Rapid pruritus reduction with ruxolitinib cream treatment in patients with atopic dermatitis.

BACKGROUND: Ruxolitinib cream is a topical formulation of ruxolitinib, a Janus kinase (JAK) 1/JAK2 inhibitor. OBJECTIVES: To report timing and magnitude of effect of ruxolitinib cream on itch in patients with atopic dermatitis (AD), a highly pruritic inflammatory skin disease. METHODS: Two phase 3 trials (TRuE-AD1 [NCT03745638]/TRuE-AD2 [NCT03745651]) enrolled patients aged ≥12 years with AD for ≥2 years, Investigator's Global Assessment score of 2 or 3, and 3%-20% affected body surface area. Patients (total N = 1249; median age, 32 years) were randomised (2:2:1) to twice daily 0.75% ruxolitinib cream, 1.5% ruxolitinib cream or vehicle cream for 8 weeks of double-blinded treatment. Worst itch was measured using the numerical rating scale (NRS). RESULTS: Significantly more patients who applied ruxolitinib cream (either strength) achieved a ≥2-point itch reduction (NRS2) within approximately 12 h versus vehicle (0.75%/1.5% ruxolitinib cream, 16.3%/13.1%; vehicle, 6.9%; both P < 0.05), with further improvements through Week 8 (58.3%/65.1% vs 29.4%; both P < 0.0001). A ≥4-point itch reduction (NRS4) was achieved by significantly more patients who applied 0.75%/1.5% ruxolitinib cream versus vehicle by Day 2 (8.9%/11.2% vs 2.1%; P < 0.005); higher rates were observed at Week 8 (41.5%/51.5% vs 15.8%; P < 0.0001). Median time for the 0.75%/1.5% ruxolitinib cream groups to achieve NRS4 from baseline was 15.0/13.0 days; this endpoint was not reached by the vehicle group. CONCLUSIONS: Ruxolitinib cream demonstrated rapid improvement in itch in patients with mild to moderate AD that was sustained for 8 weeks. Significantly more patients applying ruxolitinib cream achieved itch NRS2 within approximately 12 h and itch NRS4 by Day 2 versus vehicle.

Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk.

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants.

Markov Transition Model to Dementia with Death as a Competing Event.

This study evaluates the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. A multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data; transitions among states depend on four covariates age, education, prior state (intact cognition, or M.C.I., or G.I.) and the presence/absence of an apolipoprotein E-4 allele (APOE4). A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the survival from death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Simulation studies determine the sensitivity of the maximum likelihood estimates to the violations of the Weibull and Cox PH model assumptions. Results are illustrated with an application to the Nun Study, a longitudinal cohort of 672 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.