Efficient two-stage sequential arrays of proof of concept studies for pharmaceutical portfolios

Previous work has shown that individual randomized "proof-of-concept" (PoC) studies may be designed to maximize cost-effectiveness, subject to an overall PoC budget constraint. Maximizing cost-effectiveness has also been considered for arrays of simultaneously executed PoC studies. Defining Type III error as the opportunity cost of not performing a PoC study, we evaluate the common pharmaceutical practice of allocating PoC study funds in two stages. Stage 1, or the first wave of PoC studies, screens drugs to identify those to be permitted additional PoC studies in Stage 2. We investigate if this strategy significantly improves efficiency, despite slowing development. We quantify the benefit, cost, benefit-cost ratio, and Type III error given the number of Stage 1 PoC studies. Relative to a single stage PoC strategy, significant cost-effective gains are seen when at least one of the drugs has a low probability of success (10%) and especially when there are either few drugs (2) with a large number of indications allowed per drug (10) or a large portfolio of drugs (4). In these cases, the recommended number of Stage 1 PoC studies ranges from 2 to 4, tracking approximately with an inflection point in the minimization curve of Type III error.

Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology

In the analysis of survival times, the logrank test and the Cox model have been established as key tools, which do not require specific distributional assumptions. Under the assumption of proportional hazards, they are efficient and their results can be interpreted unambiguously. However, delayed treatment effects, disease progression, treatment switchers or the presence of subgroups with differential treatment effects may challenge the assumption of proportional hazards. In practice, weighted logrank tests emphasizing either early, intermediate or late event times via an appropriate weighting function may be used to accommodate for an expected pattern of non‐proportionality. We model these sources of non‐proportional hazards via a mixture of survival functions with piecewise constant hazard. The model is then applied to study the power of unweighted and weighted log‐rank tests, as well as maximum tests allowing different time dependent weights. Simulation results suggest a robust performance of maximum tests across different scenarios, with little loss in power compared to the most powerful among the considered weighting schemes and huge power gain compared to unfavorable weights. The actual sources of non‐proportional hazards are not obvious from resulting populationwise survival functions, highlighting the importance of detailed simulations in the planning phase of a trial when assuming non‐proportional hazards.We provide the required tools in a software package, allowing to model data generating processes under complex non‐proportional hazard scenarios, to simulate data from these models and to perform the weighted logrank tests.

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

Analysis of Austrian COVID-19 deaths by age and sex

We analyze the age and sex distribution of the reported COVID-19 deaths in Austria. In accordance with international studies, the Austrian data also suggests that the risk of death increases substantially with age. The observed age dependency of the proportions of registered COVID-19 deaths in relation to the population sizes in the age groups is approximately exponential, similar to the age dependency of the general age specific mortality rate. Furthermore, we compare the general age specific mortality rate in Austria with the estimates of the SARS-CoV‑2 infection fatality rate by Ferguson et al. (2020). The parallels to the general age specific mortality rates do not imply that COVID-19 does not pose an additional risk. On the contrary, it follows from the structure and magnitude of the infection fatality rate that it is substantial, especially for higher age groups. However, since in many cases persons with severe pre-existing conditions are affected, it is not yet possible to estimate what effects COVID-19 will have on life expectancy.

Threshold effects of air pollution and climate change on understory plant communities at forested sites in the eastern United States

