Exposure–response modelling approaches for determining optimal dosing rules in children

Within paediatric populations, there may be distinct age groups characterised by different exposure–response relationships. Several regulatory guidance documents have suggested general age groupings. However, it is not clear whether these categorisations will be suitable for all new medicines and in all disease areas. We consider two model-based approaches to quantify how exposure–response model parameters vary over a continuum of ages: Bayesian penalised B-splines and model-based recursive partitioning. We propose an approach for deriving an optimal dosing rule given an estimate of how exposure–response model parameters vary with age. Methods are initially developed for a linear exposure–response model. We perform a simulation study to systematically evaluate how well the various approaches estimate linear exposure–response model parameters and the accuracy of recommended dosing rules. Simulation scenarios are motivated by an application to epilepsy drug development. Results suggest that both bootstrapped model-based recursive partitioning and Bayesian penalised B-splines can estimate underlying changes in linear exposure–response model parameters as well as (and in many scenarios, better than) a comparator linear model adjusting for a categorical age covariate with levels following International Conference on Harmonisation E11 groupings. Furthermore, the Bayesian penalised B-splines approach consistently estimates the intercept and slope more accurately than the bootstrapped model-based recursive partitioning. Finally, approaches are extended to estimate Emax exposure–response models and are illustrated with an example motivated by an in vitro study of cyclosporine

Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats

BACKGROUND: Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials. CONCLUSION: Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework:Lessons learned

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed

Repeated measures regression mixture models

Regression mixture models are one increasingly utilized approach for developing theories about and exploring the heterogeneity of effects. In this study we aimed to extend the current use of regression mixtures to a repeated regression mixture method when repeated measures, such as diary-type and experience-sampling method, data are available. We hypothesized that additional information borrowed from the repeated measures would improve the model performance, in terms of class enumeration and accuracy of the parameter estimates. We specifically compared three types of model specifications in regression mixtures: (a) traditional single-outcome model; (b) repeated measures models with three, five, and seven measures; and (c) a single-outcome model with the average of seven repeated measures. The results showed that the repeated measures regression mixture models substantially outperformed the traditional and average single-outcome models in class enumeration, with less bias in the parameter estimates. For sample size, whereas prior recommendations have suggested that regression mixtures require samples of well over 1,000 participants, even for classes at a large distance from each other (classes with regression weights of.20 vs.70), the present repeated measures regression mixture models allow for samples as low as 200 participants with an increased number (i.e., seven) of repeated measures. We also demonstrate an application of the proposed repeated measures approach using data from the Sleep Research Project. Implications and limitations of the study are discussed

Instrumental variable estimation in semi-parametric additive hazards models

Instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two-stage least-squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct estimation of time-independent covariate effects without restricting the effect of the residual on the hazard. In this article we prove asymptotic normality of two-stage residual inclusion estimators of regression coefficients in a semi-parametric additive hazard model with time-independent and time-dependent covariate effects. We consider the cases of continuous and binary exposure. Estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands. The new methods are compared to existing ones in a simulation study and are applied to a real data set. The proposed methods perform favourably especially in cases with exposure-dependent censoring

Point estimation for adaptive trial designs II: practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred

Physical activity interventions and therapeutic exercise in adults with rare neurological disorders: development of a core outcome measure set

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Astrophysical Reaction Rates for 10^{10}B(p,α\alpha)7^{7}Be and 11^{11}B(p,α\alpha)8^{8}Be From a Direct Model

The reactions $^{10}$B(p,$\alpha$)$^{7}$Be and $^{11}$B(p,$\alpha$)$^{8}$Be are studied at thermonuclear energies using DWBA calculations. For both reactions, transitions to the ground states and first excited states are investigated. In the case of $^{10}$B(p,$\alpha$)$^{7}$Be, a resonance at $E_{Res}=10$ keV can be consistently described in the potential model, thereby allowing the extension of the astrophysical $S$-factor data to very low energies. Strong interference with a resonance at about $E_{Res}=550$ keV require a Breit-Wigner description of that resonance and the introduction of an interference term for the reaction $^{10}$B(p,$\alpha_1$)$^{7}$Be$^*$. Two isospin $T=1$ resonances (at $E_{Res1}=149$ keV and $E_{Res2}=619$ keV) observed in the $^{11}$B+p reactions necessitate Breit-Wigner resonance and interference terms to fit the data of the $^{11}$B(p,$\alpha$)$^{8}$Be reaction. $S$-factors and thermonuclear reaction rates are given for each reaction. The present calculation is the first consistent parametrization for the transition to the ground states and first excited states at low energies.Comment: 27 pages, 5 Postscript figures, uses RevTex and aps.sty; preprint also available at http://quasar.physik.unibas.ch/ Phys. Rev. C, in pres

Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”: a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

AbstractIn the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals “to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines”. The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing — making anonymised individual-level clinical trial data available to other investigators for further use — is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council’s Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.</jats:p

Study to evaluate the optimal dose of remifentanil required to ensure apnea during magnetic resonance imaging of the heart under general anesthesia

Background: Magnetic resonance (MRI) scanning of the heart is an established part of the investigation of cardiovascular conditions in children. In young children, sedation is likely to be needed, and multiple controlled periods of apnea are often required to allow image acquisition. Suppression of spontaneous ventilation is possible with remifentanil; however, the dose required is uncertain. Aims: To establish the dose of remifentanil, by infusion, required to suppress ventilation sufficiently to allow a 30-s apnea during MRI imaging of the heart. Method: Patients aged 1–6 years were exposed to different doses of remifentanil, and the success in achieving a 30-s apnea was recorded. A dose recommendation was made for each patient, informed by responses of previous patients using an adaptive Bayesian dose-escalation design. Other aspects of anesthesia were standardized. A final estimate of the dose needed to achieve a successful outcome in 80% of patients (ED80) was made using logistic regression. Results: 38 patients were recruited, and apnea achieved in 31 patients. The estimate of the ED80 was 0.184 µg/kg/min (95% CI 0.178–0.190). Post hoc analysis revealed that higher doses were required in younger patients. Conclusion: The ED80 for this indication was 0.184 µg/kg/min (95% CI 0.178–0.190). This is different from optimal dosing identified for other indications and dosing of remifentanil should be specific to the clinical context in which it is used