Nonparametric analysis of delayed treatment effects using single-crossing constraints.

Clinical trials involving novel immuno-oncology therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment effect, and, in such settings, the survival curves in the two treatment arms may have a crossing before the two curves eventually separate. To flexibly model such scenarios, we describe a nonparametric approach for estimating the treatment arm-specific survival functions which constrains these two survival functions to cross at most once without making any additional assumptions about how the survival curves are related. A main advantage of our approach is that it provides an estimate of a crossing time if such a crossing exists, and, moreover, our method generates interpretable measures of treatment benefit including crossing-conditional survival probabilities and crossing-conditional estimates of restricted residual mean life. Our estimates of these measures may be used together with efficacy measures from a primary analysis to provide further insight into differences in survival across treatment arms. We demonstrate the use and effectiveness of our approach with a large simulation study and an analysis of reconstructed outcomes from a recent combination therapy trial.

Efficacy and safety of autologous platelet-rich plasma for diabetic foot ulcers: a systematic review and meta-analysis.

OBJECTIVE: To investigate the efficacy and safety of autologous platelet-rich plasma (au-PRP) for diabetic foot ulcer (DFU) treatment. METHOD: We conducted database searches (MEDLINE, EMBASE, evidence-based medicine reviews: CENTRAL, PubMed, and Web of Science) and reference mining for randomised controlled trials from inception to 23 January 2022. Results were scrutinised, data were extracted and research quality was investigated by two independent authors. Primary outcome was the proportion of complete ulcer healing. Secondary outcomes included both the mean time to complete healing and the incidence of adverse events. Statistical analyses were performed in RevMan 5.4 (Cochrane, UK). Kaplan-Meier curves for time to complete healing were pooled in R software (version 4.1.2) (R Foundation, Austria). RESULTS: Of the 231 records identified, 17 studies with a total of 1303 participants (649 randomised to the au-PRP group and 654 to a standard of care (SOC) group) met the eligibility criteria and were included in our study. Compared with SOC, au-PRP appeared to promote the complete healing rate (odds ratio (OR): 2.11; 95% Confidence Interval: 1.55-2.86). Au-PRP also appeared to significantly shorten complete healing time (mean duration: -19.04 days; 95%CI: -20.46--17.61]). There was no significant difference on adverse events. Results were robust on sensitivity analyses. CONCLUSION: Based on the findings of this review and meta-analysis, Au-PRP is an effective and safe adjuvant therapy for DFUs.

Upadacitinib as monotherapy and in combination with non-biologic disease-modifying antirheumatic drugs for psoriatic arthritis.

OBJECTIVE: To assess the efficacy and safety of upadacitinib (UPA), an oral Janus kinase inhibitor, as monotherapy or in combination with non-biologic DMARDs (nbDMARDs) in patients with PsA. METHODS: Pooled data were analysed from patients with prior inadequate response or intolerance to one or more nbDMARD (SELECT-PsA 1) or one or more biologic DMARD (SELECT-PsA 2) who received placebo, UPA 15 mg once daily (QD) or UPA 30 mg QD as monotherapy or in combination with two or fewer nbDMARDs for 24 weeks. Efficacy outcomes included achievement of ACR responses, Psoriasis Area and Severity Index responses, minimal disease activity and change from baseline and clinically meaningful improvement in the HAQ Disability Index. Adverse events (AEs) were summarized. RESULTS: A total of 1916 patients were included; 574 (30%) received monotherapy and 1342 (70%) received combination therapy. Placebo-subtracted treatment effects for a 20% improvement in ACR criteria at week 12 were 33.7% (95% CI 24.4, 43.1) and 34.0% (95% CI 27.9, 40.1) for UPA 15 mg QD monotherapy and combination therapy, respectively, and 45.7% (95% CI 36.9, 54.5) and 39.6% (95% CI 33.7, 45.5) for UPA 30 mg QD monotherapy and combination therapy, respectively. Treatment effects for other outcomes were consistent between monotherapy and combination therapy. AE frequency was generally similar for UPA monotherapy and combination therapy, although hepatic disorders and creatine phosphokinase elevation were more common with combination therapy vs monotherapy. CONCLUSION: The efficacy and safety of UPA were generally consistent when administered as monotherapy or in combination with nbDMARDs through 24 weeks, supporting the use of UPA with or without nbDMARDs in PsA. TRIAL REGISTRATION: ClinicalTrials.gov (https://clinicaltrials.gov): SELECT-PsA 1 (NCT03104400), SELECT-PsA 2 (NCT03104374).

Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees.

Quantitative structure-activity relationship (QSAR) is a very commonly used technique for predicting the biological activity of a molecule using information contained in the molecular descriptors. The large number of compounds and descriptors and the sparseness of descriptors pose important challenges to traditional statistical methods and machine learning (ML) algorithms (such as random forest (RF)) used in this field. Recently, Bayesian Additive Regression Trees (BART), a flexible Bayesian nonparametric regression approach, has been demonstrated to be competitive with widely used ML approaches. Instead of only focusing on accurate point estimation, BART is formulated entirely in a hierarchical Bayesian modeling framework, allowing one to also quantify uncertainties and hence to provide both point and interval estimation for a variety of quantities of interest. We studied BART as a model builder for QSAR and demonstrated that the approach tends to have predictive performance comparable to RF. More importantly, we investigated BART's natural capability to analyze truncated (or qualified) data, generate interval estimates for molecular activities as well as descriptor importance, and conduct model diagnosis, which could not be easily handled through other approaches.

Quantitative MRI biomarkers to characterize regional left ventricular perfusion and function in nonhuman primates during dobutamine-induced stress: A reproducibility and reliability study.

PURPOSE: To identify reproducible and reliable noninvasive regional imaging biomarkers of cardiac function and perfusion at rest and under stress in healthy nonhuman primates (NHPs) that may be used in the future for the early characterization of preclinical heart failure models, to evaluate therapy, and for clinical translation. MATERIALS AND METHODS: Seven naive cynomolgus macaques underwent test-retest 3T cardiac MRI tagging and dual-bolus perfusion experiments. Regional cardiac function biomarkers, such as peak circumferential strain (CS), average diastolic strain-rate (DSR), contractile reserve (CR), diastolic reserve, peak torsion, and torsion reserve were quantified. Further, regional myocardial blood flow (MBF), myocardial perfusion reserve (MPR), and myocardial perfusion reserve-to-contractile reserve (MPR/CR) were also derived. Inter- and intraobserver reproducibility and test-retest reliability analyses were conducted using the reliability and generalizability coefficients including correlation coefficient (CC) and intraclass correlation coefficient (ICC). RESULTS: Overall, peak CS, DSR, and MBF are robust biomarkers at both rest and stress with moderate-good inter- and intraobserver reproducibility and test-retest reliability. At rest: intra-/interobserver reproducibility (CC): peak CS (0.81/0.81), DSR (0.81/0.81), MBF (0.72/0.57), peak torsion (0.79/0.79); test-retest reliability: (CC/ICC): peak CS (0.62/0.75), DSR (0.24/0.55), MBF (0.66/0.62), and peak torsion (0.79/0.78). Under stress: intra-/interobserver reproducibility (CC): peak CS (0.61/0.60), DSR (0.50/0.50), MBF (0.63/0.61), MPR (0.43/0.43), and peak torsion (0.38/0.38); test-retest reliability: (CC/ICC): peak CS (0.58/0.58), DSR (0.24/0.43), MBF (0.58/0.58), MPR (0.43/0.38), and peak torsion (0.38/0.38). CONCLUSION: We demonstrated the feasibility of using cardiac MRI to characterize left ventricular functional and perfusion responses to stress in an NHP species, and specific robust biomarkers such as peak CS, DSR, MBF, diastolic reserve, and MPR have been identified for clinical translation and drug research. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:556-569.

Evaluation of an fMRI USPIO-based assay in healthy human volunteers.

