A comparison of methods for constructing confidence intervals after phase II/III clinical trials

Recently, in order to accelerate drug development, trials that use adaptive seamless designs such as phase II/III clinical trials have been proposed. Phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages. Using stage 1 data, an interim analysis is performed to answer phase II objectives and after collection of stage 2 data, a final confirmatory analysis is performed to answer phase III objectives. In this paper we consider phase II/III clinical trials in which, at stage 1, several experimental treatments are compared to a control and the apparently most effective experimental treatment is selected to continue to stage 2. Although these trials are attractive because the confirmatory analysis includes phase II data from stage 1, the inference methods used for trials that compare a single experimental treatment to a control and do not have an interim analysis are no longer appropriate. Several methods for analysing phase II/III clinical trials have been developed. These methods are recent and so there is little literature on extensive comparisons of their characteristics. In this paper we review and compare the various methods available for constructing confidence intervals after phase II/III clinical trials.     

Seamlessly expanding a randomized phase II trial to phase III

A sequential Bayesian phase II/III design is proposed for comparative clinical trials. The design is based on both survival time and discrete early events that may be related to survival and assumes a parametric mixture model. Phase II involves a small number of centers. Patients are randomized between treatments throughout, and sequential decisions are based on predictive probabilities of concluding superiority of the experimental treatment. Whether to stop early, continue, or shift into phase III is assessed repeatedly in phase II. Phase III begins when additional institutions are incorporated into the ongoing phase II trial. Simulation studies in the context of a non-small-cell lung cancer trial indicate that the proposed method maintains overall size and power while usually requiring substantially smaller sample size and shorter trial duration when compared with conventional group-sequential phase III designs.     

The role of "go no-go" decisions in TB vaccine development

"Go no-go" decisions play a critical role in the product development plan of any TB vaccine at several points. Go no-go decisions are designed to serve the fundamental maxim in vaccine development that killing a bad vaccine project as early as possible is the hallmark of a successful overall program; in development, unlike in basic research, costs and skilled manpower requirements rise exponentially as the program proceeds. The opportunity costs in resources, manpower and equipment utilized on bad vaccine projects are unavailable to other vaccine programs. The go no-go decision is a fundamental piece of the process that balances risk, time and resources. An example of a go no-go decision development program is presented.     

Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: applications and practical considerations

Adaptive seamless phase II/III designs combine a phase II and a phase III study into one single confirmatory clinical trial. Several examples of such designs are presented, where the primary endpoint is binary, time-to-event or continuous. The interim adaptations considered include the selection of treatments and the selection of hypotheses related to a pre-specified subgroup of patients. Practical aspects concerning the planning and implementation of adaptive seamless confirmatory studies are also discussed.     

Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: opportunities and limitations

This is a discussion of the following two papers in this special issue on adaptive designs: 'Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: General concepts' by Frank Bretz, Heinz Schmidli, Franz K?nig, Amy Racine and Willi Maurer, and 'Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: Applications and practical considerations' by Heinz Schmidli, Frank Bretz, Amy Racine and Willi Maurer.     

An optimal Bayesian predictive probability design for phase II clinical trials with simple and complicated endpoints

Most existing phase II clinical trial designs focus on conventional chemotherapy with binary tumor response as the endpoint. The advent of novel therapies, such as molecularly targeted agents and immunotherapy, has made the endpoint of phase II trials more complicated, often involving ordinal, nested, and coprimary endpoints. We propose a simple and flexible Bayesian optimal phase II predictive probability (OPP) design that handles binary and complex endpoints in a unified way. The Dirichlet-multinomial model is employed to accommodate different types of endpoints. At each interim, given the observed interim data, we calculate the Bayesian predictive probability of success, should the trial continue to the maximum planned sample size, and use it to make the go/no-go decision. The OPP design controls the type I error rate, maximizes power or minimizes the expected sample size, and is easy to implement, because the go/no-go decision boundaries can be enumerated and included in the protocol before the onset of the trial. Simulation studies show that the OPP design has satisfactory operating characteristics.     

Decision-theoretic designs for phase II clinical trials allowing for competing studies

This article describes an approach to optimal design of phase II clinical trials using Bayesian decision theory. The method proposed extends that suggested by Stallard (1998, Biometrics 54, 279-294) in which designs were obtained to maximize a gain function including the cost of drug development and the benefit from a successful therapy. Here, the approach is extended by the consideration of other potential therapies, the development of which is competing for the same limited resources. The resulting optimal designs are shown to have frequentist properties much more similar to those traditionally used in phase II trials.     

