Achievement of the National Psoriasis Foundation treatment targets with ixekizumab: Pooled analyses from 4 clinical studies.

BACKGROUND: The National Psoriasis Foundation (NPF) published treatment targets for US patients with plaque psoriasis. However, data are lacking on how well existing therapies help achieve these goals. OBJECTIVE: To examine the ability of an interleukin 17 inhibitor, ixekizumab, in achieving these treatment targets. METHODS: Post hoc analysis was performed on pooled data from 4 phase III clinical trials assessing ixekizumab for plaque psoriasis: the UNCOVER-1, -2, and -3 trials and the IXORA-S trial. Treatment response was evaluated using the NPF-defined acceptable response (affected body surface area [BSA] of 3% or less or BSA improvement of 75% or higher at 12 weeks of treatment) and target response (BSA of 1% or less at 12 weeks and every 6 months thereafter). RESULTS: In the UNCOVER trials (n = 2701), acceptable and target response rates at week 12 were 73.9% and 51.8% with ixekizumab 80 mg every 2 weeks, 35.7% and 14.9% with etanercept 50 mg, and 3.0% and 0.6% with placebo, respectively. In the IXORA-S trial (n = 302), acceptable and target response rates at week 12 were significantly higher with ixekizumab every 2 weeks versus ustekinumab (acceptable response 68.4% vs 38.6%, P < .0001; target response 50.7% vs 24.1%, P < .0001). LIMITATIONS: Data were from controlled studies and may not reflect real-world practice. CONCLUSION: The majority of patients treated with ixekizumab in 4 phase III clinical trials achieved NPF, patient-centered treatment targets.

A comparison of different statistical methods analyzing hypoglycemia data using bootstrap simulations.

Hypoglycemia has long been recognized as a major barrier to achieving normoglycemia with intensive diabetic therapies. It is a common safety concern for the diabetes patients. Therefore, it is important to apply appropriate statistical methods when analyzing hypoglycemia data. Here, we carried out bootstrap simulations to investigate the performance of the four commonly used statistical models (Poisson, negative binomial, analysis of covariance [ANCOVA], and rank ANCOVA) based on the data from a diabetes clinical trial. Zero-inflated Poisson (ZIP) model and zero-inflated negative binomial (ZINB) model were also evaluated. Simulation results showed that Poisson model inflated type I error, while negative binomial model was overly conservative. However, after adjusting for dispersion, both Poisson and negative binomial models yielded slightly inflated type I errors, which were close to the nominal level and reasonable power. Reasonable control of type I error was associated with ANCOVA model. Rank ANCOVA model was associated with the greatest power and with reasonable control of type I error. Inflated type I error was observed with ZIP and ZINB models.

A Bayesian approach to the statistical analysis of device preference studies.

Drug delivery devices are required to have excellent technical specifications to deliver drugs accurately, and in addition, the devices should provide a satisfactory experience to patients because this can have a direct effect on drug compliance. To compare patients' experience with two devices, cross-over studies with patient-reported outcomes (PRO) as response variables are often used. Because of the strength of cross-over designs, each subject can directly compare the two devices by using the PRO variables, and variables indicating preference (preferring A, preferring B, or no preference) can be easily derived. Traditionally, methods based on frequentist statistics can be used to analyze such preference data, but there are some limitations for the frequentist methods. Recently, Bayesian methods are considered an acceptable method by the US Food and Drug Administration to design and analyze device studies. In this paper, we propose a Bayesian statistical method to analyze the data from preference trials. We demonstrate that the new Bayesian estimator enjoys some optimal properties versus the frequentist estimator.

Evaluation of Changes in Skin and Joint Outcomes and Associated Treatment Changes in Psoriatic Arthritis (PsA): Experience From the Corrona PsA/SpA Registry.

OBJECTIVE: To characterize skin severity and joint activity outcomes and associated treatment changes in patients with psoriatic arthritis (PsA) through 12 months of follow-up after enrollment in the Corrona Psoriatic Arthritis/Spondyloarthritis (PsA/SpA) Registry. METHODS: Patients ≥ 18 years of age with a diagnosis of PsA and a history of psoriasis between March 21, 2013, and September 30, 2016, were enrolled (n = 647). Demographics, clinical features, and treatment characteristics were collected and stratified by skin severity and joint activity. Change in joint and skin from enrollment to the 12-month visit was classified by change in category of Clinical Disease Activity Index (CDAI) or body surface area (BSA). Tests of association evaluated the relationship between changes in therapy and changes in skin severity and joint activity. RESULTS: Patients with improvement in both joint activity and skin severity saw the largest median reduction in both CDAI and BSA, while those who worsened in both had the greatest median increase in both CDAI and BSA. The majority of PsA patients (> 50%) had no change in skin severity regardless if they had reduced therapy (50%), no therapy changes (54%), or increased therapy (56%; P = 0.5875). However, there was a significant association between changes in therapy and changes in joint activity (P < 0.001). Patients who increased therapy were more likely to have improvement in joint activity (32%) compared to patients who reduced therapy (22%) or had no therapy changes (11%). CONCLUSION: The clinical implication for our findings suggests the assessment and incorporation of both skin and joint components may be advisable.

Understanding the association between skin involvement and joint activity in patients with psoriatic arthritis: experience from the Corrona Registry.

Objective: To compare the characteristics of patients with psoriatic arthritis among patient groups stratified by degree of skin and joint involvement, and to evaluate the relationship between skin severity and joint activity. Methods: Body surface area (BSA) and Clinical Disease Activity Index (CDAI) at enrolment were analysed. Patient characteristics were stratified by skin severity and joint activity. Baseline patient characteristics, clinical and disease characteristics and patient-reported outcomes were compared. The strength of the relationship of skin severity and joint activity was evaluated using methods for categorical variables (χ2 test, Cramer's V) and continuous variables (linear regression). Results: 1542 adult patients in the Corrona Psoriatic Arthritis/Spondyloarthritis Registry enrolled between 21 May 2013 and 20 September 2016 were analysed. Most patients in the BSA >3%/CDAI moderate/high subgroup had worse clinical and patient-reported outcomes. A significant (p<0.001) modest association (Cramer's V=0.1639) between skin severity and joint activity was observed among all patients at enrolment. Patients with higher skin severity were two times more likely to have higher joint involvement (OR 2.27, 95% CI 1.71 to 3.01). A significant linear relationship between CDAI and BSA was observed. Effect modification showed this linear relationship was modified by age, gender, insurance, work status, current therapy, Health Assessment Questionnaire, Nail visual analogue scale, minimal disease activity, dactylitis count, patient-reported pain and fatigue. Conclusion: Skin severity is modestly correlated with joint activity, and patients with higher skin severity are two times more likely to have increased joint involvement. Clinicians need to address both skin severity and joint activity in treatment decisions.

Crossover design and its application in late-phase diabetes studies.

Crossover design has been widely used in late-phase clinical studies, as well as in pharmacokinetic and pharmacodynamic, bioequivalence, and medical device studies; however, its interpretability and applicability continue to be debated. Herein we provide discussions around a crossover design's scientific benefit, applicability, and how it can be implemented in late-phase diabetes studies by properly handling key issues: carryover effect, washout period, and baseline selection. Specifically, detailed considerations are provided about the validity and situations of having appropriate length of study duration to deal with carryover effects so that a washout period may not be needed. A simulation study and data mining results on 12 crossover late-phase insulin clinical trials are presented to examine the discussion points and proposals.