PURPOSE: To present the testretest and contrast dose effect results of cerebral blood volume (CBV) functional MRI (fMRI) in healthy human volunteers using ferumoxytol (Feraheme), an ultrasmall-superparamagnetic iron oxide (USPIO) nanoparticle. MATERIALS AND METHODS: This was an open-label, two-period, fixed-sequence study in healthy young volunteers. In eight subjects, using a 3 Tesla field strength system, blood oxygen level dependent (BOLD) and CBV fMRI were acquired in response to a visual black-and-white checkboard stimulation paradigm using an escalating ferumoxytol dose design (250, 350, and 510 mg iron). Multiple outcome measures were analyzed including absolute percent signal change (|PSC|, primary endpoint), its contrast-to-noise ratio (CNR) and corresponding z-score, percent CBV change (ΔCBV) and respective CNR, concentration of Fe, and baseline CBV. RESULTS: The |PSC| in the visual cortex increased with ferumoxytol dose and was up to 3 × higher than BOLD fMRI. Test-retest reliability was comparable for BOLD and CBV fMRI. Intraclass correlation coefficients (ICCs) for |PSC| were 0.3 (one-sided 95% lower confidence limit = 0.00), 0.81 (0.47), 0.48 (0.00), and 0.3 (0.00) for BOLD and the 250-, 350-, and 510-mg doses of ferumoxytol, respectively. For ΔCBV, ICCs were 0.77 (0.37), 0.48 (0.00), and 0.49 (0.00) for 250 mg, 350 mg, and 510 mg, respectively. CONCLUSION: This work demonstrates that CBV fMRI techniques and endpoints are dose dependent, robust and have good test-retest repeatability. It also confirms previous findings that USPIO enhances sensitivity of fMRI stimulus-response endpoints. LEVEL OF EVIDENCE: 1 J. MAGN. RESON. IMAGING 2017;46:124-133.

A unified Bayesian semiparametric approach to assess discrimination ability in survival analysis.

The discriminatory ability of a marker for censored survival data is routinely assessed by the time-dependent ROC curve and the c-index. The time-dependent ROC curve evaluates the ability of a biomarker to predict whether a patient lives past a particular time t. The c-index measures the global concordance of the marker and the survival time regardless of the time point. We propose a Bayesian semiparametric approach to estimate these two measures. The proposed estimators are based on the conditional distribution of the survival time given the biomarker and the empirical biomarker distribution. The conditional distribution is estimated by a linear-dependent Dirichlet process mixture model. The resulting ROC curve is smooth as it is estimated by a mixture of parametric functions. The proposed c-index estimator is shown to be more efficient than the commonly used Harrell's c-index since it uses all pairs of data rather than only informative pairs. The proposed estimators are evaluated through simulations and illustrated using a lung cancer dataset.

Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers.

Prostate-specific antigen (PSA) is a widely used marker in clinical trials for patients with prostate cancer. We develop a mixture model to estimate longitudinal PSA trajectory in response to treatment. The model accommodates subjects responding and not responding to therapy through a mixture of two functions. A responder is described by a piecewise linear function, represented by an intercept, a PSA decline rate, a period of PSA decline, and a PSA rising rate; a nonresponder is described by an increasing linear function with an intercept and a PSA rising rate. Each trajectory is classified as a linear or a piecewise linear function with a certain probability, and the weighted average of these two functions sufficiently characterizes a variety of patterns of PSA trajectories. Furthermore, this mixture structure enables us to derive clinically useful endpoints such as a response rate and time-to-progression, as well as biologically meaningful endpoints such as a cancer cell killing fraction and tumor growth delay. We compare our model with the most commonly used dynamic model in the literature and show its advantages. Finally, we illustrate our approach using data from two multicenter prostate cancer trials. The R code used to produce the analyses reported in this paper is available on request.

A robust bayesian estimate of the concordance correlation coefficient.

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student's t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

A Bayesian estimate of the concordance correlation coefficient with skewed data.

Concordance correlation coefficient (CCC) is one of the most popular scaled indices used to evaluate agreement. Most commonly, it is used under the assumption that data is normally distributed. This assumption, however, does not apply to skewed data sets. While methods for the estimation of the CCC of skewed data sets have been introduced and studied, the Bayesian approach and its comparison with the previous methods has been lacking. In this study, we propose a Bayesian method for the estimation of the CCC of skewed data sets and compare it with the best method previously investigated. The proposed method has certain advantages. It tends to outperform the best method studied before when the variation of the data is mainly from the random subject effect instead of error. Furthermore, it allows for greater flexibility in application by enabling incorporation of missing data, confounding covariates, and replications, which was not considered previously. The superiority of this new approach is demonstrated using simulation as well as real-life biomarker data sets used in an electroencephalography clinical study. The implementation of the Bayesian method is accessible through the Comprehensive R Archive Network.

A short note on jackknifing the concordance correlation coefficient.

