week prior to treatment was consistently associated with treatment response. Other variables were positively (age, body mass index, days of rescue medication use) or negatively (severity of disease based on Kellgren-Lawrence grading) associated with response but effects were small and/or varied across treatments. Conclusions Across all treatments, GBTM identified three subgroups of subjects that were characterized by extent of treatment response (high, medium, and non-responders). Similar analyses (e.g., grouping of subjects based on response trajectory and identification of subgroup-related variables) in other studies of OA could inform clinical trial design and/or treatment approaches. (NCT02697773; NCT02709486; NCT02528188).