Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive ...design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
Objective:
When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions ...about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes.
Methods:
Web of Science was used to identify papers proposing methods relevant for using data from a ‘source population’ to support inferences for a ‘target population’. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes.
Results:
Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic.
Conclusions:
Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.
Group sequential methods are used routinely to monitor clinical trials and to provide early stopping when there is evidence of a treatment effect, a lack of an effect or concerns about patient ...safety. In many studies, the response of clinical interest is measured some time after the start of treatment and there are subjects at each interim analysis who have been treated but are yet to respond. We formulate a new form of group sequential test which gives a proper treatment of these 'pipeline' subjects; these tests can be applied even when the continued accrual of data after the decision to stop the trial is unexpected. We illustrate our methods through a series of examples. We define error spending versions of these new designs which handle unpredictable group sizes and provide an information monitoring framework that can accommodate nuisance parameters, such as an unknown response variance. By studying optimal versions of our new designs, we show how the benefits of lower expected sample size that are normally achieved by a group sequential test are reduced when there is a delay in response. The loss of efficiency for larger delays can be ameliorated by incorporating data on a correlated short-term end point, fitting a joint model for the two end points but still making inferences on the original, longer-term end point. We derive p-values and confidence intervals on termination of our new tests.
Objective
Cyclophosphamide (CYC) is used in clinical practice off‐label for the induction of remission in childhood polyarteritis nodosa (PAN). Mycophenolate mofetil (MMF) might offer a less toxic ...alternative. This study was undertaken to explore the relative effectiveness of CYC and MMF treatment in a randomized controlled trial (RCT).
Methods
This was an international, open‐label, Bayesian RCT to investigate the relative effectiveness of CYC and MMF for remission induction in childhood PAN. Eleven patients with newly diagnosed childhood PAN were randomized (1:1) to receive MMF or intravenous CYC; all patients received the same glucocorticoid regimen. The primary end point was remission within 6 months while compliant with glucocorticoid taper. Bayesian distributions for remission rates were established a priori for MMF and CYC by experienced clinicians and updated to posterior distributions on trial completion.
Results
Baseline disease activity and features were similar between the 2 treatment groups. The primary end point was met in 4 of 6 patients (67%) in the MMF group and 4 of 5 patients (80%) in the CYC group. Time to remission was shorter in the MMF group compared to the CYC group (median 7.1 weeks versus 17.6 weeks). No relapses occurred in either group within 18 months. Two serious infections were found to be likely linked to MMF treatment. Physical and psychosocial quality‐of‐life scores were superior in the MMF group compared to the CYC group at 6 months and 18 months. Combining the prior expert opinion with results from the present study provided posterior estimates of remission of 71% for MMF (90% credibility interval 90% CrI 51, 83) and 75% for CYC (90% CrI 57, 86).
Conclusion
The present results, taken together with prior opinion, indicate that rates of remission induction in childhood PAN are similar with MMF treatment and CYC treatment, and MMF treatment might be associated with better health‐related quality of life than CYC treatment.
In phase II platform trials, 'many-to-one' comparisons are performed when K experimental treatments are compared with a common control to identify the most promising treatment(s) to be selected for ...Phase III trials. However, when sample sizes are limited, such as when the disease of interest is rare, only a single Phase II/III trial addressing both treatment selection and confirmatory efficacy testing may be feasible. In this paper, we suggest a two-step safety selection and testing procedure for such seamless trials. At the end of the study, treatments are first screened on the basis of safety, and those deemed to be sufficiently safe are then taken forwards for efficacy testing against a common control. All safety and efficacy evaluations are therefore performed at the end of the study, when for each patient all safety and efficacy data are available. If confirmatory conclusions are to be drawn from the trial, strict control of the family-wise error rate (FWER) is essential. However, to avoid unnecessary losses in power, no type I error rate should be "wasted" on comparisons which are no longer of interest because treatments have been dropped due to safety concerns. We investigate the impact on power and FWER control of multiplicity adjustments which correct efficacy tests only for the number of safe selected treatments instead of adjusting for all K null hypotheses the trial begins testing. We derive conditions under which strict control of the FWER can be achieved. Procedures using the estimated association between safety and efficacy outcomes are developed for the case when the correlation between endpoints is unknown. The operating characteristics of the proposed procedures are assessed via simulation.
Before a first-in-man trial is conducted, preclinical studies are performed in animals to help characterise the safety profile of the new medicine. We propose a robust Bayesian hierarchical model to ...synthesise animal and human toxicity data, using scaling factors to translate doses administered to different animal species onto an equivalent human scale. After scaling doses, the parameters of dose-toxicity models intrinsic to different animal species can be interpreted on a common scale. A prior distribution is specified for each translation factor to capture uncertainty about differences between toxicity of the drug in animals and humans. Information from animals can then be leveraged to learn about the relationship between dose and risk of toxicity in a new phase I trial in humans. The model allows human dose-toxicity parameters to be exchangeable with the study-specific parameters of animal species studied so far or non-exchangeable with any of them. This leads to robust inferences, enabling the model to give greatest weight to the animal data with parameters most consistent with human parameters or discount all animal data in the case of non-exchangeability. The proposed model is illustrated using a case study and simulations. Numerical results suggest that our proposal improves the precision of estimates of the toxicity rates when animal and human data are consistent, while it discounts animal data in cases of inconsistency.
In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose–toxicity ...relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose–toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, ‘average’ human dosing scale, human dose–toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.
Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision‐making in a model‐based dose‐escalation procedure. ...We make a proposal for how to measure and address a prior‐data conflict in a sequential study with a small sample size. Animal data are incorporated via a robust two‐component mixture prior for the parameters of the human dose–toxicity relationship. The weights placed on each component of the prior are chosen empirically and updated dynamically as the trial progresses and more data accrue. After completion of each cohort, we use a Bayesian decision‐theoretic approach to evaluate the predictive utility of the animal data for the observed human toxicity outcomes, reflecting the degree of agreement between dose–toxicity relationships in animals and humans. The proposed methodology is illustrated through several data examples and an extensive simulation study.
An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions ...about whether or not to perform a study. Immuno‐oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open‐source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno‐oncology and for other treatments where a delayed treatment effect is expected to occur.
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