Lin's concordance correlation coefficient (CCC) is a very popular scaled index of agreement used in applied statistics. To obtain a confidence interval (CI) for the estimate of CCC, jackknifing was proposed and shown to perform well in simulation as well as in applications. However, a theoretical proof of the validity of the jackknife CI for the CCC has not been presented yet. In this note, we establish a sufficient condition for using the jackknife method to construct the CI for the CCC.

A unified Bayesian hierarchical model for MRI tissue classification.

Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different steps, we extend a previous work to develop a unified Bayesian hierarchical model, which addresses both the partial volume effect and intensity non-uniformity, the two major acquisition artifacts, simultaneously. We adopted a normal mixture model with the means and variances depending on the tissue types of voxels to model the observed intensity values. We modeled the relationship among the components of the index vector of tissue types by a hidden Markov model, which captures the spatial similarity of voxels. Furthermore, we addressed the partial volume effect by construction of a higher resolution image in which each voxel is divided into subvoxels. Finally, We achieved the bias field correction by using a Gaussian Markov random field model with a band precision matrix designed in light of image filtering. Sparse matrix methods and parallel computations based on conditional independence are exploited to improve the speed of the Markov chain Monte Carlo simulation. The unified model provides more accurate tissue classification results for both simulated and real data sets.

Bayesian random threshold estimation in a Cox proportional hazards cure model.

In this paper, we develop a Bayesian approach to estimate a Cox proportional hazards model that allows a threshold in the regression coefficient, when some fraction of subjects are not susceptible to the event of interest. A data augmentation scheme with latent binary cure indicators is adopted to simplify the Markov chain Monte Carlo implementation. Given the binary cure indicators, the Cox cure model reduces to a standard Cox model and a logistic regression model. Furthermore, the threshold detection problem reverts to a threshold problem in a regular Cox model. The baseline cumulative hazard for the Cox model is formulated non-parametrically using counting processes with a gamma process prior. Simulation studies demonstrate that the method provides accurate point and interval estimates. Application to a data set of oropharynx cancer patients suggests a significant threshold in age at diagnosis such that the effect of gender on disease-specific survival changes after the threshold.

A comparison of confidence interval methods for the concordance correlation coefficient and intraclass correlation coefficient with small number of raters.

The intraclass correlation coefficient (ICC) with fixed raters or, equivalently, the concordance correlation coefficient (CCC) for continuous outcomes is a widely accepted aggregate index of agreement in settings with small number of raters. Quantifying the precision of the CCC by constructing its confidence interval (CI) is important in early drug development applications, in particular in qualification of biomarker platforms. In recent years, there have been several new methods proposed for construction of CIs for the CCC, but their comprehensive comparison has not been attempted. The methods consisted of the delta method and jackknifing with and without Fisher's Z-transformation, respectively, and Bayesian methods with vague priors. In this study, we carried out a simulation study, with data simulated from multivariate normal as well as heavier tailed distribution (t-distribution with 5 degrees of freedom), to compare the state-of-the-art methods for assigning CI to the CCC. When the data are normally distributed, the jackknifing with Fisher's Z-transformation (JZ) tended to provide superior coverage and the difference between it and the closest competitor, the Bayesian method with the Jeffreys prior was in general minimal. For the nonnormal data, the jackknife methods, especially the JZ method, provided the coverage probabilities closest to the nominal in contrast to the others which yielded overly liberal coverage. Approaches based upon the delta method and Bayesian method with conjugate prior generally provided slightly narrower intervals and larger lower bounds than others, though this was offset by their poor coverage. Finally, we illustrated the utility of the CIs for the CCC in an example of a wake after sleep onset (WASO) biomarker, which is frequently used in clinical sleep studies of drugs for treatment of insomnia.

Quantifying temporal correlations: a test-retest evaluation of functional connectivity in resting-state fMRI.

