Jeff Maca

Enteric-coated mycophenolate sodium is therapeutically equivalent to mycophenolate mofetil in de novo renal transplant patients.

The introduction of mycophenolate mofetil (MMF) represented a major advance in transplant medicine, although optimal use may be limited by gastrointestinal (GI) side‐effects. An enteric‐coated formulation of mycophenolate sodium (EC‐MPS; myfortic®) has been developed with the aim of improving the upper GI tolerability of mycophenolic acid. Therapeutic equivalence of EC‐MPS (720 mg b.i.d.) and MMF (1000 mg MMF b.i.d.), with concomitant cyclosporine microemulsion (Neoral®) and corticosteroids, was assessed in 423 de novo kidney transplant patients recruited to a 12‐month, double‐blind study. Efficacy failure (biopsy‐proven acute rejection [BPAR], graft loss, death or loss to follow up) at 6 months (EC‐MPS 25.8% vs. MMF 26.2%; 95% CI: [−8.7, +8.0]) demonstrated therapeutic equivalence. At 12 months, the incidence of BPAR, graft loss or death was 26.3% and 28.1%, and of BPAR alone was 22.5% and 24.3% for EC‐MPS and MMF, respectively. Among those with BPAR, the incidence of severe acute rejection was 2.1% with EC‐MPS and 9.8% with MMF (p = ns). The safety profile and incidence of GI adverse events were similar for both groups. Within 12 months, 15.0% of EC‐MPS patients and 19.5% of MMF patients required dose changes for GI adverse events (p = ns). Enteric‐coated‐MPS 720 mg b.i.d. is therapeutically equivalent to MMF 1000 mg b.i.d. with a comparable safety profile.

Adaptive seamless Phase II/III designs : Background, operational aspects, and examples

Adaptive seamless designs have been considered as one possible way to shorten the time and patient exposure necessary to discover, develop, and demonstrate the benefits of a new drug. We introduce the concept of adaptive designs and describe the current statistical methodologies that relate to adaptive seamless designs. We also describe the decision process involved with seamless designs and present some illustrative examples.

Multiple Co-primary Endpoints: Medical and Statistical Solutions: A Report from the Multiple Endpoints Expert Team of the Pharmaceutical Research and Manufacturers of America

There are quite a few disorders for which regulatory agencies have required a treatment to demonstrate a statistically significant effect on multiple endpoints, each at the one-sided 2.5% level, before accepting the treatments efficacy for the disorders. Depending on the correlation among the endpoints, this requirement could lead to a substantial reduction in the studys power to conclude the efficacy of a treatment. To investigate the prevalence of this requirement and propose possible solutions, a multiple-disciplinary Multiple Endpoints Expert Team sponsored by Pharmaceutical Research and Manufacturers of America was formed in November 2003. The team recognized early that many researchers were not fully aware of the implications of requiring multiple co-primary endpoints. The team proposes possible solutions from both the medical and the statistical perspectives. The optimal solution is to reduce the number of multiple co-primary endpoints. If after careful considerations, multiple co-primary endpoints remain a scientific requirement, the team proposes statistical solutions and encourages that regulatory agencies be receptive to approaches that adopt modest upward adjustments of the nominal significance levels for testing individual endpoints. Finally, the team hopes that this report will draw more attention to the problem of multiple co-primary endpoints and stimulate further research.

Good Practices for Adaptive Clinical Trials in Pharmaceutical Product Development

This article is a summary of good adaptive practices for the planning and implementation of adaptive designs compiled from experiences gained in the pharmaceutical industry. The target audience is anyone involved in the planning and execution of clinical trials. The first step prior to planning an adaptive design is to assess the appropriateness of its use. Hence, strategic points to consider when assessing if an adaptive design is the right choice for a trial are discussed. In addition, strategic points for consideration at the design and implementation stage are included from operational, regulatory, clinical, and statistical perspectives. Good practices for trial simulation, trial documentation, and data monitoring committees are provided.

Barriers and opportunities for implementation of adaptive designs in pharmaceutical product development.

Background This review discusses barriers to implementing adaptive designs in a pharmaceutical R&D environment and provides recommendations on how to overcome challenges. A summary of findings from a survey conducted through PhRMA’s working group on adaptive designs is followed by a report based on our experience as statistical and clinical consultants to project teams charged with establishing the clinical development strategy for investigational compounds and interested in applying innovative approaches. Findings and recommendations Adaptive designs require additional work in that clinical trial simulations are needed to develop the design. Some project teams, due to time and resource constraints, are unable to invest the additional effort required to conduct necessary scenario analyses of options through simulation. We recommend formally integrating the planning time for scenario analyses and to incentivize optimal designs (e.g., designs offering the highest information value per resource unit invested). Regardless of the trial design ultimately chosen, quantitatively comparing alternative trial design options through simulation will enable earlier and better decision making in the context of the overall clinical development plan. Adhering to ‘Good Adaptive Practices’ will be key to achieving this goal. Outlook Implementing adaptive designs efficiently requires top—down and bottom— up support and the willingness to invest into integrated process and information technology infrastructures. Success is conditional on the willingness of the R&D environment to embrace the implementation of adaptive designs as a Change Management Initiative in the spirit of the Critical Path of the Food and Drug Administration. Clinical Trials 2010; 7: 167—173. http://ctj.sagepub.com

Reconsidering some aspects of the two-trials paradigm.

