This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two‐stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment difference . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for all in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.

With rising costs and prolonged timelines for drug development, more innovative trial designs are critical to improve efficiency. Use of available historical control data in a new trial can reduce the number of control patients and accordingly reduce costs and timelines. A major limitation of historical data borrowing is potential prior-data conflict. This difference can increase false decision rates and confound the outcome interpretation. The potential inflation in both type I error rate and type II error rate should be clearly characterized and controlled during the trial design stage. In this paper, we develop a simple approach to incorporating historical control data in clinical trial design and analysis. First we provide a simple statistical approach to evaluating design properties when using a Bayesian approach to incorporating historical data. We then propose a six-step process for trial design including selection and summarization of historical control data, sample size determination with and without borrowing, design property evaluation, and how much historical data should be borrowed to control false positive/negative rate inflation based on trial variability. A detailed procedure to select historical control data is also provided. Finally, we use an example to illustrate our approach. The simplicity of methodology (no simulation required), the streamlined processes for data selection, and explicit evaluation of the impact of prior-data conflict on type I error rate and power make the proposed approach statistically rigorous and easy to understand and implement.

Response adaptive randomization (RAR) is appealing from methodological, ethical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. However, applications of RAR in confirmatory drug clinical trials with multiple active arms are limited largely due to its complexity, and lack of control of randomization ratios to different treatment groups. To address the aforementioned issues, we propose a Response Adaptive Block Randomization (RABR) design allowing arbitrarily prespecified randomization ratios for the control and high-performing groups to meet clinical trial objectives. We show the validity of the conventional unweighted test in RABR with a controlled type I error rate based on the weighted combination test for sample size adaptive design invoking no large sample approximation. The advantages of the proposed RABR in terms of robustly reaching target final sample size to meet regulatory requirements and increasing statistical power as compared with the popular Doubly Adaptive Biased Coin Design are demonstrated by statistical simulations and a practical clinical trial design example.

In a drug development program, the efficacy and safety of multiple doses can be evaluated in patients through a phase 2b dose ranging study. With a demonstrated dose response in the trial, promising doses are identified. Their effectiveness then is further investigated and confirmed in phase 3 studies. Although this two-step approach serves the purpose of the program, in general, it is inefficient because of its prolonged development duration and the exclusion of the phase 2b data in the final efficacy evaluation and confirmation which are only based on phase 3 data. To address the issue, we propose a new adaptive design, which seamlessly integrates the dose finding and confirmation steps under one pivotal study. Unlike existing adaptive seamless phase 2b/3 designs, the proposed design combines the response adaptive randomization, sample size modification, and multiple testing techniques to achieve better efficiency. The design can be easily implemented through an automated randomization process. At the end, a number of targeted doses are selected and their effectiveness is confirmed with guaranteed control of family-wise error rate.

Response adaptive randomization is appealing in confirmatory adaptive clinical trials from statistical, ethical, and pragmatic perspectives, in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. The Doubly Adaptive Biased Coin Design (DBCD) is a popular solution due to its asymptotic normal property of final allocations, which further justifies its asymptotic type I error rate control. As an alternative, we propose a Response Adaptive Block Randomization (RABR) design with pre-specified randomization ratios for the control and high-performing groups to robustly achieve desired final sample size per group under different underlying responses, which is usually required in industry-sponsored clinical studies. We show that the usual test statistic has a controlled type I error rate. Our simulations further highlight the advantages of the proposed design over the DBCD in terms of consistently achieving final sample allocations and of power performance. We further apply this design to a Phase III study evaluating the efficacy of two dosing regimens of adjunctive everolimus in treating tuberous sclerosis complex but with no previous dose-finding studies in this indication.

In clinical trials, sample size reestimation is a useful strategy for mitigating the risk of uncertainty in design assumptions and ensuring sufficient power for the final analysis. In particular, sample size reestimation based on unblinded interim effect size can often lead to sample size increase, and statistical adjustment is usually needed for the final analysis to ensure that type I error rate is appropriately controlled. In current literature, sample size reestimation and corresponding type I error control are discussed in the context of maintaining the original randomization ratio across treatment groups, which we refer to as “proportional increase.” In practice, not all studies are designed based on an optimal randomization ratio due to practical reasons. In such cases, when sample size is to be increased, it is more efficient to allocate the additional subjects such that the randomization ratio is brought closer to an optimal ratio. In this research, we propose an adaptive randomization ratio change when sample size increase is warranted. We refer to this strategy as “nonproportional increase,” as the number of subjects increased in each treatment group is no longer proportional to the original randomization ratio. The proposed method boosts power not only through the increase of the sample size, but also via efficient allocation of the additional subjects. The control of type I error rate is shown analytically. Simulations are performed to illustrate the theoretical results.

