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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Apr 11;16:12186. doi: 10.1038/s41598-026-47976-8 Search in PMC Search in PubMed View in NLM Catalog Add to search Using causal machine learning and real world data to improve dose response decision making for secukinumab in psoriatic arthritis Uta Kiltz Uta Kiltz 1 Ruhr-Universität Bochum, Herne, Germany 2 Rheumazentrum Ruhrgebiet, Herne, Germany Find articles by Uta Kiltz 1, 2, # , Thomas Glassen Thomas Glassen 3 Novartis Pharma GmbH, Nürnberg, Germany Find articles by Thomas Glassen 3, # , Jan Brandt-Juergens Jan Brandt-Juergens 4 Rheumatologie, Schwerpunktpraxis, Berlin, Germany Find articles by Jan Brandt-Juergens 4 , Ferenc Kiss Ferenc Kiss 3 Novartis Pharma GmbH, Nürnberg, Germany Find articles by Ferenc Kiss 3 , Daniel Peterlik Daniel Peterlik 3 Novartis Pharma GmbH, Nürnberg, Germany Find articles by Daniel Peterlik 3 , Benjamin Gmeiner Benjamin Gmeiner 3 Novartis Pharma GmbH, Nürnberg, Germany Find articles by Benjamin Gmeiner 3, ✉ Author information Article notes Copyright and License information 1 Ruhr-Universität Bochum, Herne, Germany 2 Rheumazentrum Ruhrgebiet, Herne, Germany 3 Novartis Pharma GmbH, Nürnberg, Germany 4 Rheumatologie, Schwerpunktpraxis, Berlin, Germany ✉ Corresponding author. # Contributed equally. Received 2025 May 6; Accepted 2026 Apr 6; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . PMC Copyright notice PMCID: PMC13076753 PMID: 41965445 Abstract Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machine learning framework to real-world data from the AQUILA study to evaluate the impact of secukinumab dose escalation (150 mg to 300 mg) on disease activity and health-related quality of life (HR-QoL). Using double machine learning, we estimated both average and patient-level conditional treatment effects (CATEs) based on 42 baseline variables, including demographics, laboratory values, disease activity, HR-QoL, comorbidities, and prior treatments. 41.4% of patients receiving the higher dose showed greater improvement in Psoriatic Arthritis Impact of Disease (PsAID) scores compared to the lower dose (mean reduction: 1.81 vs. 1.44). Subgroup analysis revealed a 28% HR-QoL gain in patients with elevated body mass index (BMI) or C-reactive protein (CRP). The model predicted that approximately 75% of patients would benefit from dose escalation. These findings demonstrate the utility and scalability of causal machine learning for quantifying individualized treatment effects and guiding personalized treatment decisions in PsA. The approach is transferable to other chronic conditions, supporting more precise, data-driven care in real-world clinical settings. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-47976-8. Subject terms: Psoriatic arthritis, Machine learning, Predictive medicine Introduction The promise of personalized medicine lies in matching the right treatment - and dose - to the right patient. In patients with psoriatic arthritis (PsA), however, clinicians often rely on judgement rather than data when deciding on dose escalation. This gap reflects a broader challenge: how to identify treatment effect heterogeneity using real-world data. PsA is a chronic heterogeneous inflammatory immune-mediated disease characterized by inflammation of the musculoskeletal system, including arthritis, enthesitis, dactylitis, and spondylitis 1 , 2 . Approximately 30% of patients with psoriasis may develop PsA, especially those with severe psoriasis or involvement of the nails or scalp 2 – 4 . The prevalence of PsA in the general population ranges from 0.1% to 1% 2 , 5 , 6 , and the incidence is estimated to be 83 per 100,000 person-years (PY) [95% confidence interval (CI), 41–167 per 100,000 PY] 2 , 7 . The use of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) is recommended as the first-line treatment for patients with PsA in treatment guidelines, followed by the use of biological disease-modifying antirheumatic drugs (bDMARDs), apremilast, or targeted synthetic DMARDs (tsDMARDs) 8 . Recent advancements have led to the development of targeted therapies for psoriatic disease on the basis of the role of IL-23/IL-17 2 , 8 – 10 . Secukinumab, a fully human monoclonal antibody that selectively targets IL-17 A, is approved for the treatment of patients with PsA. Secukinumab has shown superior efficacy to placebo in PsA patients with arthritis, spondylitis, dactylitis, enthesitis, and skin and nail disease 11 – 15 . The European Medicines Agency (EMA) has approved an updated label for secukinumab, allowing an increase in dose to 300 mg from 150 mg in adults with active radiographic axial spondyloarthritis (r-axSpA) or active PsA when a treatment response is not achieved 16 . This update provides clinicians with more treatment options to achieve better patient responses and can lead to better treatment outcomes and potentially reduce disease progression. In clinical practice, physicians typically follow the approval