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In Phase I/II dose-finding trials, the objective is to find the Optimal Biological Dose (OBD), a dose that is both safe and shows sufficient activity that maximizes some optimality criterion based on safety and activity. In cancer, treatment is typically given over several cycles, complicating the identification of the OBD as both toxicity and activity outcomes may occur at any point throughout the follow up of multiple cycles. In this work we present and assess the Joint TITE-CRM, a model-based design for late onset toxicities and activity based on the well-known TITE-CRM. It is found to be superior to the currently available alternative designs that account for late onset bivariate outcomes, as well as being both intuitive and computationally feasible.

It is increasingly common for therapies in oncology to be given in combination. In some cases, patients can benefit from the interaction between two drugs, although often at the risk of higher toxicity. A large number of designs to conduct phase I trials in this setting are available, where the objective is to select the maximum tolerated dose combination. Recently, a number of model-free (also called model-assisted) designs have provoked interest, providing several practical advantages over the more conventional approaches of rule-based or model-based designs. In this paper, we demonstrate a novel calibration procedure for model-free designs to determine their most desirable parameters. Under the calibration procedure, we compare the behaviour of model-free designs to model-based designs in a comprehensive simulation study, covering a number of clinically plausible scenarios. It is found that model-free designs are competitive with the model-based designs in terms of the proportion of correct selections of the maximum tolerated dose combination. However, there are a number of scenarios in which model-free designs offer a safer alternative. This is also illustrated in the application of the designs to a case study using data from a phase I oncology trial.

Background: The use of ‘backfilling’, assigning additional patients to doses deemed safe, in phase I dose-escalation studies has been used in practice to collect additional information on the safety profile, pharmacokinetics and activity of a drug. These additional patients help ensure that the maximum tolerated dose is reliably estimated and give additional information to determine the recommended phase II dose. Methods: In this article, we study the effect of employing backfilling in a phase I trial on the estimation of the maximum tolerated dose and the duration of the study. We consider the situation where only one cycle of follow-up is used for escalation as well as the case where there may be delayed onset toxicities. Results: We find that, over a range of scenarios, the use of backfilling gives an increase in the percentage of correct selections by up to 9%. On average, for a treatment with a cycle length of 6 weeks, each additional backfilling patient reduces the trial duration by half a week. Conclusions: Backfilling in phase I dose-escalation studies can substantially increase the accuracy of estimation of the maximum tolerated dose, with a larger impact in the setting with a dose-limiting toxicity event assessment period of only one cycle. This increased accuracy and reduction in the trial duration are at the cost of increased sample size.

It is a tried and tested technique to gauge the overall understanding of a class: a multiple-choice quiz with a show of hands for who thinks the answer is a, b or c. Although quick and easy, how much does it really measure the students’ understanding? On top of that, how useful is it as an informal formative assessment? A few students usually dominate the class and less confident students may not put up their hand, or may follow what their classmates are doing, and hence both the learner and educator may never know the individuals’ true answer. Here we discuss “an updated show of hands”, whereby students scan a QR code to take them to a real-time quiz hosted on the Moodle Virtual Learning Environment (VLE), that they can answer on their smart device. All students answer the same question at the same time, and after a set time, the correct answer is revealed and the class results for that question are then displayed to everyone as an anonymous percentage. Whilst this updated method has the obvious advantage of anonymity and the obvious disadvantage of potential technical problems, in this case study we provide a full description of the implementation and an in-depth discussion on the pedagogy and practicalities of the updated show of hands – the real-time smart device quiz.

The most common objective for response-adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate-adjusted response-adaptive randomization with forward-looking Gittins Index (CARA-FLGI) Rule for binary responses. The performance of the novel test is evaluated in simulations for two-armed studies and then its applications to multiarmed studies are illustrated. The proposed test has markedly improved power over the traditional Fisher exact test when this class of nonmyopic response adaptation is used. We also find that the test's power is close to the power of a Fisher exact test under equal randomization.

An objective of phase I dose‐finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model‐based approaches in general outperform the model‐assisted approaches, with an interval censored approach and a modified version of the time‐to‐event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods.

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

In traditional toxicology trials, pharmacokinetics (PK) is investigated in the satellite group of animals, and the pharmacodynamics (PD) is investigated in the study group of animals. The new blood sampling method of microsampling opens up the opportunity to investigate both PK and PD in the same animals. To avoid excessive burden on the animals from the required blood sampling, sparse sampling schemes are typically utilized. Motivated by this application, this paper introduces a procedure to choose an optimal sparse sampling scheme and sampling time points using non-compartmental methods but which can be applied to further settings beyond this. We discuss how robust designs can be obtained and we apply and evaluate the approach to a range of scenarios to give an example of how it may be implemented. The results are compared to optimal designs for model based PK.

Pharmacokinetic studies aim to study how a compound is absorbed, distributed, metabolised, and excreted. The concentration of the compound in the blood or plasma is measured at different time points after administration and pharmacokinetic parameters such as the area under the curve (AUC) or maximum concentration (C max ) are derived from the resulting concentration time profile. In this paper, we want to compare different methods for collecting concentration measurements (traditional sampling versus microsampling) on the basis of these derived parameters. We adjust and evaluate an existing method for testing superiority of multiple derived parameters that accounts for model uncertainty. We subsequently extend the approach to allow testing for equivalence. We motivate the methods through an illustrative example and evaluate the performance using simulations. The extensions show promising results for application to the desired setting.