Marc Vandemeulebroecke

Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn's disease: unexpected results of a randomised, double-blind placebo-controlled trial

Objective The authors tested whether the anti-interleukin (IL)-17A monoclonal antibody secukinumab was safe and effective for the treatment of active Crohns disease. Design In a double-blind, randomised, placebo-controlled proof-of-concept study, 59 patients with moderate to severe Crohns disease (Crohns Disease Activity Index (CDAI) ≥220 to ≤450) were assigned in a 2:1 ratio to 2×10 mg/kg intravenous secukinumab or placebo. The primary end point, addressed by Bayesian statistics augmented with historical placebo information, was the probability that secukinumab reduces the CDAI by ≥50 points more than placebo at week 6. Ancillary analyses explored associations of 35 candidate genetic polymorphisms and faecal calprotectin response. Results 59 patients (39 secukinumab, 20 placebo, mean baseline CDAI 307 and 301, respectively) were recruited. 18/59 (31%) patients discontinued prematurely (12/39 (31%) secukinumab, 6/20 (30%) placebo), 10/59 (17%) due to insufficient therapeutic effect (8/39 (21%) secukinumab, 2/20 (10%) placebo). Fourteen serious adverse events occurred in 10 patients (seven secukinumab, three placebo); 20 infections, including four local fungal infections, were seen on secukinumab versus none on placebo. Primary end point analysis estimated <0.1% probability (∆CDAI (SD) =33.9 (19.7), 95% credible interval −4.9 to 72.9) that secukinumab reduces CDAI by ≥50 points more than placebo. Secondary area under the curve analysis (weeks 4–10) showed a significant difference (mean ΔCDAI=49; 95% CI (2 to 96), p=0.043) in favour of placebo. Post hoc subgroup analysis showed that unfavourable responses on secukinumab were driven by patients with elevated inflammatory markers (CRP≥10 mg/l and/or faecal calprotectin≥200 ng/ml; mean ΔCDAI=62; 95% CI (−1 to 125), p=0.054 in favour of placebo). Absence of the minor allele of tumour necrosis factor-like ligand 1A was strongly associated with lack of response measured by baseline-adjusted changes in calprotectin at week 6 (p=0.00035 Bonferroni-corrected). Conclusions Blockade of IL-17A was ineffective and higher rates of adverse events were noted compared with placebo. Clinical trial registration This trial was registered at ClinicalTrial.gov with the number NCT01009281.

AFQ056 treatment of levodopa‐induced dyskinesias: Results of 2 randomized controlled trials

Study objectives were to assess the efficacy, safety, and tolerability of AFQ056 in Parkinsons disease patients with levodopa‐induced dyskinesia. Two randomized, double‐blind, placebo‐controlled, parallel‐group, in‐patient studies for Parkinsons disease patients with moderate to severe levodopa‐induced dyskinesia (study 1) and severe levodopa‐induced dyskinesia (study 2) on stable dopaminergic therapy were performed. Patients received 25–150 mg AFQ056 or placebo twice daily for 16 days (both studies). Study 2 included a 4‐day down‐titration. Primary outcomes were the Lang‐Fahn Activities of Daily Living Dyskinesia Scale (study 1), the modified Abnormal Involuntary Movement Scale (study 2), and the Unified Parkinsons Disease Rating Scale–part III (both studies). Secondary outcomes included the Unified Parkinsons Disease Rating Scale–part IV items 32–33. The primary analysis was change from baseline to day 16 on all outcomes. Treatment differences were assessed. Fifteen patients were randomized to AFQ056 and 16 to placebo in study 1; 14 patients were randomized to each group in study 2. AFQ056‐treated patients showed significant improvements in dyskinesias on day 16 versus placebo (eg, Lang‐Fahn Activities of Daily Living Dyskinesia Scale, P = .021 [study 1]; modified Abnormal Involuntary Movement Scale, P = .032 [study 2]). No significant changes were seen from baseline on day 16 on the Unified Parkinsons Disease Rating Scale‐part III in either study. Adverse events were reported in both studies, including dizziness. Serious adverse events (most commonly worsening of dyskinesias, apparently associated with stopping treatment) were reported by 4 AFQ056‐treated patients in study 1, and 3 patients (2 AFQ056‐treated patient and 1 in the placebo group) in study 2. AFQ056 showed a clinically relevant and significant antidyskinetic effect without changing the antiparkinsonian effects of dopaminergic therapy.

A practical guide to Bayesian group sequential designs

Bayesian approaches to the monitoring of group sequential designs have two main advantages compared with classical group sequential designs: first, they facilitate implementation of interim success and futility criteria that are tailored to the subsequent decision making, and second, they allow inclusion of prior information on the treatment difference and on the control group. A general class of Bayesian group sequential designs is presented, where multiple criteria based on the posterior distribution can be defined to reflect clinically meaningful decision criteria on whether to stop or continue the trial at the interim analyses. To evaluate the frequentist operating characteristics of these designs, both simulation methods and numerical integration methods are proposed, as implemented in the corresponding R package gsbDesign. Normal approximations are used to allow fast calculation of these characteristics for various endpoints. The practical implementation of the approach is illustrated with several clinical trial examples from different phases of drug development, with various endpoints, and informative priors.

When is a seamless study desirable? Case studies from different pharmaceutical sponsors

BACKGROUND Inferentially seamless studies are one of the best-known adaptive trial designs. Statistical inference for these studies is a well-studied problem. Regulatory guidance suggests that statistical issues associated with study conduct are not as well understood. Some of these issues are caused by the need for early pre-specification of the phase III design and the absence of sponsor access to unblinded data. Before statisticians decide to choose a seamless IIb/III design for their programme, they should consider whether these pitfalls will be an issue for their programme. METHODS We consider four case studies. Each design met with varying degrees of success. We explore the reasons for this variation to identify characteristics of drug development programmes that lend themselves well to inferentially seamless trials and other characteristics that warn of difficulties. RESULTS Seamless studies require increased upfront investment and planning to enable the phase III design to be specified at the outset of phase II. Pivotal, inferentially seamless studies are unlikely to allow meaningful sponsor access to unblinded data before study completion. This limits a sponsors ability to reflect new information in the phase III portion. CONCLUSIONS When few clinical data have been gathered about a drug, phase II data will answer many unresolved questions. Committing to phase III plans and study designs before phase II begins introduces extra risk to drug development. However, seamless pivotal studies may be an attractive option when the clinical setting and development programme allow, for example, when revisiting dose selection.

Comments on the Draft Guidance on “Adaptive Design Clinical Trials for Drugs and Biologics” of the U.S. Food and Drug Administration

The U.S. FDA has published a draft guidance on “Adaptive Design Clinical Trials for Drugs and Biologics”, which gives regulatory guidance on methodological issues in exploratory and confirmatory clinical trials planned with an adaptive design. This comment summarizes the discussion within the joint working group “Adaptive Designs and Multiple Testing Procedures” of the Austro-Swiss and German regions of the International Biometric Society held at the 90-day public comment period in spring 2010.

A generalized analytic solution to the win ratio to analyze a composite endpoint considering the clinical importance order among components.

A composite endpoint consists of multiple endpoints combined in one outcome. It is frequently used as the primary endpoint in randomized clinical trials. There are two main disadvantages associated with the use of composite endpoints: a) in conventional analyses, all components are treated equally important; and b) in time-to-event analyses, the first event considered may not be the most important component. Recently Pocock et al. (2012) introduced the win ratio method to address these disadvantages. This method has two alternative approaches: the matched pair approach and the unmatched pair approach. In the unmatched pair approach, the confidence interval is constructed based on bootstrap resampling, and the hypothesis testing is based on the non-parametric method by Finkelstein and Schoenfeld (1999). Luo et al. (2015) developed a close-form variance estimator of the win ratio for the unmatched pair approach, based on a composite endpoint with two components and a specific algorithm determining winners, losers and ties. We extend the unmatched pair approach to provide a generalized analytical solution to both hypothesis testing and confidence interval construction for the win ratio, based on its logarithmic asymptotic distribution. This asymptotic distribution is derived via U-statistics following Wei and Johnson (1985). We perform simulations assessing the confidence intervals constructed based on our approach versus those per the bootstrap resampling and per Luo et al. We have also applied our approach to a liver transplant Phase III study. This application and the simulation studies show that the win ratio can be a better statistical measure than the odds ratio when the importance order among components matters; and the method per our approach and that by Luo et al., although derived based on large sample theory, are not limited to a large sample, but are also good for relatively small sample sizes. Different from Pocock et al. and Luo et al., our approach is a generalized analytical method, which is valid for any algorithm determining winners, losers and ties. Copyright

A Longitudinal Item Response Theory Model to Characterize Cognition over Time in Elderly Subjects.

For drug development in neurodegenerative diseases such as Alzheimers disease, it is important to understand which cognitive domains carry the most information on the earliest signs of cognitive decline, and which subject characteristics are associated with a faster decline. A longitudinal Item Response Theory (IRT) model was developed for the Basel Study on the Elderly, in which the Consortium to Establish a Registry for Alzheimers Disease – Neuropsychological Assessment Battery (with additions) and the California Verbal Learning Test were measured on 1,750 elderly subjects for up to 13.9 years. The model jointly captured the multifaceted nature of cognition and its longitudinal trajectory. The word list learning and delayed recall tasks carried the most information. Greater age at baseline, fewer years of education, and positive APOEɛ4 carrier status were associated with a faster cognitive decline. Longitudinal IRT modeling is a powerful approach for progressive diseases with multifaceted endpoints.

A modified varying-stage adaptive phase II/III clinical trial design

Conventionally, adaptive phase II/III clinical trials are carried out with a strict two-stage design. Recently, a varying-stage adaptive phase II/III clinical trial design has been developed. In this design, following the first stage, an intermediate stage can be adaptively added to obtain more data, so that a more informative decision can be made. Therefore, the number of further investigational stages is determined based upon data accumulated to the interim analysis. This design considers two plausible study endpoints, with one of them initially designated as the primary endpoint. Based on interim results, another endpoint can be switched as the primary endpoint. However, in many therapeutic areas, the primary study endpoint is well established. Therefore, we modify this design to consider one study endpoint only so that it may be more readily applicable in real clinical trial designs. Our simulations show that, the same as the original design, this modified design controls the Type I error rate, and the design parameters such as the threshold probability for the two-stage setting and the alpha allocation ratio in the two-stage setting versus the three-stage setting have a great impact on the design characteristics. However, this modified design requires a larger sample size for the initial stage, and the probability of futility becomes much higher when the threshold probability for the two-stage setting gets smaller. Copyright

The stratified win ratio

ABSTRACT The win ratio was first proposed in 2012 by Pocock and his colleagues to analyze a composite endpoint while considering the clinical importance order and the relative timing of its components. It has attracted considerable attention since then, in applications as well as methodology. It is not uncommon that some clinical trials require a stratified analysis. In this article, we propose a stratified win ratio statistic in a similar way as the Mantel-Haenszel stratified odds ratio, derive a general form of its variance estimator with a plug-in of existing or potentially new variance/covariance estimators of the number of wins for the two treatment groups, and assess its statistical performance using simulation studies. Our simulations show that our proposed Mantel-Haenszel-type stratified win ratio performs similarly to the Mantel-Haenszel stratified odds ratio for the simplified situation when the win ratio reduces to the odds ratio, and our proposed stratified win ratio is preferred compared to the inverse-variance weighted win ratio and unweighted win ratio particularly when the data are sparse. We also formulate a homogeneity test following Cochran’s approach that assesses whether the stratum-specific win ratios are homogeneous across strata, as this method is used frequently in meta-analyses and a better test for the win ratio homogeneity is not available yet.

Multistate modeling and simulation of patient trajectories after allogeneic hematopoietic stem cell transplantation to inform drug development

We present a case study for developing clinical trial scenarios in a complex progressive disease with multiple events of interest. The idea is to first capture the course of the disease in a multistate Markov model, and then to simulate clinical trials from this model, including a variety of hypothesized drug effects. This case study focuses on the prevention of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HSCT). The patient trajectory after HSCT is characterized by a complex interplay of various events of interest, and there is no established best method of measuring and/or analyzing treatment benefits. We characterized patient trajectories by means of multistate models that we fitted to a subset of the Center for International Blood and Marrow Transplant Research (CIBMTR) database. Events of interest included acute GvHD of grade III or IV, severe chronic GvHD, relapse of the underlying disease, and death. The transition probability matrix was estimated using the Aalen-Johansen estimator, and patient characteristics were identified that were associated with different transition rates. In a second step, clinical trial scenarios were simulated from the model assuming various drug effects on the background transition rates, and the operating characteristics of different endpoints and analysis strategies were compared in these scenarios. This helped devise a drug development strategy in GvHD prevention after allogeneic HSCT. More generally, multistate models provide a rich framework for exploring complex progressive diseases, and the availability of a corresponding simulation machinery provides great flexibility for clinical trial planning.