status and recommend a higher dose for patients with prior biologic experience, moderate to severe PsA symptoms, or body weight exceeding 90 kilograms 16 . However, adherence to these guidelines is inconsistent in clinical practice, and dose decisions are often based on the clinical judgement of the individual physician. Evidence suggests that increasing the dose of secukinumab from 150 mg to 300 mg can improve efficacy outcomes in PsA patients 14 , 17 . A phase III study also demonstrated greater improvements in psoriasis symptoms with a higher dose of secukinumab 12 . Although there is evidence of improved efficacy with secukinumab dose escalation in patients with PsA, details on how much treatment escalation can improve outcomes within subgroups of patients or even at an individual level remain unclear. To overcome the shortcomings of guiding evidence, the use of causal machine learning (ML)-based models can be explored. While supervised ML methods have been applied to predict treatment outcomes in patients with PsA/ankylosing spondylitis (AS) 18 , to our knowledge, causal ML techniques have not yet been applied to evaluate the effects of updosing in PsA. In this context, causal inference plays a vital role in bridging the gap between prediction and decision-making. Causal inference is a systematic approach for comprehending the cause-and-effect relationships between variables or events. By identifying and understanding the factors that contribute to a specific outcome, causal inference provides valuable insights. This method is widely employed in fields such as social sciences, economics, and clinical research. In clinical research, causal analysis has proven valuable in analysing data from clinical trials 19 , especially when the overall population has a poor response or a weak or no response to a particular treatment. Through this method, researchers can assess treatment effect heterogeneity, providing a deeper understanding of the factors that influence treatment outcomes and identifying subgroups that respond more favourably to treatment 20 . Objective The primary objective was to develop a causal ML model that is based on data from the AQUILA non-interventional study. This model aims to predict the impact of a hypothetical increase in secukinumab dose from 150 mg (low) to 300 mg (high) on (i) disease activity and (ii) health-related quality of life (HR-QoL) in patients with PsA. The model offers insights into which patient subgroups are most likely to benefit from dose escalation. This causal machine learning analysis was not preregistered, as the approach was developed as an exploratory extension of the AQUILA study dataset to evaluate individualized treatment effects. Results Data from 1994 PsA patients were used in this ML study, and 1235 fulfilled the inclusion criteria for the analysis (Fig. 1 ). The baseline demographics and characteristics of the patients are presented in Table 1. Fig. 1. Open in a new tab ( a ) Sankey plot showing patient adherence in the 150 mg and 300 mg dose groups and ( b ) flow chart of the attrition of patients from PsAID assessment in the AQUILA study. The right panel displays patient dropout from PsAID assessments. The left panel depicts changes in treatment status throughout the six visits. The length of each bar and the link between bars are proportional to the number of patients. While slightly more patients began at a low dose, over the course of the visits, more patients transitioned to a high dose than did those who switched to a low dose. Ultimately, more patients were on the high dose. PsAID, Psoriatic Arthritis Impact of Disease. Visits were scheduled at baseline (Visit 1 at week 0) and at weeks 4, 16, 28, 40, and 52. As patients entered the study at different times, the exact visit dates varied between patients. Table 1. Baseline demographics and characteristics of patients. Characteristics Low dose (150 mg, n = 616) High dose (300 mg, n = 619) Count Mean ± SD Count Mean ± SD Age 616 53.3 ± 11.3 619 52.3 ± 11.7 Female, n (%) 616 377 (61.2) 619 335 (54.1) BMI 591 28.8 ± 5.7 608 30.0 ± 6.5 CRP level (mg/L) 476 8.4 ± 14.2 497 8.3 ± 13.2 PASI, 0–72 216 4.7 ± 8.6 279 9.4 ± 12.3 WJC, 0–67.3 392 5.7 ± 6.3 406 7.1 ± 7.2 SJC, 0–66 393 3.6 ± 4.5 406 4.4 ± 6.2 TJC, 0–68 393 6.8 ± 8.2 406 8.4 ± 8.5 PsAID score, 0–10 616 5.1 ± 2.1 619 5.4 ± 2.1 PGA score, 0–10 458 5.3 ± 2.5 432 5.3 ± 2.5 BDI score, 0–63 545 12.8 ± 9.4 544 12.6 ± 9.6 MOS Sleep score, 0.9–5.9 378 3.4 ± 0.4 383 3.4 ± 0.4 PhGA score, 0–10 560 5.5 ± 2.0 574 5.6 ± 2.1 SPI-II score, 16.8–65.9 356 42.6 ± 9.3 367 42.4 ± 10.1 Open in a new tab Abbreviations: BDI: beck depression inventory; BMI: body mass index; CRP: C-reactive protein; MOS: medical outcomes study sleep scale; PASI: psoriasis area and severity index; PGA: patient global assessment; PhGA: physician’s global assessment; PsAID: psoriatic arthritis impact of disease; SJC: swollen joint count; SPI-II: sleep problems index-II; TJC: tender joint count; WJC: weighted joint count. Notes: Only