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We develop optimal decision rules for sample size re-estimation in two-stage adaptive group sequential clinical trials. It is usual for the initial sample size specification of such trials to be adequate to detect a realistic treatment effect δ a with good power, but not sufficient to detect the smallest clinically meaningful treatment effect δ min . Moreover it is difficult for the sponsors of such trials to make the up-front commitment needed to adequately power a study to detect δ min . It is easier to justify increasing the sample size if the interim data enter a so-called "promising zone" that ensures with high probability that the trial will succeed. We have considered promising zone designs that optimize unconditional power and promising zone designs that optimize conditional power and have discussed the tension that exists between these two objectives. Where there is reluctance to base the sample size re-estimation rule on the parameter δ min we propose a Bayesian option whereby a prior distribution is assigned to the unknown treatment effect δ , which is then integrated out of the objective function with respect to its posterior distribution at the interim analysis., (© 2022 John Wiley & Sons Ltd.)

Statistical methods for controlling the type-I error of hypothesis tests in adaptive group sequential clinical trials are well established and well understood. However, methods for obtaining statistically valid point estimates and confidence intervals for adaptive designs are not as well established or as well understood. At the end of an adaptive trial, one may calculate the repeated confidence interval (RCI), which provides conservative coverage of δ , or the backward image confidence interval (BWCI), which provides exact coverage of δ and is an extension of the stagewise adjusted confidence interval (SWCI, used in classical group sequential designs). The BWCI can also provide a median unbiased estimate (MUE) of δ . There is a need to better understand the coverage and possible biases associated with these methods. We conducted a simulation study exploring parameter estimation following sample size reestimation based on testing methods with strong control of type-I error. Generally, the BWCI provided exact coverage, the naïve CI provided inconsistent coverage, and the RCI provided conservative coverage. Additionally, we note considerable asymmetry in the coverage from above/from below for the RCI, although we did not see any instance where the 95% RCI excluded the true parameter more than 2.5% on either side. At the end of an adaptive group sequential trial, we strongly recommend the use of the BWCI (and associated MUE), with the RCI computed during interim looks; the naïve CI should be avoided. These results and conclusions also hold true for classical group sequential designs., (© 2021 John Wiley & Sons Ltd.)

Two methods for designing adaptive multiarm multistage (MAMS) clinical trials, originating from conceptually different group sequential frameworks are presented, and their operating characteristics are compared. In both methods pairwise comparisons are made, stage-by-stage, between each treatment arm and a common control arm with the goal of identifying active treatments and dropping inactive ones. At any stage one may alter the future course of the trial through adaptive changes to the prespecified decision rules for treatment selection and sample size reestimation, and notwithstanding such changes, both methods guarantee strong control of the family-wise error rate. The stage-wise MAMS approach was historically the first to be developed and remains the standard method for designing inferentially seamless phase 2-3 clinical trials. In this approach, at each stage, the data from each treatment comparison are summarized by a single multiplicity adjusted P-value. These stage-wise P-values are combined by a prespecified combination function and the resultant test statistic is monitored with respect to the classical two-arm group sequential efficacy boundaries. The cumulative MAMS approach is a more recent development in which a separate test statistic is constructed for each treatment comparison from the cumulative data at each stage. These statistics are then monitored with respect to multiplicity adjusted group sequential efficacy boundaries. We compared the powers of the two methods for designs with two and three active treatment arms, under commonly utilized decision rules for treatment selection, sample size reestimation and early stopping. In our investigations, which were carried out over a reasonably exhaustive exploration of the parameter space, the cumulative MAMS designs were more powerful than the stage-wise MAMS designs, except for the homogeneous case of equal treatment effects, where a small power advantage was discernable for the stage-wise MAMS designs., (© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.)

Over the past 25 years, adaptive designs have gradually gained acceptance and are being used with increasing frequency in confirmatory clinical trials. Recent surveys of submissions to the regulatory agencies reveal that the most popular type of adaptation is unblinded sample size re-estimation. Concerns have nevertheless been raised that this type of adaptation is inefficient.We intend to show in our discussion that such concerns are greatly exaggerated in any practical setting and that the advantages of adaptive sample size re-estimation usually outweigh any minor loss of efficiency., (Copyright © 2016 John Wiley & Sons, Ltd.)

The development of molecularly targeted therapies for certain types of cancers has led to the consideration of population enrichment designs that explicitly factor in the possibility that the experimental compound might differentially benefit different biomarker subgroups. In such designs, enrollment would initially be open to a broad patient population with the option to restrict future enrollment, following an interim analysis, to only those biomarker subgroups that appeared to be benefiting from the experimental therapy. While this strategy could greatly improve the chances of success for the trial, it poses several statistical and logistical design challenges. Because late-stage oncology trials are typically event driven, one faces a complex trade-off between power, sample size, number of events, and study duration. This trade-off is further compounded by the importance of maintaining statistical independence of the data before and after the interim analysis and of optimizing the timing of the interim analysis. This paper presents statistical methodology that ensures strong control of type 1 error for such population enrichment designs, based on generalizations of the conditional error rate approach. The special difficulties encountered with time-to-event endpoints are addressed by our methods. The crucial role of simulation for guiding the choice of design parameters is emphasized. Although motivated by oncology, the methods are applicable as well to population enrichment designs in other therapeutic areas., (Copyright © 2014 John Wiley & Sons, Ltd.)