Forest understory plant communities in the eastern United States are often diverse and are potentially sensitive to changes in climate and atmospheric inputs of nitrogen caused by air pollution. In recent years, empirical and processed-based mathematical models have been developed to investigate such changes in plant communities. In the study reported here, a robust set of understory vegetation response functions (expressed as version 2 of the Probability of Occurrence of Plant Species model for the United States [US-PROPS v2]) was developed based on observations of forest understory and grassland plant species presence/absence and associated abiotic characteristics derived from spatial datasets. Improvements to the US-PROPS model, relative to version 1, were mostly focused on inclusion of additional input data, development of custom species-level input datasets, and implementation of methods to address uncertainty. We investigated the application of US-PROPS v2 to evaluate the potential impacts of atmospheric nitrogen (N) and sulfur (S) deposition, and climate change on forest ecosystems at three forested sites located in New Hampshire, Virginia, and Tennessee in the eastern United States. Species-level N and S critical loads (CLs) were determined under ambient deposition at all three modeled sites. The lowest species-level CLs of N deposition at each site were between 2 and 11 kg N/ha/yr. Similarly, the lowest CLs of S deposition, based on the predicted soil pH response, were less than 2 kg S/ha/yr among the three sites. Critical load exceedance was found at all three model sites. The New Hampshire site included the largest percentage of species in exceedance. Simulated warming air temperature typically resulted in lower maximum occurrence probability, which contributed to lower CLs of N and S deposition. The US-PROPS v2 model, together with the PROPS-CLF model to derive CL functions, can be used to develop site-specific CLs for understory plants within broad regions of the United States. This study demonstrates that species-level CLs of N and S deposition are spatially variable according to the climate, light availability, and soil characteristics at a given location. Although the species niche models generally performed well in predicting occurrence probability, there remains uncertainty with respect to the accuracy of reported CLs. As such, the specific CLs reported here should be considered as preliminary estimates.

FRI0084 SAFETY, PHARMACOKINETICS, PHARMACODYNAMICS, IMMUNOGENICITY, AND PRELIMINARY EFFICACY OF ROZIBAFUSP ALFA IN SUBJECTS WITH RHEUMATOID ARTHRITIS: INTERIM ANALYSIS OF A PHASE 1B RANDOMIZED, PLACEBO-CONTROLLED, MULTIPLE ASCENDING DOSE CLINICAL TRIAL

Background:

Autoimmune diseases, including systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), are associated with autoantibody production and dysregulated T- and B-cell responses. Rozibafusp alfa (AMG 570) is a first-in-class bispecific antibody-peptide conjugate targeting T- and B-cell activity through inhibition of ICOSL and BAFF and is currently in phase 2 clinical development for the treatment of SLE.

Objectives:

This interim analysis of a phase 1b study (NCT03156023) reports the safety, pharmacokinetics (PK), pharmacodynamics (PD), immunogenicity, and preliminary efficacy of rozibafusp alfa in subjects with RA.

Methods:

Subjects (N~34; age 18–75 years) with active RA, defined as a disease activity score (DAS28-CRP) >2.6, were randomized 3:1 to receive rozibafusp alfa or placebo subcutaneously every 2 weeks for 10 weeks (6 doses), with 24 weeks of follow-up. Subjects were divided into 4 cohorts to study 4 ascending doses of rozibafusp alfa. All subjects were maintained on a stable dose of methotrexate. The primary endpoint was the subject incidence of treatment-emergent adverse events (TEAEs). Additional assessments included serum PK profiles, PD (eg, ICOSL receptor occupancy [RO], changes in peripheral blood B cells), incidence of anti-rozibafusp alfa antibodies, and Patient and Physician Global Assessments (PtGA and PhGA) of disease activity.

Results:

As of June 5, 2019, 34 subjects were enrolled and included in this interim analysis. Rozibafusp alfa was generally well tolerated. TEAEs occurred in 92.3% and 87.5% of subjects receiving rozibafusp alfa and placebo, respectively. Most of these events were of grade ≤2 severity. The most common TEAE was upper respiratory infection (23.1%) for subjects receiving rozibafusp alfa and nasopharyngitis (37.5%) for subjects receiving placebo. No treatment-related AEs were of grade ≥3 severity and occurred in >2 subjects. Rozibafusp alfa demonstrated a nonlinear PK profile with greater than a dose-proportional increase in concentration across evaluated doses. The terminal half-life of rozibafusp alfa ranged from 5 to 11 days, with longer half-lives at higher dose levels. ICOSL RO on circulating B-cells was dose-related and reversible; upon multiple dosing, >90% mean RO was observed in cohorts 3 and 4. Treatment with rozibafusp alfa reduced the percentage of naïve B-cells and increased the percentage of memory B-cells in all cohorts. As of March 22, 2019, 2 of 18 (11.1%) rozibafusp alfa-treated subjects developed anti-rozibafusp alfa antibodies with no correlation to safety or AEs. Preliminary analysis of disease-related activity showed a trend for greater numerical improvement from baseline in PtGA and PhGA with rozibafusp alfa vs. placebo in cohorts 3 and 4.