data from patients with PsAID scores at baseline and visit 3 or visit 4 are presented. Among the 1235 patients, 616 received 150 mg secukinumab (low dose), and 619 received 300 mg secukinumab (high dose) at the baseline visit. In general, the high-dose group presented numerically higher disease activity scores and HR-QoL scores at baseline, indicating more severe disease symptoms (see Table 1). The relevant demographics were similar between the two groups. Disease activity and HR-QoL outcomes Changes in disease activity scores and HR-QoL scores are illustrated in Fig. 2 . The number of missing values varied between 76% for the PASI and 38% for the PsAID. Fig. 2. Open in a new tab Observed disease activity and HR-QoL outcome changes for 150 and 300 mg doses at weeks 28 or 40 in the AQUILA study. The case numbers for each dose and dimension are shown near the axis. To show the most important differences first, dimensions are ordered by the relative overlap of their dose-specific 95% confidence intervals, from least to most. BDI-II, Beck Depression Inventory-II; PASI, Psoriasis Area and Severity Index; MOS, Medical Outcomes Sleep Scale; PhGA, Physician’s Global Assessment; PGA, Patient Global Assessment; PsAID, Psoriasis Arthritis Impact of Disease; SPI-II, Sleep Problems Index-II; CRP, C-reactive protein. Significant changes in scores between baseline and Visit 3/4 (at week 16–28) were observed for the PASI ( p < 0.001), PsAID ( p < 0.01), and BDI ( p < 0.05). The PsAID score was selected as the primary outcome measure because of the high completeness of the data. The high-dose cohort had a numerically greater ( p > 0.05) baseline PsAID score of 5.35 than did the low-dose cohort (5.12) (Table 1; Fig. 3 (c)). Fig. 3. Open in a new tab Observed HR-QoL (PsAID) across visits. ( a ) Line plot showing the PsAID scores of patients receiving low (150 mg) and high (300 mg) secukinumab doses across six visits. ( b ) Patient distribution into high, moderate, and low HR-QoL and remission categories over six visits. ( c ) Table comparing low- and high-dose groups: mean baseline PsAID score, percentage of effectively treated patients, and mean PsAID score change. HR-QoL, health-related quality of life; PsAID, Psoriatic Arthritis Impact of Disease. The average PsAID score reduction after treatment was − 1.63 points. While the overall population achieved a PsAID score reduction, the high-dose cohort had a greater reduction ( p < 0.001) of 1.81 PsAID points compared with 1.44 PSAID points for the low-dose cohort at Visit 3. When effective treatment was defined as a PsAID score change of ≥ 3 points, 29.1% and 35.4% of patients achieved this under low and high doses, respectively (Fig. 3 (c)). From the curvature of the line plot for both doses (Fig. 3(a)), most changes in value occurred between Visit 1 and Visit 3, and after Visit 3, the line was much flatter; therefore, the change between Visits 1 and 3 was used as the target value for causal analysis. The pooled distribution of patients across HR-QoL categories (remission, low, moderate, high impact) over time is shown in Fig. 3 b, indicating a general shift toward better health-related quality of life between Visit 1 and Visit 3. The PsAID score distribution at baseline and PsAID score changes after 16–28 weeks are presented in Supplementary Fig. 1 . Model features Fitting the causal model to the full cohort of 1235 patients revealed a global causal effect for each feature. The global causal effect represents the overall effect considering all patients, as opposed to local effects that focus on specific individual or subgroup responses. The associated coefficients with the respective p values are depicted in the forest plot in Fig. 4 . Fig. 4. Open in a new tab Forest plot of model coefficients and p values. Coefficients are sorted by p value in ascending order. Each coefficient represents the PsAID score reduction per unit feature change. For example, a start dose change from 0 (150 mg) to 1 (300 mg) decreased the PsAID score by 0.28 points. BDI, Beck Depression Inventory; BMI, body mass index; CRP, C-reactive protein; PASI, Psoriasis Area and Severity Index; PhGA, Physician´s Global Assessment; MOS, Medical Outcomes Study Sleep Scale; PGA, Patient Global Assessment; PsAID, Psoriatic Arthritis Impact of Disease; biologics pretreat., number of pretreatments with biologics; months of etanercept pretreat., months pretreated with etanercept; smoking status, smoking status with active smoker as baseline, e.g., ex-smokervactive smoker depicts the relative effect of ex-smoker status against active smoker status on the PsAID score; 1v0, binary (yes/no); num, numerical. Response to treatment in subgroups The causal model yielded a significant p value for “Start dose,” indicating a significant effect of updosing on HR-QoL, with an average CATE estimated at 0.236 units (compared with the biased 0.37 units). The heterogeneity tree in Fig. 5 shows modelled subgroup changes in response differences in the PsAID between the 150 mg