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for the PCOSAct Study Group IMPORTANCE Acupuncture is used to induce ovulation in some women with polycystic ovary syndrome, without supporting clinical evidence.OBJECTIVE To assess whether active acupuncture, either alone or combined with clomiphene, increases the likelihood of live births among women with polycystic ovary syndrome. DESIGN, SETTING, AND PARTICIPANTSA double-blind (clomiphene vs placebo), single-blind (active vs control acupuncture) factorial trial was conducted at 21 sites (27 hospitals) in mainland China between July 6, 2012, and November 18, 2014, with 10 months of pregnancy follow-up until October 7, 2015. Chinese women with polycystic ovary syndrome were randomized in a 1:1:1:1 ratio to 4 groups.INTERVENTIONS Active or control acupuncture administered twice a week for 30 minutes per treatment and clomiphene or placebo administered for 5 days per cycle, for up to 4 cycles. The active acupuncture group received deep needle insertion with combined manual and low-frequency electrical stimulation; the control acupuncture group received superficial needle insertion, no manual stimulation, and mock electricity. CONCLUSIONS AND RELEVANCE Among Chinese women with polycystic ovary syndrome, the use of acupuncture with or without clomiphene, compared with control acupuncture and placebo, did not increase live births. This finding does not support acupuncture as an infertility treatment in such women. MAIN OUTCOMES AND MEASURES

The proactive approach we advocate has the potential to benefit patients and health care providers by providing more comprehensive safety information at the time of new product marketing and beyond.

Children represent a large underserved population of "therapeutic orphans," as an estimated 80% of children are treated off-label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or "borrowing") of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure-response information for antiepileptic drugs to pediatrics.

Assessment for joint disease in psoriasis patients being treated at dermatology clinics may facilitate earlier psoriatic arthritis diagnosis and treatment initiation, which may prevent disability and other negative impacts.

These data from routine rheumatology clinical practice settings highlight the effectiveness of common biologic and DMARD therapies, and provide additional data beyond those of randomized, controlled trials.

Continuous etanercept treatment provided greater sustained improvements in PROs than interrupted therapy; however, interrupting etanercept therapy, if needed, has predictable and manageable effects.

Summary Recent guidance from the Food and Drug Administration for the evaluation of new therapies in the treatment of type 2 diabetes (T2DM) calls for a program-wide meta-analysis of cardiovascular (CV) outcomes. In this context, we develop a new Bayesian meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for meta-analysis clinical trial design with a focus on controlling the type I error and power. We also propose the partial borrowing power prior to incorporate the historical survival meta data into the statistical design. Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a simulation-based algorithm for computing various quantities, such as the power and the type I error in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies.