Amy Racine

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.

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.

Pretreatment with aldosterone or corticosterone blocks the memory-enhancing effects of nimodipine, captopril, CGP 37,849, and strychnine in mice.

Oral pretreatment with aldosterone or corticosterone blocked the memory-enhancing effects of the calcium antagonist nimodipine, the ACE inhibitor captopril, the NMDA blocker CGP 37 849, and the glycine antagonist strychnine in a passive-avoidance test in mice. The memory-disturbing effects of phenobarbitone, diazepam, CGP 37 849 and scopolamine were not influenced by the hormonal pretreatment. These findings could indicate the involvement of a steroid-sensitive mechanism in drug-induced improvement of memory. In the light of clinical observations showing elevated cortisol levels in Alzheimer patients, the results might also explain why only a limited number of these patients respond to therapy with memory enhancers.

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.

Re-treatment Studies: Design and Analysis

For some disorders such as irritable bowel syndrome, symptoms may wax and wane with variable periodicity. In clinical practice, treatment is usually targeted at symptom reduction during exacerbations. The management of such patients will likely involve intermittent courses of therapy tailored to the patients individual symptom pattern instead of chronic, uninterrupted treatment. Three study designs to assess the feasibility and efficacy of repeated drug treatment in patients with irritable bowel syndrome are discussed. The first design is a parallel group design in which patients, after the initial treatment period, may repeatedly receive the same treatment as in the first period (test drug or control). In the second design, patients who respond favorably to initial treatment with a test drug and whose symptoms recur when this treatment is stopped will be randomly assigned to receive the test drug or control in a second period. The third design has in addition a control group in the initial period. The three designs are compared regarding analysis and sample size.

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.

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.