Uniformly minimum variance conditionally unbiased estimation in multi-arm multi-stage clinical trials

Multi-arm multi-stage clinical trials compare several experimental treatments with a control treatment, with poorly performing treatments dropped at interim analyses. This leads to inferential challenges, including the construction of unbiased treatment effect estimators. A number of estimators unbiased conditional on treatment selection have been proposed, but are specific to certain selection rules, may ignore the comparison to the control and are not all minimum variance. We obtain estimators for treatment effects compared to the control that are uniformly minimum variance unbiased conditional on selection with any specified rule or stopping for futility

Estimation after subpopulation selection in adaptive seamless trials

During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios

Extrapolating parametric survival models in health technology assessment : a simulation study

Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods’ suitability for selecting a parametric model through simulation of data replicating the follow-up of intervention arms for various time-to-event outcomes from 4 clinical trials. Follow-up was replicated through the consideration of recruitment duration and minimum and maximum follow-up times. Ten thousand simulations of each scenario were performed. We demonstrate that the different methods can result in disagreement over the best model and that it is inappropriate to base model selection solely on goodness-of-fit statistics without consideration of hazard behavior and plausibility of extrapolations. We show that typical trial follow-up can be unsuitable for extrapolation, resulting in unreliable estimation of multiple parameter models, and infer that selecting survival models based only on goodness-of-fit statistics is unsuitable due to the high level of uncertainty in a cost-effectiveness analysis. This article demonstrates the potential problems of overreliance on goodness-of-fit statistics when selecting a model for extrapolation. When follow-up is more mature, BIC appears superior to the other selection methods, selecting models with the most accurate and least biased estimates of RMST

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Escape rate and Hausdorff measure for entire functions

The escaping set of an entire function is the set of points that tend to infinity under iteration. We consider subsets of the escaping set defined in terms of escape rates and obtain upper and lower bounds for the Hausdorff measure of these sets with respect to certain gauge functions.Comment: 24 pages; some errors corrected, proof of Theorem 2 shortene

Designing Digital Content to Support Science Journalism

Journalists need to become more effective at communicating science and countering post-truth activities that seek to undermine scientific processes and evidence. Digital support for journalists when investigating and writing about sciencerelated topics is one means of improving this science communication. However, little bespoke digital support is available. This paper reports the research and development of one new form of such digital support. During a participatory design process, experienced science journalists and other professionals were interviewed about their challenges experienced and understanding of good practices in science journalism. These challenges and good practices informed the development of a prototype of a new form of digital tool that was evaluated by journalists without specialist science training. A new version of the prototype, called INQUEST, was implemented to automate some parts of good practices in order to augment journalists’ capabilities. These practices included the retrieval of science information from diverse sources, targeting different science audiences, and providing different forms of guidance for explaining science to the target audience. This prototype is presented, and an early evaluation of it is reported

Physiotherapy rehabilitation for osteoporotic vertebral fracture (PROVE) : study protocol for a randomised controlled trial

Background: Osteoporosis and vertebral fracture can have a considerable impact on an individual’s quality of life. There is increasing evidence that physiotherapy including manual techniques and exercise interventions may have an important treatment role. This pragmatic randomised controlled trial will investigate the clinical and cost-effectiveness of two different physiotherapy approaches for people with osteoporosis and vertebral fracture, in comparison to usual care. Methods/Design: Six hundred people with osteoporosis and a clinically diagnosed vertebral fracture will be recruited and randomly allocated to one of three management strategies, usual care (control - A), an exercise-based physiotherapy intervention (B) or a manual therapy-based physiotherapy intervention (C). Those in the usual care arm will receive a single session of education and advice, those in the active treatment arms (B + C) will be offered seven individual physiotherapy sessions over 12 weeks. The trial is designed as a prospective, adaptive single-blinded randomised controlled trial. An interim analysis will be completed and if one intervention is clearly superior the trial will be adapted at this point to continue with just one intervention and the control. The primary outcomes are quality of life measured by the disease specific QUALLEFO 41 and the Timed Loaded Standing test measured at 1 year. Discussion: There are a variety of different physiotherapy packages used to treat patients with osteoporotic vertebral fracture. At present, the indication for each different therapy is not well defined, and the effectiveness of different modalities is unknown