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The question of selecting the ‘best’ among different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example, which treatment gives the best response rate. Motivated by recent developments in the theory of context-dependent information measures, we propose a flexible response-adaptive experimental design based on a novel criterion governing treatment arm selections which can be used in adaptive experiments with simple (e.g. binary) and complex (e.g. co-primary, ordinal or nested) end points. It was found that, for specific choices of the context-dependent measure, the criterion leads to a reliable selection of the correct arm without any parametric or monotonicity assumptions and provides noticeable gains in settings with costly observations. The asymptotic properties of the design are studied for different allocation rules, and the small sample size behaviour is evaluated in simulations in the context of phase II clinical trials with different end points. We compare the proposed design with currently used alternatives and discuss its practical implementation.

\"Monograph (in four volumes) on New York-based architecture firm FXFOWLE. Each of the four volumes is built around an individual theme: reveal (projects in the landscape), filter (interaction with culture/climate), evolve (adaptive reuse), and effect (buildings designed around a specific/unusual function). Each volume contains a short introduction from the firm, a critical essay by an outside writer, and comprehensive presentation of 4-5 projects. Heavily illustrated throughout\"-- Provided by publisher.

The paper reviews the growing literature on responsive and adaptive designs for surveys. These designs encompass various methods for managing data collection, including front loading potentially difficult cases, tailoring data collection strategies to different subgroups, prioritizing effort according to estimated response propensities, imposing stop rules for ending data collection, monitoring key survey estimates throughout the field period, using two-phase or multiphase sampling for following up non-respondents and calculating indicators of non-response bias (such as the R-indicator) other than response rates to monitor and guide fieldwork. We give particular attention to efforts to evaluate these strategies experimentally or via simulations. Although the field seems to have embraced these new tools, most of the evaluation studies suggest they produce marginal reductions in cost and non-response bias. It is clearly difficult to lower survey costs without reducing some aspect of survey quality. Other issues limiting the effectiveness of these designs include weakly predictive auxiliary variables, ineffective interventions and slippage in the implementation of interventions in the field. These problems are not, however, unique to responsive or adaptive design. We give recommendations for improving such designs and for improving the management of data collection efforts in the current difficult environment for surveys.

Expected Fisher information can be found a priori and as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast with the common claim that the inverse of the observed Fisher information is a better approximation of the variance of the maximum likelihood estimator. Observed Fisher information cannot be known a priori; however, if an experiment is conducted sequentially, in a series of runs, the observed Fisher information from previous runs is known. In the current work, two adaptive designs are proposed that use the observed Fisher information from previous runs to inform the design of future runs.

To investigate the design, conduct, and analysis of adaptive trials through a systematic survey and provide recommendations for future adaptive trials. We systematically searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up to January 2020. We included trials that were self-described as adaptive trials or applied adaptive designs. We identified three frequently used adaptive designs and summarized their methodological details in terms of design, conduct, and analysis. Lastly, we provided recommendations for future adaptive trials. We included a total of 128 trials in this study. The primary motivations for using adaptive design were to speed up the trials and facilitate decision-making (n = 29, 31.5%). The three most frequently used methods were group sequential design (GSD) (n = 71, 55.5%), adaptive dose-finding design (ADFD) (n = 35, 27.3%), and adaptive randomization design (ARD) (n = 26, 20.3%). The timing and frequency of interim analysis were detailed in three-fourths of the GSD trials (n = 55, 77.5%) and in half of the ADFD trials (n = 19, 54.3%); however, more than half of the ARD trials (n = 15, 57.7%) did not provide this information. Some trials selected a different outcome than the primary outcome for interim analysis (GSD: n = 7, 12.7%; ADFD: n = 8, 27.6%; ARD: n = 7, 50.0%), but the majority of these trials did not provide explicit reasons for this choice (GSD: n = 7, 100.0%; ADFD: n = 7, 87.5%; ARD: n = 5, 71.4%). More than half (n = 76, 59.4%) of trials did not mention the accessibility of supporting documents, and two-thirds (n = 86, 67.2%) did not state the establishment of independent data monitoring committees (IDMCs). Moreover, unplanned adjustments were observed during the conduct of one-sixth adaptive trials (n = 22, 17.2%). Based on our findings, we provide 14 recommendations for improving adaptive trials in the future. Substantial improvements were needed in methods of adaptive trials, particularly in the areas of interim analysis, the establishment of independent data monitoring committees, and unplanned adjustments. In this study, we offer recommendations from both general and specific aspects for researchers to carefully design, conduct, and analyze adaptive trials.