Conclusion:

This interim analysis is the first to report the safety and tolerability of multiple ascending doses of rozibafusp alfa in RA subjects, with preliminary efficacy findings observed in the highest dose cohorts. PK/PD analysis demonstrated nonlinear, target-mediated disposition consistent with cell surface target interaction and PD activity consistent with dual ICOSL/BAFF neutralization. These findings informed the design and dose selection of an ongoing phase 2, randomized, placebo-controlled study to assess the efficacy and safety of rozibafusp alfa in subjects with active SLE and inadequate responses to standard of care therapy.

Acknowledgments:

Amgen Inc. and AstraZeneca sponsored this phase 1b study

Disclosure of Interests:

Lubna Abuqayyas Shareholder of: Stockholder of Amgen Inc., Employee of: Employee of Amgen Inc., Laurence Cheng Shareholder of: Stockholder of Amgen Inc., Employee of: Former employee of Amgen Inc., Deepali Mitragotri Shareholder of: Stockholder of Amgen Inc., Employee of: Employee of Amgen Inc., Shawna Smith Shareholder of: Stockholder of Amgen Inc., Employee of: Employee of Amgen Inc., Marcia Teixeira dos Santos Shareholder of: Stockholder of Amgen Inc., Employee of: Employee of Amgen Inc., Yanchen Zhou Shareholder of: Stockholder of Amgen Inc., Employee of: Employee of Amgen Inc., Vishala Chindalore Grant/research support from: Nektar Therapeutics for conducted studies, Speakers bureau: > 5 years ago, Stanley Cohen Grant/research support from: Grant and research support from Amgen, AbbVie, Pfizer, Genentech, and Lilly, Consultant of: Consultant for Amgen, AbbVie, Pfizer, Genentech and Lilly, Alan Kivitz Shareholder of: AbbVie, Amgen, Gilead, GSK, Pfizer Inc, Sanofi, Consultant of: AbbVie, Boehringer Ingelheim,,Flexion, Genzyme, Gilead, Janssen, Novartis, Pfizer Inc, Regeneron, Sanofi, SUN Pharma Advanced Research, UCB, Paid instructor for: Celgene, Genzyme, Horizon, Merck, Novartis, Pfizer, Regeneron, Sanofi, Speakers bureau: AbbVie, Celgene, Flexion, Genzyme, Horizon, Merck, Novartis, Pfizer Inc, Regeneron, Sanofi, Maximilian Posch: None declared, Barbara Sullivan Shareholder of: Shareholder of Amgen Inc., Employee of: Former employee of Amgen Inc. Current employee of Ultragenyx, Jane Parnes Shareholder of: Stockholder of Amgen Inc., Employee of: Employee of Amgen Inc.

Optimized multiple testing procedures for nested sub-populations based on a continuous biomarker

An important step in the development of targeted therapies is the identification and confirmation of sub-populations where the treatment has a positive treatment effect compared to a control. These sub-populations are often based on continuous biomarkers, measured at baseline. For example, patients can be classified into biomarker low and biomarker high subgroups, which are defined via a threshold on the continuous biomarker. However, if insufficient information on the biomarker is available, the a priori choice of the threshold can be challenging and it has been proposed to consider several thresholds and to apply appropriate multiple testing procedures to test for a treatment effect in the corresponding subgroups controlling the family-wise type 1 error rate. In this manuscript we propose a framework to select optimal thresholds and corresponding optimized multiple testing procedures that maximize the expected power to identify at least one subgroup with a positive treatment effect. Optimization is performed over a prior on a family of models, modelling the relation of the biomarker with the expected outcome under treatment and under control. We find that for the considered scenarios 3 to 4 thresholds give the optimal power. If there is a prior belief on a small subgroup where the treatment has a positive effect, additional optimization of the spacing of thresholds may result in a large benefit. The procedure is illustrated with a clinical trial example in depression.