and 300 mg doses. The root node CATE of 0.24 corresponds to the average CATE of all individuals. A total of 918 patients with a BMI ≤ 32.5 exhibited a poor response difference between treatments, with a CATE of 0.14, whereas 317 patients with a higher BMI had a more favourable CATE of 0.52. Further divisions can be made for more precise treatment response differences, as shown in Fig. 5. Notably, 613 patients within the leaves (lowest level nodes) with green boundaries reduced their PsAID score on average by 0.45 points with updosing from 150 to 300 mg, a 28% improvement over the status quo. Fig. 5. Open in a new tab Predicted heterogeneity tree illustrating subgroup treatment responses. The heterogeneity tree resembles a decision tree, but it splits at points that maximize heterogeneity in the resulting “child” nodes. Those of the latter, which fulfil the corresponding splitting condition are always arranged to the left. Each leaf represents a subgroup of samples whose response to treatment is distinct from that of the other samples. The root node indicates the entire population with an average CATE (= reduction from 150 mg to 300 mg) of 0.236 units, whereas nodes with green boundaries represent subgroups that respond more favourably to the treatment, showing a 28% improvement versus 16% for the overall population. CATE, conditional average treatment effect difference; PsAID, Psoriatic Arthritis Impact of Disease; preTx Biologics, number of pretreatments with biologics; CRP, C-reactive protein; BMI, body mass index. Response to treatment at the individual level The distribution of predicted treatment effects on PsAID scores among patients receiving low and high doses if they hypothetically switched to the other dose group is shown in Fig. 6 . Fig. 6. Open in a new tab Predicted overall impact of a hypothetical initial treatment switch on PsAID scores. (a) Histogram: Approximately 75% of patients who switched from the low to the high dose could achieve up to a 0.8-point gain in benefit, whereas 75% of patients who switched from the high to the low dose might lose up to 0.8 points of benefit. ( b ) Covariate impact 2D density plots: High effectiveness for patients with “BMI” > 38 when switching doses is shown at the top left, whereas for “pretreatment biologics count”, no distinct dose‒response difference is visible in the lower left plot. Low dose: 150 mg; high dose: 300 mg. PsAID, Psoriatic Arthritis Impact of Disease; BMI, body mass index. To provide another perspective on treatment effects, we assessed patient shifts between predefined burden categories following a simulated treatment switch. The outcomes for the low-dose cohort (top row) and the corresponding distribution of potential outcomes after dose escalation (bottom row) are shown in Fig. 7 . Fig. 7. Open in a new tab Predicted patient burden category shifts after dose increases considering the counterfactual outcomes. The top row shows the distribution of patient outcomes under low-dose treatment, whereas the bottom row represents potential outcome distributions after dose escalation. The arrows indicate the absolute number of patients shifting between categories, with percentages shown in parentheses. Sixty-two out of 141 high-burden patients (44%) transitioned to the moderate burden category. Among the 164 patients with a moderate burden at the low dose, 90 (55%) shifted to the low burden category. Similarly, of the 195 patients classified as having a low burden under the low dose, 103 (53%) moved into remission after the dose increase. Discussion and outlook In clinical studies, most PsA patients experience a substantial decrease in disease activity during the initial three months of biologic treatment. This observation guided us to focus our analysis on the period from Visit 1 to Visit 3/4. During this timeframe, we evaluated changes in 6 h-QoL and 3 disease activity measures in PsA patients. Among these, only changes in PsAID scores at baseline were significantly different between the two cohorts. Notably, the high-dose cohort had a higher baseline PsAID score (5.35) than did the low-dose cohort (5.12). Both groups demonstrated improvements in PsAID scores, with the high-dose group showing a more pronounced reduction (1.81 vs. 1.44) over approximately 16 weeks. While the low-dose cohort appeared to be more effectively treated in terms of PhGA scores, this difference was numerical rather than statistically significant. Our causal ML model suggests that increasing the dose of secukinumab could significantly improve treatment outcomes for PsA patients. The model predicts that approximately 75% of all patients should receive a higher dose, and 75% of those currently on one dose should switch. However, these findings are based solely on model predictions and do not account for the current approval status of secukinumab, emphasizing the need for regulatory considerations before implementing dose changes in clinical practice. Given real-world treatment, assignment is often subject to