There have been many interpretations of functional connectivity and proposed measures of temporal correlations between BOLD signals across different brain areas. These interpretations yield from many studies on functional connectivity using resting-state fMRI data that have emerged in recent years. However, not all of these studies used the same metrics for quantifying the temporal correlations between brain regions. In this paper, we use a public-domain test-retest resting-state fMRI data set to perform a systematic investigation of the stability of the metrics that are often used in resting-state functional connectivity (FC) studies. The fMRI data set was collected across three different sessions. The second session took place approximately eleven months after the first session, and the third session was an hour after the second session. The FC metrics composed of cross-correlation, partial cross-correlation, cross-coherence, and parameters based on an autoregressive model. We discussed the strengths and weaknesses of each metric. We performed ROI-level and full-brain seed-based voxelwise test-retest analyses using each FC metric to assess its stability. For both ROI-level and voxel-level analyses, we found that cross-correlation yielded more stable measurements than the other metrics. We discussed the consequences of this result on the utility of the FC metrics. We observed that for negatively correlated ROIs, their partial cross-correlation is shrunk towards zero, thus affecting the stability of their FC. For the present data set, we found greater stability in FC between the second and third sessions (one hour between sessions) compared to the first and second sessions (approximately 11months between sessions). Finally, we report that some of the metrics showed a positive association between strength and stability. In summary, the results presented in this paper suggest important implications when choosing metrics for quantifying and assessing various types of functional connectivity for resting-state fMRI studies.

Efficacy and anti-inflammatory analysis of glucocorticoid, antihistamine and leukotriene receptor antagonist in the treatment of allergic rhinitis.

BACKGROUND: There are many adverse reactions in the treatment of allergic rhinitis (AR) mainly with conventional drugs. Leukotriene receptor antagonists, glucocorticoids and nasal antihistamines can all be used as first-line drugs for AR, but the clinical effects of the three drugs are not clear. AIM: To examine the impact of glucocorticoids, antihistamines, and leukotriene receptor antagonists on individuals diagnosed with AR, specifically focusing on their influence on serum inflammatory indexes. METHODS: The present retrospective study focused on the clinical data of 80 patients diagnosed and treated for AR at our hospital between May 2019 and May 2021. The participants were categorized into the control group and the observation group. The control group received leukotriene receptor antagonists, while the observation group was administered glucocorticoids and antihistamines. Conducted an observation and comparison of the symptoms, physical sign scores, adverse reactions, and effects on serum inflammatory indexes in two distinct groups of patients, both before and after treatment. RESULTS: Subsequent to treatment, the nasal itching score, sneeze score, runny nose score, nasal congestion score, and physical signs score exhibited notable discrepancies (P < 0.05), with the observation group demonstrating superior outcomes compared to the control group (P < 0.05). The interleukin (IL)-6, IL-10, tumor necrosis factor-alpha, Soluble Intercellular Adhesion Molecule-1, Leukotriene D4 after treatment were significantly different and the observation group It is better than the control group, which is statistically significant (P < 0.05). Following the intervention, the incidence of adverse reactions in the observation group, including symptoms such as nasal dryness, discomfort in the throat, bitter taste in the mouth, and minor erosion of the nasal mucosa, was found to be 7.5%. This rate was significantly lower compared to the control group, which reported an incidence of 27.5%. The difference between the two groups was statistically significant (P < 0.05). CONCLUSION: Glucocorticoids and antihistamines have obvious therapeutic effects, reduce serum inflammatory index levels, relieve symptoms and signs of patients, and promote patients' recovery, which can provide a reference for clinical treatment of AR.

Disease Control with Upadacitinib in Patients with Psoriatic Arthritis: A Post Hoc Analysis of the Randomized, Placebo-Controlled SELECT-PsA 1 and 2 Phase 3 Trials.

INTRODUCTION: Low disease activity (LDA)/remission is the target of treatment in patients with psoriatic arthritis (PsA). We assessed the proportions of patients with PsA receiving upadacitinib who achieved LDA/remission over 1 year. METHODS: This was a post hoc analysis of the double-blind, placebo-controlled SELECT-PsA 1 (also adalimumab-controlled) and SELECT-PsA 2 trials. Treatment targets assessed included LDA/remission defined by Disease Activity in Psoriatic Arthritis (≤ 14/ ≤ 4) and Psoriatic Arthritis Disease Activity Scores (≤ 3.2/ ≤ 1.9), as well as minimal disease activity (MDA)/very low disease activity (VLDA) states (5/7 and 7/7 components, respectively, of MDA criteria). Targets were assessed at 24 and 56 weeks. For binary outcomes, non-responder imputation was used for missing data. Data from patients receiving upadacitinib 30 mg was not included in the analysis. RESULTS: Overall, 1386 patients were analyzed. Disease control (i.e., LDA/MDA) was achieved at 24 weeks in upadacitinib 15 mg-treated patients across both studies: LDA/MDA was achieved by 25-48% of patients receiving upadacitinib 15 mg versus 2-16% of patients receiving placebo, and remission/VLDA rates were 7-14% with upadacitinib 15 mg versus 0-4% with placebo. The proportions of patients achieving treatment targets were numerically similar to upadacitinib 15 mg and adalimumab. All responses were sustained at 56 weeks. CONCLUSIONS: Remission and LDA are feasible targets with upadacitinib treatment in patients with PsA. TRIAL REGISTRATION: ClinicalTrial.gov identifiers NCT03104400 (SELECT-PsA 1) and NCT03104374 (SELECT-PsA 2).