A critical review of graphics for subgroup analyses in clinical trials

Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.

A multiple comparison procedure for dose-finding trials with subpopulations

Identifying subgroups of patients with an enhanced response to a new treatment has become an area of increased interest in the last few years. When there is knowledge about possible subpopulations with an enhanced treatment effect before the start of a trial it might be beneficial to set up a testing strategy, which tests for a significant treatment effect not only in the full population, but also in these prespecified subpopulations. In this paper, we present a parametric multiple testing approach for tests in multiple populations for dose-finding trials. Our approach is based on the MCP-Mod methodology, which uses multiple comparison procedures (MCPs) to test for a dose-response signal, while considering multiple possible candidate dose-response shapes. Our proposed methods allow for heteroscedastic error variances between populations and control the family-wise error rate over tests in multiple populations and for multiple candidate models. We show in simulations that the proposed multipopulation testing approaches can increase the power to detect a significant dose-response signal over the standard single-population MCP-Mod, when the specified subpopulation has an enhanced treatment effect.

Simultaneous inference for multiple marginal generalized estimating equation models

Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.

Nested combination tests with a time-to-event endpoint using a short-term endpoint for design adaptations

Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.

Adaptive designs in clinical trials: from scientific advice to marketing authorisation to the European Medicine Agency

Background

In recent years, experience on the application of adaptive designs in confirmatory clinical trials has accumulated. Although planning such trials comes at the cost of additional operational complexity, adaptive designs offer the benefit of flexibility to update trial design and objectives as data accrue. In 2007, the European Medicines Agency (EMA) provided guidance on confirmatory clinical trials with adaptive (or flexible) designs. In order to better understand how adaptive trials are implemented in practice and how they may impact medicine approval within the EMA centralised procedure, we followed on 59 medicines for which an adaptive clinical trial had been submitted to the EMA Scientific Advice (SA) and analysed previously in a dedicated EMA survey of scientific advice letters. We scrutinized in particular the submission of the corresponding medicines for a marketing authorisation application (MAA). We also discuss the current regulatory perspective as regards the implementation of adaptive designs in confirmatory clinical trials.

Methods

Using the internal EMA MAA database, the AdisInsight database and related trial registries, we analysed how many of these 59 trials actually started, the completion status, results, the time to trial start, the adaptive elements finally implemented after SA, their possible influence on the success of the trial and corresponding product approval.

Results

Overall 31 trials out of 59 (53%) were retrieved. Thirty of them (97%) have been started and 23 (74%) concluded. Nine of these trials (39% out of 23) demonstrated a significant treatment effect on their primary endpoint and 4 (17% out of 23) supported a marketing authorisation (MA). An additional two trials were stopped using pre-defined criteria for futility, efficiently identifying trials on which further resources should not be spent. Median time to trial start after SA letter was given by EMA was 5 months. In the investigated trial registries, at least 18 trial (58% of 31 retrieved trials) designs were implemented with adaptive elements, which were predominantly dose selection, sample size reassessment (SSR) and stopping for futility (SFF). Among the 11 completed trials including adaptive elements, 6 demonstrated a significant treatment effect on their primary endpoint (55%).

Conclusions

Adaptive designs are now well established in the drug development landscape. If properly pre-planned, adaptations can play a key role in the success of some of these trials, for example to help successfully select the most promising dose regimens for phase II/III trials. Interim analyses can also enable stopping of trials for futility when they do not hold their promises. Type I error rate control, trial integrity and results consistency between the different stages of the analyses are fundamental aspects to be discussed thoroughly. Engaging early dialogue with regulators and implementing the scientific advice received is strongly recommended, since much experience in discussing adaptive designs and assessing their results has been accumulated.

