P003 Predictors of differential treatment response between adalimumab and methotrexate in psoriasis: a causal forest analysis of the British Association of Dermatologists Biologics and Immunomodulators Register

Abstract While conventional comparative effectiveness studies report average treatment effects (ATEs), they may not capture the complex patterns of individual treatment responses. Causal forest, a tree-based machine learning approach that identifies patient-level factors influencing differential treatment responses, facilitates the estimation of conditional average treatment effects (CATEs) based on an individual’s covariates. Our aim was to identify patient characteristics that predict differential responses to adalimumab vs. methotrexate in psoriasis using causal forest analysis, and to characterize heterogeneous treatment effects across clinically relevant subgroups. The analysis compared biologic-naive adult patients who initiated either adalimumab or methotrexate between September 2007 and April 2024 across the UK and Republic of Ireland following the protocol recorded by the prospective registry the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR). Missing data were handled using one randomly selected dataset from multiple imputation by chained equations. A causal forest model was employed to estimate CATE for achieving Psoriasis Area and Severity Index (PASI) ≤ 2 during the treatment period, incorporating baseline patient characteristics and comorbidities. Variable importance analysis was used to identify key predictors of differential treatment response between adalimumab and methotrexate, with their effects quantified through best linear projection. In total 6810 patients (adalimumab 3974, methotrexate 2836) were included in the analysis. While adalimumab demonstrated superior overall effectiveness [ATE 0.41, 95% confidence interval (CI) 0.38–0.44], CATE analysis revealed substantial variation in individual treatment effects (median 0.42, interquartile range 0.37–0.47). Key predictors of differential treatment effects included male sex (absolute risk difference 0.15, P < 0.001). Effects were greater with increasing baseline PASI (0.007 per unit, P = 0.001), but lower with increasing age (−0.004 per year, P = 0.006) and weight (−0.003 per kg, P = 0.002). Subgroup analysis (CATE with 94% CI) showed higher treatment effects in younger patients (< 30 years: 0.46, 0.41–0.51 vs. > 70 years: 0.36, 0.34–0.39), female patients (0.45, 0.41–0.50 vs. male: 0.38, 0.35–0.41) and those with higher baseline PASI (> 20: 0.45, 0.41–0.49 vs. < 10: 0.41, 0.37–0.46). Increasing weight (< 90 kg: 0.43, 0.38–0.48 vs. > 120 kg: 0.40, 0.36–0.45) and comorbidity burden (no comorbidities: 0.44, 0.40–0.49 vs. ≥ 3 comorbidities: 0.37, 0.33–0.40) were associated with reduced treatment effects. The causal forest analysis reveals significant heterogeneity in responses to adalimumab and methotrexate, underscoring the impact of individual characteristics on treatment effectiveness. Factors such as age, sex, baseline PASI, weight and number of comorbidities are associated with differential treatment responses when prescribing adalimumab or methotrexate. These findings provide a data-driven framework to support personalized treatment decisions in clinical practice.