A flexible Bayesian approach for modeling monotonic dose-response relationships in drug development trials.

Clinical trials often involve comparing 2-4 doses or regimens of an experimental therapy with a control treatment. These studies might occur early in a drug development process, where the aim might be to demonstrate a basic level of proof (the so-called proof of concept (PoC) studies), at a later stage, to help establish a dose or doses that should be used in phase III trials (dose-finding), or even in confirmatory studies, where the registration of several doses might be considered. When a small number of doses are examined, the ability to implement parametric modeling is somewhat limited. As an alternative, in this paper, a flexible Bayesian model is suggested. In particular, we draw on the idea of using Bayesian model averaging (BMA) to exploit an assumed monotonic dose-response relationship, without using strong parametric assumptions. The approach is exemplified by assessing operating characteristics in the design of a PoC study examining a new treatment for psoriatic arthritis and a post hoc data analysis involving three confirmatory clinical trials, which examined an adjunctive treatment for partial epilepsy. Key difficulties, such as prior specification and computation, are discussed. A further extension, based on combining the flexible modeling with a classical multiple comparisons procedure, known as MCP-MOD, is examined. The benefit of this extension is a potential reduction in the number of simulations that might be needed to investigate operating characteristics of the statistical analysis.

Rationale for the selection of dual primary endpoints in prevention studies of cognitively unimpaired individuals at genetic risk for developing symptoms of Alzheimer's disease.

BACKGROUND: There is a critical need for novel primary endpoints designed to detect early and subtle changes in cognition in clinical trials targeting the asymptomatic (preclinical) phase of Alzheimer's disease (AD). The Alzheimer's Prevention Initiative (API) Generation Program, conducted in cognitively unimpaired individuals at risk of developing AD (e.g., enriched by the apolipoprotein E (APOE) genotype), used a novel dual primary endpoints approach, whereby demonstration of treatment effect in one of the two endpoints is sufficient for trial success. The two primary endpoints were (1) time to event (TTE)-with an event defined as a diagnosis of mild cognitive impairment (MCI) due to AD and/or dementia due to AD-and (2) change from baseline to month 60 in the API Preclinical Composite Cognitive (APCC) test score. METHODS: Historical observational data from three sources were used to fit models to describe the TTE and the longitudinal APCC decline, both in people who do and do not progress to MCI or dementia due to AD. Clinical endpoints were simulated based on the TTE and APCC models to assess the performance of the dual endpoints versus each of the two single endpoints, with the selected treatment effect ranging from a hazard ratio (HR) of 0.60 (40% risk reduction) to 1 (no effect). RESULTS: A Weibull model was selected for TTE, and power and linear models were selected to describe the APCC score for progressors and non-progressors, respectively. Derived effect sizes in terms of reduction of the APCC change from baseline to year 5 were low (0.186 for HR = 0.67). The power for the APCC alone was consistently lower compared to the power of TTE alone (58% [APCC] vs 84% [TTE] for HR = 0.67). Also, the overall power was higher for the 80%/20% distribution (82%) of the family-wise type 1 error rate (alpha) between TTE and APCC compared to 20%/80% (74%). CONCLUSIONS: Dual endpoints including TTE and a measure of cognitive decline perform better than the cognitive decline measure as a single primary endpoint in a cognitively unimpaired population at risk of AD (based on the APOE genotype). Clinical trials in this population, however, need to be large, include older age, and have a long follow-up period of at least 5 years to be able to detect treatment effects.

Bayesian models for subgroup analysis in clinical trials.

BACKGROUND: In a pharmaceutical drug development setting, possible interactions between the treatment and particular baseline clinical or demographic factors are often of interest. However, the subgroup analysis required to investigate such associations remains controversial. Concerns with classical hypothesis testing approaches to the problem include low power, multiple testing, and the possibility of data dredging. PURPOSE: As an alternative to hypothesis testing, the use of shrinkage estimation techniques is investigated in the context of an exploratory post hoc subgroup analysis. A range of models that have been suggested in the literature are reviewed. Building on this, we explore a general modeling strategy, considering various options for shrinkage of effect estimates. This is applied to a case-study, in which evidence was available from seven-phase II-III clinical trials examining a novel therapy, and also to two artificial datasets with the same structure. METHODS: Emphasis is placed on hierarchical modeling techniques, adopted within a Bayesian framework using freely available software. A range of possible subgroup model structures are applied, each incorporating shrinkage estimation techniques. RESULTS: The investigation of the case-study showed little evidence of subgroup effects. Because inferences appeared to be consistent across a range of well-supported models, and model diagnostic checks showed no obvious problems, it seemed this conclusion was robust. It is reassuring that the structured shrinkage techniques appeared to work well in a situation where deeper inspection of the data suggested little evidence of subgroup effects. LIMITATIONS: The post hoc examination of subgroups should be seen as an exploratory analysis, used to help make better informed decisions regarding potential future studies examining specific subgroups. To a certain extent, the degree of understanding provided by such assessments will be limited by the quality and quantity of available data. CONCLUSIONS: In light of recent interest by health authorities into the use of subgroup analysis in the context of drug development, it appears that Bayesian approaches involving shrinkage techniques could play an important role in this area. Hopefully, the developments outlined here provide useful methodology for tackling such a problem, in-turn leading to better informed decisions regarding subgroups.