Designing phase II studies in the context of a programme of clinical research

Conventional statistical determinations of sample size in phase II studies typically lead to sample sizes of the order of 25 (Schoenfeld, 1980, International Journal of Radiation Oncology, Biology and Physics 6, 371-374). When the development of new treatments is proceeding rapidly relative to the recruitment of suitable patients, such requirements can prove to be too demanding. As a result, either sample sizes are reduced by a rather arbitrary weakening of the risk specifications, or certain new treatments go untested. In this paper, the phase II testing of a number of treatments will be considered as a single study which has the objective of identifying the most promising treatment for phase III investigation. It is seen to be advantageous to test more treatments, with fewer subjects receiving each, than the conventional methods would allow.     

Preparing for phase II/III HIV vaccine trials in Africa

Before performing phase II/III clinical trials in Africa, preliminary studies, including assessment and building up of clinical and laboratory infrastructures, estimates of human immunodeficiency virus incidence, investigation of the background immune response, and evaluation of the cross-clade immune response, need to be done. Plans and ongoing work in the context of the AIDS Vaccine Integrated Project and some preliminary data are presented.     

A Bayesian decision approach for sample size determination in phase II trials

Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.     

Decision theoretic designs for phase II clinical trials with multiple outcomes

In many phase II clinical trials, it is essential to assess both efficacy and safety. Although several phase II designs that accommodate multiple outcomes have been proposed recently, none are derived using decision theory. This paper describes a Bayesian decision theoretic strategy for constructing phase II designs based on both efficacy and adverse events. The gain function includes utilities assigned to patient outcomes, a reward for declaring the new treatment promising, and costs associated with the conduct of the phase II trial and future phase III testing. A method for eliciting gain function parameters from medical collaborators and for evaluating the design's frequentist operating characteristics is described. The strategy is illustrated by application to a clinical trial of peripheral blood stem cell transplantation for multiple myeloma.     

Automated detection of the phase III slope during inert gas washout testing

We describe a method to determine the phase III slope for the purpose of calculating indexes of ventilation heterogeneity, S(acin) and S(cond), from the multiple breath nitrogen washout test (MBNW). Our automated method applies a recursive, segmented linear regression technique to each breath of the MBNW test and determines the best point of transition, or breakpoint, between each phase of the washout. A sample set of 50 MBNW tests (controls, asthma, and COPD) was used to establish the conditions in which the phase III slope obtained from the automated technique best matched that obtained by two manual interpreters. We then applied our technique to a test set of 30 subjects (with an even number of subjects in each of the above groups) and compared these results against the manual analysis of a third independent manual interpreter. Indexes of ventilation heterogeneity were determined using both methods and compared. The phase III slopes determined by the automatic technique best matched the manual interpreter when the phase III slope was calculated from the phase II-III transition point plus the addition of 50% of the phase II volume to the end of the expiration. Calculation of the indexes S(acin) and S(cond) showed no overall difference between analysis methods in either S(acin) (P = 0.14) or S(cond) (P = 0.59) when the set threshold was applied to our automated analysis. Our analysis method provides an alternate means for rapid quantification of the MBNW test, removing operator dependence without alteration in either S(acin) or S(cond).     

Lipolytic and metabolic response to glucagon in fasting king penguins: phase II vs. phase III

This study aims to determine how glucagon intervenes in the regulation of fuel metabolism, especially lipolysis, at two stages of a spontaneous long-term fast characterized by marked differences in lipid and protein availability and/or utilization (phases II and III). Changes in the plasma concentration of various metabolites and hormones, and in lipolytic fluxes as determined by continuous infusion of [2-3H]glycerol and [1-14C]palmitate, were examined in vivo in a subantarctic bird (king penguin) before, during, and after a 2-h glucagon infusion. In the two fasting phases, glucagon infusion at a rate of 0.025 microg. kg(-1). min(-1) induced a three- to fourfold increase in the plasma concentration and in the rate of appearance (Ra) of glycerol and nonesterified fatty acids, the percentage of primary reesterification remaining unchanged. Infusion of glucagon also resulted in a progressive elevation of the plasma concentration of glucose and beta-hydroxybutyrate and in a twofold higher insulinemia. These changes were not significantly different between the two phases. The plasma concentrations of triacylglycerols and uric acid were unaffected by glucagon infusion, except for a 40% increase in plasma uric acid in phase II birds. Altogether, these results indicate that glucagon in a long-term fasting bird is highly lipolytic, hyperglycemic, ketogenic, and insulinogenic, these effects, however, being similar in phases II and III. The maintenance of the sensitivity of adipose tissue lipolysis to glucagon could suggest that the major role of the increase in basal glucagonemia observed in phase III is to stimulate gluconeogenesis rather than fatty acid delivery.     