Adaptive sample size designs, including group sequential designs, have been used as alternatives to fixed sample size designs to achieve more robust statistical power and better trial efficiency. This work investigates the efficiency of adaptive sample size designs as compared to group sequential designs. We show that given a group sequential design, a uniformly more efficient adaptive sample size design based on the same maximum sample size and rejection boundary can be constructed. While maintaining stable statistical power at the required level, the expected sample size of the obtained adaptive sample size design is uniformly smaller than that of the group sequential design with respect to a range of the true treatment difference. The finding provides further insights into the efficiency of adaptive sample size designs and challenges the popular belief of better efficiency associated with group sequential designs. Good adaptive performance plus easy implementation and other desirable operational features make adaptive sample size designs more attractive and applicable to modern clinical trials.

Flexible sample size designs, including group sequential and sample size re-estimation designs, have been used as alternatives to fixed sample size designs to achieve more robust statistical power and better trial efficiency. In this work, a new representation of sample size re-estimation design suggested by Cui et al. [5,6] is introduced as an adaptive group sequential design with flexible timing of sample size determination. This generalized adaptive group sequential design allows one time sample size determination either before the start of or in the mid-course of a clinical study. The new approach leads to possible design optimization on an expanded space of design parameters. Its equivalence to sample size re-estimation design proposed by Cui et al. provides further insight on re-estimation design and helps to address common confusions and misunderstanding. Issues in designing flexible sample size trial, including design objective, performance evaluation and implementation are touched upon with an example to illustrate.

Introduction: Loss of exclusivity for biological therapeutics opens the door for biosimilar development. Biosimilars must demonstrate structural, functional, and clinical similarity with a currently approved biological originator product. A therapeutic alternative for biologic-naive patients, a single switch from an originator to biosimilar has also been studied in clinically stable patients; further, switching therapy multiple times (alternating) between an originator and a biosimilar has been investigated. Because biosimilars are not identical to originators and no robust clinical data have convincingly demonstrated that switching or alternating therapy of stable patients is safe and efficacious, there is an imperative need to understand the characteristics of well-designed clinical trials to support these practices.Areas covered: Clinical trials of biosimilars are reviewed, with an emphasis on trial designs that incorporate therapy switching, including the NOR-SWITCH study as an example.Expert opinion: As currently designed, biosimilar clinical trials provide insufficient information to support switching or alternating between originator products and their biosimilars. Lack of regulatory guidance contributes to this void. More robust data are required to inform the safety and efficacy of switching or alternating therapies, particularly regarding immunogenicity risks. Studies that also include alternations of therapy are needed to address these knowledge gaps.

It is common in multiregional clinical development that data from a global trial and a local trial (in a target country) together will be used to support local filing in the target country. This approach is considered efficient drug development both globally and in the target country. However, it remains a challenge how to combine global trial data and local trial data toward local filing. To address this challenge, we propose an "interpretation-centric" evaluation criterion based on a weighted estimator that weights data from the target country and outside of the target country. This approach provides an unbiased estimate of a global treatment effect with appropriate representation of the target country patient population, where the "appropriate representation" is the desired proportion of the target country participants in a global trial and is measured by the weight parameter. This natural interpretation can facilitate drug development discussion with local regulatory agencies. Sample size of the local trial can be determined using the proposed weighted estimator. Approaches for weight determination are also discussed.