Designing a non-inferiority study in kidney transplantation: a case study.

In organ transplantation, placebo-controlled clinical trials are not possible for ethical reasons, and hence non-inferiority trials are used to evaluate new drugs. Patients with a transplanted kidney typically receive three to four immunosuppressant drugs to prevent organ rejection. In the described case of a non-inferiority trial for one of these immunosuppressants, the dose is changed, and another is replaced by an investigational drug. This test regimen is compared with the active control regimen. Justification for the non-inferiority margin is challenging as the putative placebo has never been studied in a clinical trial. We propose the use of a random-effect meta-regression, where each immunosuppressant component of the regimen enters as a covariate. This allows us to make inference on the difference between the putative placebo and the active control. From this, various methods can then be used to derive the non-inferiority margin. A hybrid of the 95/95 and synthesis approach is suggested. Data from 51 trials with a total of 17,002 patients were used in the meta-regression. Our approach was motivated by a recent large confirmatory trial in kidney transplantation. The results and the methodological documents of this evaluation were submitted to the Food and Drug Administration. The Food and Drug Administration accepted our proposed non-inferiority margin and our rationale.

Dose-response analysis of ranibizumab as-needed regimens for visual improvement in patients with diabetic macular edema using a modelling approach.

BACKGROUND: Ranibizumab and aflibercept are anti-vascular endothelial growth factor therapies for diabetic macular edema (DME) but have only been directly compared in one study: the Protocol T study, a 24-month randomized controlled trial which compared the safety and efficacy of three anti-VEGF agents (ranibizumab 0.3 mg, aflibercept 2.0 mg and bevacizumab 1.25 mg). The ranibizumab dose used in Protocol T is not licensed for use outside of the US, where a higher ranibizumab dose of 0.5 mg is approved. Therefore, the relevance of the head-to-head Protocol T study findings to healthcare providers in Europe is limited. The purpose of this research was to predict the visual outcomes that may have been achieved in Protocol T with ranibizumab 0.5 mg. METHODS: A simplified dose-response model was constructed to describe the relationship between average monthly dose and one-year best corrected visual acuity (BCVA) change from baseline. A linear mixed effects model was evaluated and Bayesian Monte-Carlo Markov chains method was used to estimate the model parameters. RESULTS: If ranibizumab 0.5 mg PRN had been studied in Protocol T, it would have resulted in a BCVA gain of 14-15 early treatment diabetic retinopathy study (ETDRS) letters; 3-4 letters more than the actual BCVA gain reported with ranibizumab 0.3 mg PRN. In Protocol T patients with poor baseline BCVA (<69 letters), a similar additional letter gain would have been achieved. CONCLUSION: The relevance of the Protocol T study findings are limited due to the use of ranibizumab 0.3 mg PRN which, based on the modelling approach reported herein, resulted in sub-optimal visual gains.

Neovascular Age-Related Macular Degeneration: A Visual Acuity Model of Natural Disease Progression and Ranibizumab Treatment Effect.

Intravitreal ranibizumab is a first-line therapy for neovascular age-related macular degeneration (nAMD), but there is a need to optimize patient outcomes while minimizing treatment burden. Here, we developed an indirect response, nonlinear, mixed effects model of disease progression and drug effect in anti-vascular endothelial growth factor (VEGF) treatment-naïve patients. A total of 1,524 treatment-naïve patients and 29,754 visual acuity observations from the ANCHOR, MARINA, PIER, and EXCITE clinical trials informed the model. The model accurately described natural nAMD disease progression and predicted mean visual acuity gains in the HARBOR study, notably with a 2.0 mg ranibizumab dose not used for model development. Furthermore, individualized treatment regimens were shown by simulation to be a viable alternative to the commonly used pro re nata or fixed monthly dosing regimen approaches. Therefore, this model could be a useful tool to predict the outcomes of different, more patient-tailored treatment regimens in nAMD.

Imatinib pharmacokinetics in patients with gastrointestinal stromal tumour: a retrospective population pharmacokinetic study over time. EORTC Soft Tissue and Bone Sarcoma Group.

