Dose selection in seamless phase II/III clinical trials based on efficacy and toxicity

Seamless phase II/III clinical trials are attractive in development of new drugs because they accelerate the drug development process. Seamless phase II/III trials are carried out in two stages. After stage 1 (phase II stage), an interim analysis is performed and a decision is made on whether to proceed to stage 2 (phase III stage). If the decision is to continue with further testing, some dose selection procedure is used to determine the set of doses to be tested in stage 2. Methodology exists for the analysis of such trials that allows complete flexibility of the choice of doses that continue to the second stage. There is very little work, however, on optimizing the selection of the doses. This is a challenging problem as it requires incorporation of the dose-response relationship, of the observed safety profile and of the planned analysis method. In this thesis we propose a dose-selection procedure for binary outcomes in adaptive seamless phase II/III clinical trials that incorporates the dose response relationship, and explicitly incorporates both efficacy and toxicity. The choice of the doses to continue to stage 2 is made by comparing the predictive power of the potential sets of doses which might continue to stage 2

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

Utilisation of pharmacy-based sexual and reproductive health services : a quantitative retrospective study

Objectives: To explore the utilisation of pharmacy-based sexual and reproductive health services (SRHS) in order to optimise delivery and identify barriers to access. Methods: The health provider Umbrella offers six SRHS from over 120 pharmacies in Birmingham (England). In this retrospective study, data collected between August 2015 and August 2018 were used to analyse uptake, user characteristics and attendance patterns according to day of the week. Results: A total of 60 498 requests for a pharmacy service were included in the analysis. Emergency contraception (50.4%), condoms (33.1%) and STI self-sampling kits (9.6%) accounted for more than 90% of all requests. A lower uptake of services was observed for the contraceptive injection (0.6%), oral contraception (5.4%) and chlamydia treatment (1.0%). Services were most likely to be requested by those self-identifying as female (85.6%), and those aged 16–24 years (53.8%). Based on available ethnicity data (n=54 668), most requests for a service were made by White/White British individuals (43.4%) and Asian/Asian British people (23.1%). The largest number of services were delivered on Mondays (20.9%) and the lowest on Sundays (5.0%). A high proportion of requests for services on Saturdays (57.0%), Sundays (67.6%) and Mondays (54.4%) were made by females presenting for emergency contraception. Conclusion: The evaluation of healthcare utilisation is important to help refine and optimise the delivery of services. However, information relating to pharmacy-based SRHS is scarce and often limited to a single type of service provision. Overall, a wide range of pharmacy-based services were accessed by a diverse range of people, suggesting that pharmacies are a suitable provider of many SRHS. However, the routinely collected data analysed in the study had several limitations restricting the analysis. Sexual health providers should ensure they collect data which are as comprehensive as is possible in order to help understand the utilisation of services

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

Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations

Impact of COVID-19 on clinical outcomes for patients with fractured hip

AIMS: There are reports of a marked increase in perioperative mortality in patients admitted to hospital with a fractured hip during the COVID-19 pandemic in the UK, USA, Spain, and Italy. Our study aims to describe the risk of mortality among patients with a fractured neck of femur in England during the early stages of the COVID-19 pandemic. Methods: We completed a multicentre cohort study across ten hospitals in England. Data were collected from 1 March 2020 to 6 April 2020, during which period the World Health Organization (WHO) declared COVID-19 to be a pandemic. Patients ≥ 60 years of age admitted with hip fracture and a minimum follow-up of 30 days were included for analysis. Primary outcome of interest was mortality at 30 days post-surgery or postadmission in nonoperative patients. Secondary outcomes included length of hospital stay and discharge destination. Results: In total, 404 patients were included for final analysis with a COVID-19 diagnosis being made in 114 (28.2%) patients. Overall, 30-day mortality stood at 14.4% (n = 58). The COVID-19 cohort experienced a mortality rate of 32.5% (37/114) compared to 7.2% (21/290) in the non-COVID cohort (p < 0.001). In adjusted analysis, 30-day mortality was greatest in patients who were confirmed to have COVID-19 (odds ratio (OR) 5.64, 95% confidence interval (CI) 2.95 to 10.80; p < 0.001) with an adjusted excess risk of 20%, male sex (OR 2.69, 95% CI 1.37 to 5.29; p = 0.004) and in patients with ≥ two comorbidities (OR 4.68, CI 1.5 to 14.61; p = 0.008). Length of stay was also extended in the COVID-19 cohort, on average spending 17.6 days as an inpatient versus 12.04 days in the non-COVID-19 group (p < 0.001). Conclusion: This study demonstrates that patients who sustain a neck of femur fracture in combination with COVID-19 diagnosis have a significantly higher risk of mortality than would be normally expected