It is well recognized that sample size determination is challenging because of the uncertainty on the treatment effect size. Several remedies are available in the literature. Group sequential designs start with a sample size based on a conservative (smaller) effect size and allow early stop at interim looks. Sample size re‐estimation designs start with a sample size based on an optimistic (larger) effect size and allow sample size increase if the observed effect size is smaller than planned. Different opinions favoring one type over the other exist. We propose an optimal approach using an appropriate optimality criterion to select the best design among all the candidate designs. Our results show that (1) for the same type of designs, for example, group sequential designs, there is room for significant improvement through our optimization approach; (2) optimal promising zone designs appear to have no advantages over optimal group sequential designs; and (3) optimal designs with sample size re‐estimation deliver the best adaptive performance. We conclude that to deal with the challenge of sample size determination due to effect size uncertainty, an optimal approach can help to select the best design that provides most robust power across the effect size range of interest. Copyright © 2016 John Wiley & Sons, Ltd.

Interim analyses can be planned to make Go/No-Go decisions in late phase clinical trials and decision quality is an issue of interest because of the timing of interim analysis is often selected based on empirical experience and thresholds for interim Go/No-Go decisions are determined arbitrarily. There is no systematic research to investigate interrelationship among three commonly used statistical methods for interim decision-making, namely conditional power, predictive power, and predicted confidence interval methods. We used a receiver operating characteristic (ROC) approach to evaluate decision-making quality of these methods and they are proved to be equivalent analytically and verified by simulations. To achieve the pre-specified sensitivity and specificity for Go/No-Go decision-making at interim, the required minimum sample size for interim analysis and the threshold for each of three statistical methods can be systematically determined based on the target design parameters of the clinical trials. The application of the obtained results is given for continuous outcome measures.

Clinical trials can be enriched on subpopulations that may be more responsive to treatments to improve the chance of trial success. In 2012 FDA issued a draft guidance to facilitate enrichment design, where it pointed out the uncertainty on the subpopulation classification and on the treatment effect outside of the identified subpopulation. We consider a novel design strategy where the identified subpopulation (biomarker-positive) is augmented by some biomarker-negative patients. Specifically, after sufficiently powering biomarker-positive subpopulation we propose to enroll biomarker-negative patients, enough to assess the overall treatment benefit. We derive a weighted statistic for this assessment, correcting for the disproportionality of biomarker-positive and biomarker-negative subpopulations under enriched trial setting. Screening information is utilized for weight determination. This statistic is an unbiased estimate of the overall treatment effect as that in all-comer trials, and is the basis to power for the overall treatment effect. For analysis, testing will be first performed on biomarker-positive subpopulation; only if treatment benefit is established in this subpopulation will overall treatment effect be tested using the weighted statistic. This design approach differs from typical enrichment design or stratified all-comer design in that the former enrolls only biomarker-positive patients and the latter enrolls a regular all-comer population. It also differs from adaptive enrichment by maintaining the trial design and analysis priority on biomarker-positive subpopulation. Therefore the proposed approach not only warrants a high probability of trial success on biomarker-positive subpopulation, but also efficiently assesses the overall treatment effect in the presence of an uncertain treatment benefit among biomarker-negative patients.