A common standard for the demonstration of efficacy in a clinical submission is a statistically significant outcome in at least two pivotal trials (“two-trials convention”). When the data structures in different trials are sufficiently similar to allow pooling of the data across trials for a combined analysis, we argue here that such an analysis is a more logical and efficient basis for a judgment regarding efficacy. Criteria for combined analyses may be established, which ensure the same false positive rate protection as the two-trials convention. A combined analysis will generally have much more power than the corresponding application of the two-trials approach that has the same false positive rate protection. In addition, we describe the behavior of modified versions of pure combined analysis, which incorporate a formal standard for reproducibility of trial results by limiting the larger of the individual trial p-values. These modifications are shown to maintain the desirable behavior of the pure combined analysis, namely, higher power compared to the two-trials convention.

Evaluation and Review of Strategies to Assess Cardiovascular Risk in Clinical Trials in Patients with Type 2 Diabetes Mellitus

This article is a result of the efforts of the American Statistical Association Biopharmaceutical Section Working Group on Safety. With representatives from different institutions, this group reviewed the drugs approved by the United States Food and Drug Administration (FDA) to treat Type 2 diabetes mellitus during 2002–2014 with a focus on the cardiovascular (CV) risk assessment. The main objective of this article is to understand the impact of FDA guidance of 2008 on assessment of CV risk in antidiabetes development programs, which are summarized and displayed in chronological order. Compared to New Drug Applications (NDAs) submitted prior to the FDA 2008 guidance, the number of patient-years significantly increased for NDAs approved in the post-guidance era. To meet guidance requirements on CV risk assessment, meta-analyses and large cardiovascular outcome trials (CVOTs) have been conducted. These CVOTs provide an opportunity to assess safety signals beyond CV risk and assess the benefit/risk ratio better in diabetic patients with a high risk for CV events, but they also present challenges. The advantages and disadvantages of different CV assessment strategies are summarized in this manuscript. Finally, we raise some emerging questions and discuss future opportunities for CV risk assessment research. Supplementary materials for this article are available online.

A Review of: “Adaptive Design Methods in Clinical Trials, by S.-C. Chow and Mark Chang”

Adaptive designs have been the focus of a considerable amount of statistical research over the last few years, and there have been many publications in scientific journals pertaining to the statistical and logistical considerations of conducting such trials. There have also been special issues of Journal of Biopharmaceutical Statistics (Volume 15, Number 4, 2005), Drug Information Journal (Volume 40, Number 4, 2006), and Biometrics (Volume 62, Number 3, 2006) devoted to the topic of adaptive designs. This book is the first book devoted exclusively to adaptive designs, which attempts to consolidate what has been written about adaptive designs, and be a reference for the subject. The book consists of 12 chapters. The first chapter is an introduction, which introduces the basic definitions of adaptive designs, designs and the different types of adaptive designs, which will be discussed in the book. There is a short description of each type of design, and each design is covered in more detail in subsequent chapters. The second chapter refers to one type of change to a protocol, which is a protocol amendment. This chapter contains detailed derivations of statistical methodology to test whether the protocol amendment resulted in a change in the underlying population which was enrolled into the study prior to and after the amendment was implemented. This section contains very detailed mathematical derivations pertaining to methods for testing if there was a change in enrollment populations. This chapter does not give guidance on when these methods would be required. Would these methods be used for preplanned protocol amendments, or only those which arise out of necessity? This section also has detailed derivations for sample size formulas for the standard tests in clinical trials, which can be found in many textbooks. This chapter also appears to contain many mathematical derivations, which have limited benefit to the reader. Most of these derivations are not related to the field of adaptive designs, since one basic definition of an adaptive design is that there will be a modification to the design of the trial, without the use of a protocol amendment. The third chapter discusses the topic of adaptive randomization, which has a long history in the statistical literature. This chapter reviews the basic adaptive randomization techniques, including treatment-adaptive techniques (e.g., Efron’s biased coin model, Lachin’s urn model, etc.), covariate-adaptive techniques (Zelen’s model, Pocock-Simon’s model, etc.), and response-adaptive techniques, such as play-the-winner models. This chapter reviews the literature of the subject, and