Imatinib pharmacokinetics (PK) may be affected by a number of factors that are related to the disease being treated and to the response of that disease to imatinib. Patients in the phase I and phase II trials conducted by the EORTC in patients with gastrointestinal stromal tumours (GISTs) and other sarcomas had detailed blood sampling for imatinib PK on day 1 and on day 29. Patients with GISTs also had repeat sampling, using a limited sampling strategy, after approximately 12 months on therapy. This population PK study was carried out to examine what covariates affected imatinib PK in GIST patients and what PK changes occurred over time. In the model producing the best fit, low clearance (CL) correlated with low body weight and high granulocyte count, whereas low haemoglobin correlated with low volume of distribution. For a patient with 77% of the median body weight or with 1.87 times the median granulocyte count, the apparent CL is 6.53 l/h, about 70% of the typical apparent CL of 9.33 l/h; for a patient of 84% of the typical haemoglobin level, the volume of distribution is about 70%. Total white blood cell count correlated closely with granulocyte count and there was a moderate correlation between haemoglobin and albumin (r = 0.454). There was a trend towards increased imatinib clearance after chronic exposure over 12 months. The typical apparent CL increased 33% from day 1. Nevertheless, the approximate 95% confidence interval of the increase of the typical apparent CL was 33 +/- 34.6%, which contains zero. It is not yet clear whether this is a significant factor in the amelioration of imatinib toxicity that occurs with time or is related to disease control, and further work is required to confirm this observation.

Imatinib plasma levels are correlated with clinical benefit in patients with unresectable/metastatic gastrointestinal stromal tumors.

PURPOSE To study the pharmacokinetics (PK) of imatinib (IM) in patients with advanced GI stromal tumors (GISTs) treated in a randomized phase II study and to explore the potential relationship between IM plasma levels and long-term clinical outcomes. PATIENTS AND METHODS Patients were randomly assigned to receive IM at 400 mg versus 600 mg daily. IM plasma levels were analyzed in a subset of patients (n = 73) for whom PK data on day 1 and at steady-state (SS, day 29) were available. IM PK was evaluated using a population PK approach. The relationship between IM plasma exposure and clinical outcome was explored by grouping patients into quartiles according to IM trough concentration (C(min)). The clinical outcome parameters evaluated include overall objective benefit rate (OOBR; complete response plus partial response plus stable disease) time to progression (TTP), and KIT genotyping. Results IM PK exposure showed a high inter-patient variability, and clinical outcomes were correlated with IM trough levels at SS. The median TTP was 11.3 months for patients in the lowest C(min) quartile (Q1, < 1,110 ng/mL) compared with more than 30 months for Q2 to Q4 (P = .0029). OOBR was also inferior in Q1 patients. In patients with GIST with KIT exon 11 mutations (n = 39), the OOBR was 67% for Q1 patients versus 100% for all others (P = .001). CONCLUSION In patients with advanced GIST, IM trough levels at SS were associated with clinical benefit. Patients with IM C(min) below 1,100 ng/mL showed a shorter TTP and lower rate of clinical benefit (OOBR). Further studies are justified to test whether monitoring IM plasma levels might optimize clinical outcomes for patients with GIST.

Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: general concepts.

Traditional drug development consists of a sequence of independent trials organized in different phases. Full development typically involves (i) a learning phase II trial and (ii) one or two confirmatory phase III trial(s). For example, in the phase II trials several doses of the new compound might be compared to a control and/or placebo with the goal of deciding whether to stop or continue development and, in the latter case, selecting one or two "best" doses to carry forward into the confirmatory phase. The phase III trials are then conducted as stand-alone confirmatory studies, not incorporating in their statistical analyses data collected in the previous phases. Seamless phase II/III designs are aimed at interweaving the two phases of full development by combining them into one single, uninterrupted study conducted in two stages. In the dose-finding example above, one (or more) dose(s) are selected after the first stage based on the available data at interim, and are then observed further in the second stage. The final analysis of the selected dose(s) includes patients from both stages and is performed such that the overall type I error rate is controlled at a prespecified level regardless of the dose selection rule used at interim. The adequacy of the dose selection at interim is obviously a critical step for the success of a seamless phase II/III trial. In this paper we focus on the description of flexible test procedures allowing for adaptively selecting hypotheses at interim and thus allowing the combination of learning and confirming in a single seamless trial. We review the statistical background, introduce different test procedures and compare them in a power study. In a subsequent paper (Schmidli et al., 2006) we give several applications from our daily practice and discuss related implementation issues in conducting adaptive seamless designs.

Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: applications and practical considerations.

Adaptive seamless phase II/III designs combine a phase II and a phase III study into one single confirmatory clinical trial. Several examples of such designs are presented, where the primary endpoint is binary, time-to-event or continuous. The interim adaptations considered include the selection of treatments and the selection of hypotheses related to a pre-specified subgroup of patients. Practical aspects concerning the planning and implementation of adaptive seamless confirmatory studies are also discussed.

Modelling and simulation in the development and use of anti-cancer agents: an underused tool?

To help identify the role of modelling and simulation in the development of anti-cancer agents, their main advantages and the obstacles to their rational use, an expert meeting was organized by COST B15. This manuscript presents a synthesis of views expressed at that meeting and indicates future directions. The manuscript also shows some examples where modelling and simulation have proven to be of relevant value in the drug development process for anti-cancer agents.