An important issue for many economic experiments is how the experimenter can ensure sufficient power in order to reject one or more hypotheses. The paper illustrates how methods for testing multiple hypotheses simultaneously in adaptive, two-stage designs can be used to improve the power of economic experiments. We provide a concise overview of the relevant theory and illustrate the method in three different applications. These include a simulation study of a hypothetical experimental design, as well as illustrations using two data sets from previous experiments. The simulation results highlight the potential for sample size reductions, maintaining the power to reject at least one hypothesis while ensuring strong control of the overall Type I error probability.

A biosimilar product refers to a follow-on biologic that is intended to be approved for marketing on the basis of biosimilarity to an existing patented biological product (i.e. the reference product). To develop a biosimilar product, it is essential to demonstrate biosimilarity between the follow-on biologic and the reference product, typically through two-arm randomization trials. We propose a Bayesian adaptive design for trials to evaluate biosimilar products. To take advantage of the abundant historical data on the efficacy of the reference product that is typically available at the time that a biosimilar product is developed, we propose the calibrated power prior, which allows our design to borrow information adaptively from the historical data according to the congruence between the historical data and the new data collected from the current trial. We propose a new measure, the Bayesian biosimilarity index, to measure the similarity between the biosimilar product and the reference product. During the trial, we evaluate the Bayesian biosimilarity index in a group sequential fashion on the basis of the accumulating interim data and stop the trial early once there is enough information to conclude or reject the similarity. Extensive simulation studies show that the design proposed has higher power than traditional designs. We applied the design to a biosimilar trial for treating rheumatoid arthritis.

Clinical trials usually involve efficient and ethical objectives. Different adaptive designs have been proposed to satisfy these needs. We combine interim analysis, the sequential estimation-adjusted urn model (SEU), and sample size re-estimation (SSR) in one clinical trial. We show that the asymptotic distribution, under the null hypothesis, of the proposed sequential statistic follows Brownian motion by simultaneously addressing the three sequential procedures (allocation of patients, urn composition, and sequential parameter estimators) and the sequential statistics with revised information time due to SSR. Therefore, to control the type I error rate, traditional critical values for sequential monitoring based on Brownian motion can be used for the proposed procedure. Numerical studies with three types of urn models demonstrate that our proposed approach can control the type I error rate well and also achieve efficient and ethical objectives. Les études cliniques comportent des objectifs d’éthique et d’efficacité. Différents plans d’expérience adaptatifs proposés permettent de les atteindre. Les auteurs combinent dans une même étude clinique des analyses intérimaires, le modèle séquentiel d’urnes ajusté pour l’estimation, ainsi que la ré-estimation de la taille d’échantillon (RTE). Sous l’hypothèse nulle, ils montrent que la distribution asymptotique de la statistique séquentielle proposée suit un mouvement brownien en abordant les trois procédures séquentielles (attribution des patients, composition de l’urne et estimation séquentielle des paramètres) ainsi que les statistiques séquentielles avec l’information révisée due à la RTE. Ainsi, les valeurs critiques traditionnelles pour le monitoring séquentiel basées sur un mouvement brownien peuvent être utilisées pour contrôler l’erreur de type I avec la procédure proposée. Les auteurs illustrent leur approche à l’aide d’études numériques pour trois types de modèles d’urnes et montrent qu’ils peuvent bien contrôler le taux d’erreur de type I et atteindre leurs objectifs d’éthique et d’efficacité.

We present a two-stage phase I–II design of a combination of two drugs in cancer clinical trials. The goal is to estimate safe dose combination regions with a desired level of efficacy. In stage I, conditional escalation with overdose control is used to allocate dose combinations to successive cohorts of patients and the maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. In stage II, we propose a Bayesian adaptive design for conducting the phase II trial to determine dose combination regions along the maximum tolerated dose curve with a desired level of efficacy. The methodology is evaluated by extensive simulations and application to a real trial.