selection bias. Patients were not randomly allocated to the groups, and those in the 300 mg dose cohort typically presented with more severe symptoms. Hence, the observed PsAID score difference between the low- and high-dose cohorts is likely influenced by selection bias. To address this, we developed a causal model employing methods such as inverse probability of treatment weighting (IPTW) to mitigate bias. Although the significant effect of updosing on HR-QoL, with an average CATE estimated at 0.236 units (compared with the biased 0.37 units), may appear modest, it represents a notable improvement of approximately 16% compared to the average reduction of about 1.44 units in the low-dose cohort (see Fig. 3 C). The causal model identified several significant features, including “smoking status”, “PsAID”, “BMI”, “BDI”, and “plaque psoriasis”. Patients with high baseline PsAID scores usually start with higher doses, which significantly affects PsAID score changes. Baseline PsAID scores also moderately correlate with BDI scores, which might explain why the change in BDI score was identified as significant. Additionally, smokers responded less favourably to low doses than to high doses, underscoring the need for higher doses because of the detrimental impact of smoking on the treatment response (see Supplementary Fig. 2 ) 21 . Moreover, BMI aligns with guidelines suggesting high doses for patients exceeding 90 kg, whereas plaque psoriasis often indicates more severe PsA and thus a higher warranted dose. Additionally, EMA guidelines recommend a 300 mg dose for TNF inhibitor (TNFi)-inadequate responders. The causal model can generate a straightforward decision tree to determine optimal doses for patients. For example, patients with an elevated BMI or high CRP level should receive high doses, leading to outcome improvements ranging from 16% to 28% among these groups. The purpose of using supervised ML algorithms is to minimize discrepancies between actual and predicted values. While these models can be highly accurate, they may not always yield the desired results when applied in practice. Accurate models may also lack sufficient guidance for system modifications or decision-making. A notable limitation of the model is its failure to account for drug costs and potential side effects. Higher doses tend to be recommended on the basis of model predictions, given the superior treatment effects of these doses. However, lower doses are more cost-effective, making them preferable unless the higher dose substantially improves treatment efficacy (e.g., a PsAID score reduction of more than 0.2 points). Without a defined threshold, 98% of patients would commence treatment on a higher dose; with a 0.2 point threshold, 66% should start treatment on a higher dose; and with a 0.5 point threshold, only 9% of patients should begin treatment on a higher dose. Another limitation of this study relates to the inspected metrics. The AQUILA study was initiated in 2016 (with protocol development even earlier), when inclusion of measures like DAPSA/cDAPSA and PASDAS were not collected and cannot be reliably derived due to systematic and random missingness in several required components. Consequently, these measures could not be included in this analysis. Despite these limitations, this study demonstrates the feasibility and value of applying causal ML to real-world data to inform personalized dosing strategies in PsA patients. By identifying subgroups most likely to benefit from increased doses, our model can serve as a robust tool for personalized treatment strategies, optimizing clinical outcomes in a cost-effective manner. This approach highlights the need for a nuanced understanding of dose‒response relationships and the potential for personalized medicine to improve patient care. The aim of future studies should be to refine these models further, incorporating additional variables such as drug costs and side effect profiles, to develop even more comprehensive treatment guidelines. Ultimately, integrating causal ML into clinical practice holds promise for transforming the management of PsA and other complex diseases, leading to improved patient outcomes and more efficient health care delivery. Moreover, it is worth noting the potential for such models to be extended beyond PsA to other diseases in which tailored treatment plans are required. The ability of these models to account for individual patient characteristics and predict optimal interventions underscores their significance. The continuous refinement of these models, alongside advancements in data collection and analysis techniques, will be pivotal in their successful implementation. It is essential to engage healthcare professionals, researchers, and stakeholders in a collaborative effort to harness the full potential of causal ML, ensuring that the benefits are maximized for patients and healthcare systems alike. Methods AQUILA study AQUILA is an ongoing multicentre, prospective, non-interventional study designed to