We propose an "efficacy-to-effectiveness" (E2E) clinical trial design, in which an effectiveness trial would commence seamlessly upon completion of the efficacy trial. Efficacy trials use inclusion/exclusion criteria to produce relatively homogeneous samples of participants with the target condition, conducted in settings that foster adherence to rigorous clinical protocols. Effectiveness trials use inclusion/exclusion criteria that generate heterogeneous samples that are more similar to the general patient spectrum, conducted in more varied settings, with protocols that approximate typical clinical care. In E2E trials, results from the efficacy trial component would be used to design the effectiveness trial component, to confirm and/or discern associations between clinical characteristics and treatment effects in typical care, and potentially to test new hypotheses. An E2E approach may improve the evidentiary basis for selecting treatments, expand understanding of the effectiveness of treatments in subgroups with particular clinical features, and foster incorporation of effectiveness information into regulatory processes.

Methods for controlling the type-1 error of an adaptive group sequential trial were developed in seminal papers by Cui, Hung, and Wang (Biometrics, 1999), Lehmacher and Wassmer (Biometrics, 1999), and Müller and Schäfer (Biometrics, 2001). However, corresponding solutions for the equally important and related problem of parameter estimation at the end of the adaptive trial have not been completely satisfactory. In this paper, a method is provided for computing a two-sided confidence interval having exact coverage, along with a point estimate that is median unbiased for the primary efficacy parameter in a two-arm adaptive group sequential design. The possible adaptations are not only confined to sample size alterations but also include data-dependent changes in the number and spacing of interim looks and changes in the error spending function. The procedure is based on mapping the final test statistic obtained in the modified trial into a corresponding backward image in the original trial. This is an advance on previously available methods, which either produced conservative coverage and no point estimates or provided exact coverage for one-sided intervals only., (Copyright © 2013 John Wiley & Sons, Ltd.)

Introduction: Infants and children with congenital aortic stenosis and coarctation of the aorta can be treated by catheter intervention. There are several pharmacological and mechanical techniques described to overcome the balloon movement; none, however, have proved entirely satisfactory. An alternative method to achieve balloon stability is the use of rapid ventricular pacing. We describe our experience with titrating the pacing rate and the use of this technique., Methods: A retrospective review of database was performed, to identify patients who underwent transcatheter intervention with rapid ventricular pacing. Invasive systemic pressures were documented with a catheter in the aorta. Rapid ventricular pacing was initiated at the rate of 180 per minute and increased by increments of 20 per minute to a rate required to achieve a drop in systemic pressure by 50% and a drop in pulse pressure by 25%. The balloon was inflated only after the desired pacing rate was reached. Pacing was continued until the balloon was completely deflated., Results: Thirty patients were identified, 29 of whom had interventions with rapid ventricular pacing. Balloon valvuloplasty of aortic valve was performed on 25 patients while 4 patients had stenting for coarctation by this technique. The rate of ventricular pacing required ranged from 200 to 260 per minute with a median rate of 240. Balloon stability at the time of intervention was achieved in 27 patients., Conclusion: Rapid ventricular pacing is a safe and effective method to provide transient decrease in cardiac output at the time of transcatheter interventions to achieve balloon stability.

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist., (Copyright 2001 John Wiley & Sons, Ltd.)

The Cochran-Armitage test for trend is a popular statistical procedure for detecting increasing or decreasing probabilities of response when a categorical exposure is ordered. Such associations may arise in a variety of biomedical research settings, particularly in dose-response designs such as carcinogenicity experiments. Previously, computing limitations mandated the use of the asymptotic trend test, but with the availability of new algorithms, increased computing power, and appropriate software the exact trend test is now a practical option. Nevertheless, the exact test is sometimes criticized on the grounds that it is conservative. In this paper we investigate the implications of this conservatism by comparing the true type I error and power of three alternative tests of trend - the asymptotic test, the exact test and an admissible exact test proposed by Cohen and Sackrowitz. The computations are performed by an extension to the network algorithm of Mehta et al. This allows us to make precise power comparisons between the tests under any given design without resorting to simulation. We show how this tool can guide investigators in choosing the most appropriate test by considering the design of two-year carcinogenicity studies carried out by the National Toxicology Program. We additionally compare the tests for various other combinations of sample sizes and number of groups or levels of exposure. We conclude that the asymptotic test, while more powerful where it is valid, generally does not preserve the type I error. This violation of the a priori testing level can be greatly affected by imbalance in the data or unequal spacing of dose levels., (Copyright 2000 John Wiley & Sons, Ltd.)

Ordered categorical data occur frequently in biomedical research. The linear by linear association test for ordered R x C tables permits the investigator to specify row and column scores for analysis. When an investigator believes that there may be more than one set of reasonable scores or when more than one investigator proposes scores, we need a method to decide upon a single procedure to use. We show how to use efficiency robustness principles to combine tests from two or more sets of scores into one robust test for analysis. This test minimizes the worst possible efficiency loss over all the sets of scores. We illustrate the methodology for the R x C case and, in detail, for the important special 2 x C case.

We provide an alternative to the maximum likelihood method for making inferences about the parameters of the logistic regression model. The method is based appropriate permutational distributions of sufficient statistics. It is useful for analysing small or unbalanced binary data with covariates. It also applies to small-sample clustered binary data. We illustrate the method by analysing several biomedical data sets.

The MACC (methotrexate, adriamycin, cyclophosphamide, CCNU) regimen was administered to 43 patients with advanced epidermoid and adenocarcinoma of the lung. Only 5 patients (12%), all of whom were ambulatory responded with partial remissions. Median time to progression for the 5 responders was 20 weeks from start of treatment. Median survival was 15.5 weeks for patients with epidermoid cancer and 14.4 weeks for those with adenocarcinoma. Hematologic toxicity was severe, with 2 treatment-related deaths during profound myelosuppression. White blood counts below 2000/microliter were reported in 47%, and below 1,000/microliter in 26%. Since the activity of this regimen, given as it was in full doses, is not superior to that achieved with standard doses of single agents which are less toxic, further employment of the MACC regimen is not recommended, either for advanced disease or as a surgical adjuvant.