Sparse item testing of clinical scales in neurology trials to alleviate burden to patients.

Background: Neurology trials typically rely on composite scales for measuring symptom severity. Completing all items in a long scale can be burdensome for patients, caregivers, and trial personnel.

Objectives: To test the hypothesis that sparse item testing, aided by item-response modelling, can preserve the power for detecting treatment effect in a controlled trial.

Tofacitinib pharmacokinetics in children and adolescents with juvenile idiopathic arthritis.

These analyses characterized tofacitinib pharmacokinetics (PKs) in children and adolescents with juvenile idiopathic arthritis (JIA). Data were pooled from phase I (NCT01513902), phase III (NCT02592434), and open-label, long-term extension (NCT01500551) studies of tofacitinib tablet/solution (weight-based doses administered twice daily [b.i.

Modeling of Survival and Frequency of Cardiovascular-Related Hospitalization in Patients with Transthyretin Amyloid Cardiomyopathy Treated with Tafamidis.

Introduction: ATTR-ACT (Tafamidis in Transthyretin Cardiomyopathy Clinical Trial) demonstrated the efficacy and safety of tafamidis in transthyretin amyloid cardiomyopathy (ATTR-CM). Model-based analyses from ATTR-ACT can examine predictor effects on dose-response/exposure-response relationships.

Methods: Parametric hazard distributions were developed for all-cause mortality and frequency of cardiovascular-related hospitalization.

Population Pharmacokinetics of Tofacitinib in Patients With Moderate to Severe Ulcerative Colitis.

Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). We characterized tofacitinib pharmacokinetics in patients with moderate to severe UC, and the effects of covariates on variability in pharmacokinetic parameter estimates. Data were pooled from 1 8-week phase 2 and 2 8-week phase 3 induction studies, and a 52-week phase 3 maintenance study (N = 1096).

Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies.

Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power.