Simulated impact of RTS,S/AS01 vaccination programs in the context of changing malaria transmission

Introduction

The RTS,S/AS01 pre-erythrocytic malaria vaccine is in phase III clinical trials. It is critical to anticipate where and how it should be implemented if trials are successful. Such planning may be complicated by changing levels of malaria transmission.

Methods/results

Computer simulations were used to examine RTS,S/AS01 impact, using a vaccine profile based on phase II trial results, and assuming that protection decays only slowly. Settings were simulated in which baseline transmission (in the absence of vaccine) was fixed or varied between 2 and 20 infectious mosquito bites per person per annum (ibpa) over ten years. Four delivery strategies were studied: routine infant immunization (EPI), EPI plus infant catch-up, EPI plus school-based campaigns, and EPI plus mass campaigns. Impacts in changing transmission settings were similar to those in fixed settings. Assuming a persistent effect of vaccination, at 2 ibpa, the vaccine averted approximately 5–7 deaths per 1000 doses of vaccine when delivered via mass campaigns, but the benefit was less at higher transmission levels. EPI, catch-up and school-based strategies averted 2–3 deaths per 1000 doses in settings with 2 ibpa. In settings where transmission was decreasing or increasing, EPI, catch-up and school-based strategies averted approximately 3–4 deaths per 1000 doses.

Discussion

Where transmission is changing, it appears to be sufficient to consider simulations of pre-erythrocytic vaccine impact at a range of initial transmission levels. At 2 ibpa, mass campaigns averted the most deaths and reduced transmission, but this requires further study. If delivered via EPI, RTS,S/AS01 could avert approximately 6–11 deaths per 1000 vaccinees in all examined settings, similar to estimates for pneumococcal conjugate vaccine in African infants. These results support RTS,S/AS01 implementation via EPI, for example alongside vector control interventions, providing that the phase III trials provide support for our assumptions about efficacy.     

A parallel phase I/II clinical trial design for combination therapies

The use of multiple drugs in a single clinical trial or as a therapeutic strategy has become common, particularly in the treatment of cancer. Because traditional trials are designed to evaluate one agent at a time, the evaluation of therapies in combination requires specialized trial designs. In place of the traditional separate phase I and II trials, we propose using a parallel phase I/II clinical trial to evaluate simultaneously the safety and efficacy of combination dose levels, and select the optimal combination dose. The trial is started with an initial period of dose escalation, then patients are randomly assigned to admissible dose levels. These dose levels are compared with each other. Bayesian posterior probabilities are used in the randomization to adaptively assign more patients to doses with higher efficacy levels. Combination doses with lower efficacy are temporarily closed and those with intolerable toxicity are eliminated from the trial. The trial is stopped if the posterior probability for safety, efficacy, or futility crosses a prespecified boundary. For illustration, we apply the design to a combination chemotherapy trial for leukemia. We use simulation studies to assess the operating characteristics of the parallel phase I/II trial design, and compare it to a conventional design for a standard phase I and phase II trial. The simulations show that the proposed design saves sample size, has better power, and efficiently assigns more patients to doses with higher efficacy levels.     

Effect of variability of sequence length of go trials preceding a stop trial on ability of response inhibition in stop-signal task

Abstract

This study investigated whether the variability of the sequence length of the go trials preceding a stop trial enhanced or interfered with inhibitory control. The hypotheses tested were either inhibitory control improves when the sequence length of the go trials varies as a consequence of increased preparatory effort or it degrades as a consequence of the switching cost from the go trial to the stop trial. The right-handed participants abducted the left or right index finger in response to a go cue during the go trials. A stop cue was given at 50, 90, or 130?ms after the go cue, with 0.25 probability in the stop trial. In the less variable session, a stop trial was presented after two, three, or four consecutive go trials. In the variable session, a stop trial was presented after one, two, three, four, or five consecutive go trials. The reaction time and stop-signal reaction time were not significantly different between the sessions and between the response sides. Nevertheless, the probability of successful inhibition of the right-hand response in the variable session was higher than that in the less variable session when the stop cue was given 50?ms after a go cue. This finding supports the view that preparatory effort due to less predictability of the chance of a forthcoming response inhibition enhances the ability of the right-hand response inhibition when the stop process begins earlier.     