Background Biologics, in particular monoclonal antibodies (mAbs), transformed treatment of autoimmune diseases such as rheumatoid arthritis (RA) by selectively targeting pathogenesis and modifying disease course. Many biosimilars are being developed–products similar to innovator based on quality, safety, and efficacy. Unlike generics, biosimilars are not exact copies of the innovator. Small changes in product characteristics (quality attributes [QAs]) could impart clinically important differences. Objectives We present a series of QAs for Humira® (adalimumab) from >500 drug substance batches produced from 4 facilities and 5 production scales from 2001-2013. Humira is approved for 10 indications by EMA, including RA, ankylosing spondylitis, and psoriatic arthritis. Methods Proportions of charged species act as markers for overall molecular charge, a feature sensitive to manufacturing process changes. To assess surface charge profile of Humira drug substance, weak cation exchange HPLC was used to separate charge isoforms containing 0, 1, or 2 C-terminal lysines and other acidic species. Identity and quantification of oligosaccharides on conserved N-linked glycosylation site of Humira was evaluated by normal phase HPLC. For binding and functional assays, surface plasmon resonance was used to assess TNFα binding. Anti-TNF ELISA was used to determine relative binding capacity of Humira in solution. A meta-analysis of clinical efficacy data over time considered potential differentiating factors to facilitate comparison across and within studies. Results Manufacturing changes for Humira primarily targeted scale increases, improvements in process robustness, and control across manufacturing sites. Charge microheterogeneity of Humira has been consistent as measured by proportion of lysines species over the product9s history. Glycosylation patterns contribute to the unique structural signature of mAbs, influence function and are considered important product QAs when determining comparability. Identity and quantity of oligosaccharides on the conserved N-linked glycosylation site of Humira was evaluated on batches manufactured each year. Relative quantity of the agalactosyl fucosylated biantennary oligosaccharide forms of Humira has remained consistent through time, as have proportions of galactose-containing fucosylated biantennary oligosaccharides and detectable high mannose glycoforms. Concurrent to the molecule9s structural consistency, potency of antibody for ligand binding was maintained through time. In addition, the structural consistency exhibited by Humira parallels the clinical performance of the mAb in the early and established RA trials that have been performed to support regulatory approval and commitments. Conclusions Humira has demonstrated a highly consistent quality attribute profile, exhibiting minimal variability through >500 batches produced over 12 years, globally aligned and maintained across multiple sites/process scales. The consistency of the structural attributes of Humira provides the foundation for consistency in clinical performance. Collectively, these data contribute to the ongoing scientific debate on criteria necessary to establish and maintain biosimilarity. Acknowledgements The authors and AbbVie designed the study and analyzed/interpreted the data. All authors contributed to the development of the content and reviewed/approved the abstract. Disclosure of Interest J. Venema Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc., P. Tebbey Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc., A. Varga Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc., M. Naill Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc., X. Wang Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc., L. Cui Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc., J. Clewell Shareholder of: AbbVie, Inc., Employee of: AbbVie, Inc.

Accelerated approval by the Food and Drug Administration (FDA), under the agency's Fast Track review designation, allows early approval of drugs to treat serious diseases and fill an unmet medical need based on a surrogate endpoint. In May 2012, FDA issued a draft Guidance for Industry on the accelerated approval of breast cancer drugs based on the surrogate endpoint "pathologic complete response" (pCR). The research reported in this article investigates potential issues in designing clinical studies for pCR-based accelerated approval. The correlation between pCR and long-term survival was investigated. Two sample comparisons based on a conditional survival model under different assumptions were performed and are discussed along with simulation results. The findings from this research may shed some light on the implementation of the FDA draft guidance.

In clinical trials, researchers usually determine a study sample size prior to the start of the study to provide a sufficient power at a targeted treatment difference. When the targeted treatment difference deviates from the true one, the study may either have insufficient power or use more subjects than necessary. To address the difficulty in sample size planning, researchers have developed various flexible sample size designs and compared their performances. Some previous work suggests that re‐estimation designs are inefficient and that one can improve uniformly by using standard group sequential likelihood ratio tests, although more interim analyses are involved. However, researchers need to further study the statement and the minimal number of tests needed before a standard group sequential test might outperform a re‐estimation design. In this paper, we conducted simulation studies to answer these questions using various optimality criteria. Copyright © 2012 John Wiley & Sons, Ltd.

On the basis of the highly stable G2H (2 : 1) ternary complex formed by two methyl viologen cation radicals inside the cavity of cucurbit[8]uril, we prepared three monocationic 4‐phenylpyridinium derivatives: 1‐(hydroxyethyl)‐4‐phenyl‐pyridinium (1+) bromide, 1‐(octaethyleneglycol)‐4‐phenyl‐pyridinium (2+) chloride, and 4‐[4‐(methoxymethoxy)phenyl]pyridinium (3+) iodide, as possible guests for 2 : 1 complexation inside cucurbit[8]uril. We also investigated a fourth monocationic guest (4+), in which a central vinylidene group is inserted to elongate the 4‐phenyl‐pyridinium residue. Using 1H NMR and UV–Vis spectroscopic data and mass spectrometric data, we obtained unequivocal evidence for the formation of G2H (2 : 1) ternary complexes in all cases. The stoichiometry of the complexes was further verified by continuous variation (Job) plots, and in some cases, high resolution ESI‐MS spectrometric data. Diffusion coefficient measurements, using 1H NMR pulse gradient spin echo techniques, yielded values consistent with the formation and expected structures of the ternary complexes. Copyright © 2012 John Wiley & Sons, Ltd.