assess the effectiveness, safety, and adherence rates of secukinumab in patients with active PsA or r-axSpA in routine clinical practice in Germany 22 . The study includes patients aged ≥ 18 years with high disease activity who are either receiving secukinumab or scheduled to initiate treatment based on therapeutic need. Patients were treated following the approved guidelines, initially receiving either 150 mg or an increased 300 mg dose of secukinumab, with the dose being adjusted as needed throughout the study. Exclusion criteria were data unavailability for the 52-week observation period, concurrent participation in other clinical or non-interventional studies involving secukinumab, or existing contraindications to secukinumab. Each patient was observed for a planned period of 52 weeks 22 . Data was collected over the observational period, with visits scheduled at baseline (week 0) and at weeks 4, 16, 28, 40, and 52. As patients entered the study at different times, the exact visit dates varied between patients. The data collected for assessing disease activity and HR-QoL in patients with PsA included scores for the Psoriatic Arthritis Impact of Disease (PsAID), Beck Depression Inventory (BDI), Medical Outcomes Study Sleep Scale (MOS Sleep), Physician’s Global Assessment (PhGA), Sleep Problems Index-II (SPI-II), Psoriasis Area and Severity Index (PASI) response, and visual analogue scale (VAS) for global disease activity, global pain, and nighttime pain. Patient selection Patients were included if they were adults (aged ≥ 18 years) with a physician-confirmed diagnosis of active PsA who started secukinumab treatment not earlier than 4 weeks before the baseline. Additionally, patients needed to have records available at both baseline and Visit 3 or, if Visit 3 data were missing, Visit 4. During this period, patient secukinumab dose had to remain unchanged. To ensure consistency in the analysis, only patients meeting these criteria were included. Study variables In the analysis, changes in disease activity, HR-QoL scores and PsAID scores from baseline to Visits 3 or 4. The PsAID score ranges between 0 and 10 on the basis of responses to 12 questions, with 10 representing the worst outcome for PsA patients 23 . To evaluate the treatment effect and impact of both doses (150 mg and 300 mg), PsAID scores were divided into four categories: remission (< 2), low (≥ 2 & ≤ 3), moderate (> 3 & < 6) and high (≥ 6). Our categorization into 4 categories is identical to Di Carlo et al. 25 who also divide PsAID into remission (0, 1), low (2,3,4), moderate (5,6) and high (7+). However, we applied a more strict categorization as we included a score of 4 already into the moderate category and a score of 6 in the high category. Effective treatment was considered a ≥ 3-point change in the PsAID score. This reflects the identified improvement threshold of 1.5–2.5 points 26 for the PsAID-12 total score with an additional safety margin of 1. A t-test was performed to determine whether there were significant differences between the 150 mg and 300 mg groups. The PsAID score was selected as the primary outcome measure because of the high completeness of the data. Causal machine learning model Causal ML encompasses a class of methods that integrate statistical techniques with ML algorithms to infer causal relationships from observational data. These models enable researchers to estimate the potential outcomes of different interventions, such as a dose adjustment, on disease activity and HR-QoL. In the present study, the treatment effect for each patient with active PsA was estimated by comparing two potential outcomes: one under 300 mg dose treatment and another under 150 mg dose treatment. However, the individual treatment effect could not be directly observed, as each patient could be assigned to only one treatment condition. To address these challenges, we applied the double ML framework to estimate the conditional average treatment effect (CATE), as illustrated in Fig. 8 . Fig. 8. Open in a new tab Overview of the methodology used to evaluate the effects of varying dose levels. ( a ) Development of the causal machine learning model. ( b ) Application of the model to data from patients with an initial dose of 150 mg to predict the Visit 3/4 outcome for a hypothetical initial dose of 300 mg. The same principle applied to the case of a 300 mg starting dose. ( c ) Two-stage causal inference framework (referred to as double machine learning). CATE, conditional average treatment effect. In causal inference, CATE represents the expected treatment effect for a specific subgroup or individual given their observed characteristics. In contrast, the average treatment effect (ATE) is the overall expected difference in outcomes between the two treatment conditions across the entire population. While various causal inference methods exist for different problem settings, we chose the double ML framework because of its robustness in handling high-dimensional data and its ability to provide