Evidence supporting regulatory-decision making on orphan medicinal products authorisation in Europe: methodological uncertainties

BACKGROUND

To assess uncertainty in regulatory decision-making for orphan medicinal products (OMP), a summary of the current basis for approval is required; a systematic grouping of medical conditions may be useful in summarizing information and issuing recommendations for practice.

METHODS

A grouping of medical conditions with similar characteristics regarding the potential applicability of methods and designs was created using a consensus approach. The 125 dossiers for authorised OMP published between 1999 and 2014 on the EMA webpage were grouped accordingly and data was extracted from European Public Assessment Reports (EPARs) to assess the extent and robustness of the pivotal evidence supporting regulatory decisions.

RESULTS

88% (110/125) of OMP authorizations were based on clinical trials, with 35% (38/110) including replicated pivotal trials. The mean (SD) number of pivotal trials per indication was 1.4 (0.7), and the EPARs included a median of three additional non-pivotal supportive studies. 10% of OMPs (13/125) were authorised despite only negative pivotal trials. One-third of trials (53/159) did not include a control arm, one-third (50/159) did not use randomisation, half the trials (75/159) were open-label and 75% (119/159) used intermediate or surrogate variables as the main outcome. Chronic progressive conditions led by multiple system/organs, conditions with single acute episodes and progressive conditions led by one organ/system were the groups where the evidence deviated most from conventional standards. Conditions with recurrent acute episodes had the most robust datasets. The overall size of the exposed population at the time of authorisation of OMP - mean(SD) 190.5 (202.5) - was lower than that required for the qualification of clinically-relevant adverse reactions.

CONCLUSIONS

The regulatory evidence supporting OMP authorization showed substantial uncertainties, including weak protection against errors, substantial use of designs unsuited for conclusions on causality, use of intermediate variables, lack of a priorism and insufficient safety data to quantify risks of relevant magnitude. Grouping medical conditions based on clinical features and their methodological requirements may facilitate specific methodological and regulatory recommendations for the study of OMP to strengthen the evidence base.

Applicability and added value of novel methods to improve drug development in rare diseases

BACKGROUND

The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports.

METHODS

The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were 'innovative trial designs' (six methods), 'level of evidence' (one method), 'study endpoints and statistical analysis' (four methods), and 'meta-analysis' (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters.

RESULTS AND DISCUSSION

Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters.

CONCLUSION

Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes.

Recent advances in methodology for clinical trials in small populations: the InSPiRe project

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.

Subgroup identification in clinical trials via the predicted individual treatment effect

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

A proposal for a new PhD level curriculum on quantitative methods for drug development

This paper provides an overview of "Improving Design, Evaluation and Analysis of early drug development Studies" (IDEAS), a European Commission-funded network bringing together leading academic institutions and small- to large-sized pharmaceutical companies to train a cohort of graduate-level medical statisticians. The network is composed of a diverse mix of public and private sector partners spread across Europe, which will host 14 early-stage researchers for 36 months. IDEAS training activities are composed of a well-rounded mixture of specialist methodological components and generic transferable skills. Particular attention is paid to fostering collaborations between researchers and supervisors, which span academia and the private sector. Within this paper, we review existing medical statistics programmes (MSc and PhD) and highlight the training they provide on skills relevant to drug development. Motivated by this review and our experiences with the IDEAS project, we propose a concept for a joint, harmonised European PhD programme to train statisticians in quantitative methods for drug development.

Design and estimation in clinical trials with subpopulation selection

Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst‐case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.

Methods for the analysis of multiple endpoints in small populations: A review

While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. This paper reviews approaches based on a combination of test statistics or measurements across endpoints as well as multiple testing procedures that allow for confirmatory conclusions on individual endpoints. We especially focus on feasibility in trials with small sample sizes and do not solely rely on asymptotic considerations. A systematic literature search in the Scopus database, supplemented by a manual search, was performed to identify research papers on analysis methods for multiple endpoints with relevance to small populations. The identified methods were grouped into approaches that combine endpoints into a single measure to increase the power of statistical tests and methods to investigate differential treatment effects in several individual endpoints by multiple testing.

Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup

Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.