Psoriatic arthritis is a disease that causes inflammation of the skin and joints. Doctors measure how bad a patient's disease is by measuring signs and symptoms of the disease, and using these to make a "score." The aim of treatment is to reduce the score to low levels (known as "low disease activity") or very low levels ("remission"). This study looked at results from two clinical trials that compared upadacitinib, a medicine used to treat psoriatic arthritis, with no medicine (placebo) to see how many patients had low disease activity or were in remission after 1 year of treatment. The results showed that more patients who were taking upadacitinib had low disease activity or were in remission after the first 6 months of treatment compared with those who took placebo. This difference between upadacitinib and placebo could still be seen after 1 year of treatment. These results show that treatment with upadacitinib is effective enough for some patients with psoriatic arthritis to achieve low disease activity or remission and to stay at this level, even after more than 1 year of treatment.

Analyzing longitudinal binary data in clinical studies.

In clinical studies, it is common to have binary outcomes collected over time as repeated measures. This manuscript reviews and evaluates two popular classes of statistical methods for analyzing binary response data with repeated measures: likelihood-based Generalized Linear Mixed Model (GLMM), and semiparametric Generalized Estimating Equation (GEE). Recommendations for choice of analysis model and points to consider for implementation in clinical studies in the presence of missing data are provided based on a comprehensive literature review, as well as, a simulation study evaluating the performance of both GLMM and GEE under scenarios representative of typical clinical trial settings. Under Missing at Random (MAR) assumption, GLMM is preferred over GEE, and the SAS PROC GLIMMIX marginal model is recommended for implementing GLMM in analyzing clinical trial data. When there is an underlying continuous variable used to define the binary response, and the missing proportion is high and/or unbalanced between treatment groups, a two-step approach combining Multiple Imputation (MI) and GEE (MI-GEE) is recommended.

Effect of upadacitinib on reducing pain in patients with active psoriatic arthritis or ankylosing spondylitis: post hoc analysis of three randomised clinical trials.

OBJECTIVE: Evaluate the effect of upadacitinib on pain outcomes in patients with active psoriatic arthritis (PsA) or ankylosing spondylitis (AS) across 3 randomised trials (SELECT-PsA 1 and 2 for PsA; SELECT-AXIS 1 for AS). METHODS: Patients were randomised to upadacitinib 15 mg once daily or placebo (all 3 studies), or adalimumab 40 mg every other week (SELECT-PsA 1 only). Pain outcomes included proportion of patients achieving ≥30%, ≥50% and ≥70% reduction from baseline in patient global assessment of pain and other end points. RESULTS: A higher proportion of patients receiving upadacitinib versus placebo achieved ≥30%, ≥50% and ≥70% reduction in pain end points as early as week 2; these improvements with upadacitinib were generally sustained or increased through year 1 (PsA 1/2 studies: 64%/48%, 58%/42% and 38%/22%, respectively; SELECT-AXIS 1 study: 76%, 72% and 54%). Results were similar with adalimumab in PsA 1 (59%, 49% and 32%). Patients who switched from placebo to upadacitinib 15 mg were able to reach a similar level of improvement as the continuous upadacitinib groups by year 1 (PsA 1/2 studies: 46%-60%, 35%-49% and 15%-34%; AS study: 83%, 72% and 46%). Results were similar with other pain end points. CONCLUSION: Rapid and sustained improvements in pain outcomes across several end points were consistently shown with upadacitinib over 1 year in patients with active PsA or AS who had either inadequate response to prior non-biologic or biologic disease-modifying antirheumatic drugs (PsA studies) or were biologic-naïve with inadequate response to non-steroidal anti-inflammatory drugs (AS study).