Curtailment in single‐arm two‐stage phase II oncology trials

Two‐stage designs that allow for early stopping if the treatment is ineffective are commonly used in phase II oncology trials. A limitation of current designs is that early stopping is only allowed at the end of the first stage, even if it becomes evident during the trial that a significant result is unlikely. One way to overcome this limitation is to implement stochastic curtailment procedures that enable stopping the trial whenever the conditional power is below a pre‐specified threshold θ. In this paper, we present the results for implementing curtailment rules in either only the second stage or both stages of the designs. In total, 102 scenarios with different parameter settings were investigated using conditional power thresholds θ between 0 and 1 in steps of 0.01. An increase in θ results not only in a decrease of the actual Type I error rate and power but also of the expected sample size. Therefore, a reasonable balance has to be found when selecting a specific threshold value in the planning phase of a curtailed two‐stage design. Given that the effect of curtailment highly depends on the underlying design parameters, no general recommendation for θ can be made. However, up to , the loss in power was less than 5% for all investigated scenarios while savings of up to 50% in expected sample size occurred. In general, curtailment is most appropriate when the outcome can be observed fast or when accrual is slow so that adequate information for making early and frequent decisions is available.     

Optimal adaptive two‐stage designs for phase II cancer clinical trials

In oncology, single‐arm two‐stage designs with binary endpoint are widely applied in phase II for the development of cytotoxic cancer therapies. Simon's optimal design with prefixed sample sizes in both stages minimizes the expected sample size under the null hypothesis and is one of the most popular designs. The search algorithms that are currently used to identify phase II designs showing prespecified characteristics are computationally intensive. For this reason, most authors impose restrictions on their search procedure. However, it remains unclear to what extent this approach influences the optimality of the resulting designs. This article describes an extension to fixed sample size phase II designs by allowing the sample size of stage two to depend on the number of responses observed in the first stage. Furthermore, we present a more efficient numerical algorithm that allows for an exhaustive search of designs. Comparisons between designs presented in the literature and the proposed optimal adaptive designs show that while the improvements are generally moderate, notable reductions in the average sample size can be achieved for specific parameter constellations when applying the new method and search strategy.     

Ion‐Exchange and Adsorption of Fe(III) by Sepia Melanin

Sepia eumelanin is associated with many metal ions, yet little is known about its metal binding capacity and the chemical nature of the binding site(s). Herein, the natural concentrations of metal ions are presented and the ability to remove metals by exposure of the melanin granules to EDTA is quantified. The results reveal that the binding constants of melanin at pH 5.8 for Mg(II), Ca(II), Sr(II) and Cu(II) are, respectively, 5, 4, 14 and 34 times greater than the corresponding binding constants of these ions with EDTA. By exposing Sepia eumelanin to aqueous solutions of FeCl₃, the content of bound Fe(III) can be increased from a natural concentration of ～180 ppm to a saturation limit of ～80 000 ppm or 1.43 mmol/g of melanin. Similar saturation limits are found for Mg(II) and Ca(II). Exposure of Sepia melanin granules to aqueous solutions containing Ca(II) results in the stoichiometric replacement of the initially bound Mg(II), arguing that these two ions occupy the same binding site(s) in the pigment. The pH‐dependent binding of Mg(II) and Ca(II) suggests coordination of these ions to carboxylic acid groups in the pigment. Mg(II) and Ca(II) can be added to a Fe(III)‐saturated melanin sample without affecting the amount of Fe(III) pre‐adsorbed, clearly establishing Fe(III) and Mg(II)/Ca(II) occupy different binding sites. Taking recent Raman spectroscopic data into account, the binding of Fe(III) is concluded to involve coordination to o‐dihydroxyl groups. The effects of metal ion content on the surface morphology were analyzed. No significant changes were found over the full range of Fe(III) concentration studied, which is supported by the Brunauer–Emmett–Teller surface area analysis. These observations imply the existence of channels within the melanin granules that can serve to transport metal ions.