unbiased estimates of treatment effects. The aim of our model is to quantify the effect of treatment escalation at both the individual and subgroup levels. For this analysis, we utilized the CausalAnalysis class from EconML, a Python package developed by Microsoft Research that provides ML techniques for estimating individual treatment effects from observational or experimental data 24 . EconML offers a suite of causal inference tools, including double ML. These tools allow users to integrate their ML models into the framework, enabling the analysis of features responsible for causality. In addition to identifying causal factors, EconML can be leveraged to analyse and interpret appropriate interventions to address these underlying factors. We used either the LinearDML or the CausalForestDML class for Stage 2 of the double ML framework. The “CausalAnalysis” class handles the detailed implementation of this approach, allowing us to focus on parameters such as choosing a ‘linear’ or ‘forest’ model and setting different feature subsets as confounders to untangle the selection bias. This class also includes functions to determine causal feature importance and create policy trees to identify subgroups with varied treatment responses. Since the counterfactual value in the heterogeneity model is a predicted value (not directly observed in the data), validating these predictions is not straightforward. A counterfactual value represents what would have happened under a different treatment scenario. As a result, there is no performance metric that can be used for testing and validating counterfactuals. However, during model fitting, the CausalAnalysis class provides k-fold cross validation to evaluate causal models on the training data. The model selection process, which encompasses performance metrics as well as the ML optimization and validation pipeline, is outlined in the supplementary material. Variable selection to be considered in the model Potential inputs to ML models included 42 variables identified in our previous study 18 , including demographic characteristics, laboratory measurements, disease activity scores, HR-QoL scores, pretreatment data, and comorbidities at baseline (Visit 1). Variables with ≥ 50% missing values were removed from the test dataset. Five types of variables were considered in the model: Y is the target variable, T is the treatment variable, W represents confounder variables affecting both the treatment and the outcome, and X includes the remaining variables that affect only the outcome: Target variable (Y) : Changes in the scores of 3 disease activity measures and 6 h-QoL measures (between baseline and Visit 3/4) were used individually as the outcome to construct causality models. The target variables for disease activity and HR-QoL included PsAID, BDI, PhGA, SPI-II, PGA, and PASI response scores. The selection of those scores was based on the content and rate of missing data. Treatment variable (T) : A single variable for 150 mg (0) or 300 mg dose (1) patients starting at baseline. Confounder variables (W) : Baseline value of the target variable (Y), BMI, PASI score, number of months pretreated with TNF-α inhibitors, number of pretreatments with biologics, and presence of plaque psoriasis. Details about how we identified these confounders can be found in the subsection ‘METHODS/Causal analysis’ in the supplementary material. Control variables (X) : Demographic characteristics, baseline disease activity or HR-QoL scores, and comorbidities. The final feature selection for the ML model was determined through Shapley Additive exPlanations (SHAP) analyses, literature research, and model tuning to ensure optimal performance and clinical relevance; see also the supplementary section ‘Model Selection’. Ethics and consent All patients provided written informed consent for the use of their real-life data in this non-interventional study of patients with active PsA or axSpA in routine clinical practice in Germany. All methods were carried out in accordance with relevant guidelines and regulations. The study was conducted in accordance with local legislation and institutional requirements and complied with Novartis and regulatory standards, ensuring that the rights, safety, and well-being of participants were protected, consistent with the principles of the Declaration of Helsinki. As ethics committees in Germany do not provide formal approval for non-interventional studies, a voluntary ethics consultation was conducted with the Ethics Committee of the University of Würzburg. Advice was obtained on all aspects of the study, including handling of patient data and study procedures, and all recommendations were fully implemented. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (173.1KB, docx) Acknowledgements We would like to thank Sean Wang for early experiments and fruitful discussions. Author contributions BG designed the study, FK prepared the data, TG performed the analysis. 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