Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background.

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.

Secukinumab in moderate-to-severe hidradenitis suppurativa (SUNSHINE and SUNRISE): week 16 and week 52 results of two identical, multicentre, randomised, placebo-controlled, double-blind phase 3 trials.

BACKGROUND: Few therapeutic options are available for patients with moderate-to-severe hidradenitis suppurativa. We aimed to assess the efficacy of secukinumab in patients with moderate-to-severe hidradenitis suppurativa in two randomised trials. METHODS: SUNSHINE and SUNRISE were identical, multicentre, randomised, placebo-controlled, double-blind phase 3 trials done in 219 primary sites in 40 countries. Patients aged 18 years old or older with the capacity to provide written informed consent and with moderate-to-severe hidradenitis suppurativa (defined as a total of ≥5 inflammatory lesions affecting ≥2 distinct anatomical areas) for at least 1 year were eligible for inclusion. Included patients also agreed to daily use of topical over-the-counter antiseptics on the areas affected by hidradenitis suppurativa lesions while on study treatment. Patients were excluded if they had 20 or more fistulae at baseline, had ongoing active conditions requiring treatment with prohibited medication (eg, systemic biological immunomodulating treatment, live vaccines, or other investigational treatments), or met other exclusion criteria. In both trials, patients were randomly assigned (1:1:1) by means of interactive response technology to receive subcutaneous secukinumab 300 mg every 2 weeks, subcutaneous secukinumab 300 mg every 4 weeks, or subcutaneous placebo all via a 2 mL prefilled syringe in a double-dummy method as per treatment assignment. The primary endpoint was the proportion of patients with a hidradenitis suppurativa clinical response, defined as a decrease in abscess and inflammatory nodule count by 50% or more with no increase in the number of abscesses or in the number of draining fistulae compared with baseline, at week 16, assessed in the overall population. Hidradenitis suppurativa clinical response was calculated based on the number of abscesses, inflammatory nodules, draining fistulae, total fistulae, and other lesions in the hidradenitis suppurativa affected areas. Safety was assessed by evaluating the presence of adverse events and serious adverse events according to common terminology criteria for adverse events, which were coded using Medical Dictionary for Regulatory Activities terminology. Both the SUNSHINE, NCT03713619, and SUNRISE, NCT03713632, trials are registered with ClinicalTrials.gov. FINDINGS: Between Jan 31, 2019, and June 7, 2021, 676 patients were screened for inclusion in the SUNSHINE trial, of whom 541 (80%; 304 [56%] women and 237 [44%] men; mean age 36·1 years [SD 11·7]) were included in the analysis (181 [33%] in the secukinumab every 2 weeks group, 180 [33%] in the secukinumab every 4 weeks group, and 180 [33%] in the placebo group). Between the same recruitment dates, 687 patients were screened for inclusion in the SUNRISE trial, of whom 543 (79%; 306 [56%] women and 237 [44%] men; mean age 36·3 [11·4] years) were included in the analysis (180 [33%] in the secukinumab every 2 weeks group, 180 [33%] in the secukinumab every 4 weeks group, and 183 [34%] in the placebo group). In the SUNSHINE trial, significantly more patients in the secukinumab every 2 weeks group had a hidradenitis suppurativa clinical response (rounded average number of patients with response in 100 imputations, 81·5 [45%] of 181 patients) compared with the placebo group (60·7 [34%] of 180 patients; odds ratio 1·8 [95% CI 1·1-2·7]; p=0·0070). However, there was no significant difference between the number of patients in the secukinumab every 4 weeks group (75·2 [42%] of 180 patients) and the placebo group (1·5 [1·0-2·3]; p=0·042). Compared with the placebo group (57·1 [31%] of 183 patients), significantly more patients in the secukinumab every 2 weeks group (76·2 [42%] of 180 patients; 1·6 [1·1-2·6]; p=0·015) and the secukinumab every 4 weeks group (83·1 [46%] of 180 patients; 1·9 [1·2-3·0]; p=0·0022) had a hidradenitis suppurativa clinical response in the SUNRISE trial. Patient responses were sustained up to the end of the trials at week 52. The most common adverse event by preferred term up to week 16 was headache in both the SUNSHINE (17 [9%] patients in the secukinumab every 2 weeks group, 20 [11%] in the secukinumab every 4 weeks group, and 14 [8%] in the placebo group) and SUNRISE (21 [12%] patients in the secukinumab every 2 weeks group, 17 [9%] in the secukinumab every 4 weeks group, and 15 [8%] in the placebo group) trials. No study-related deaths were reported up to week 16. The safety profile of secukinumab in both trials was consistent with that previously reported, with no new or unexpected safety findings detected. INTERPRETATION: When given every 2 weeks, secukinumab was clinically effective at rapidly improving signs and symptoms of hidradenitis suppurativa with a favourable safety profile and with sustained response up to 52 weeks of treatment. FUNDING: Novartis Pharma.

A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis.

OBJECTIVE: Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials. METHODS: Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way. RESULTS: We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting. CONCLUSION: Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.

New strategies for confirmatory testing of secondary hypotheses on combined data from multiple trials.

BACKGROUND: Pivotal evidence of efficacy of a new drug is typically generated by (at least) two clinical trials which independently provide statistically significant and mutually corroborating evidence of efficacy based on a primary endpoint. In this situation, showing drug effects on clinically important secondary objectives can be demanding in terms of sample size requirements. Statistically efficient methods to power for such endpoints while controlling the Type I error are needed. METHODS: We review existing strategies for establishing claims on important but sample size-intense secondary endpoints. We present new strategies based on combined data from two independent, identically designed and concurrent trials, controlling the Type I error at the submission level. We explain the methodology and provide three case studies. RESULTS: Different strategies have been used for establishing secondary claims. One new strategy, involving a protocol planned analysis of combined data across trials, and controlling the Type I error at the submission level, is particularly efficient. It has already been successfully used in support of label claims. Regulatory views on this strategy differ. CONCLUSIONS: Inference on combined data across trials is a useful approach for generating pivotal evidence of efficacy for important but sample size-intense secondary endpoints. It requires careful preparation and regulatory discussion.

New insights on hidradenitis suppurativa phenotypes and treatment response: An exploratory automated analysis of the SUNSHINE and SUNRISE trials.

BACKGROUND: Defining hidradenitis suppurativa (HS) subtypes was previously limited by small sample sizes and poor interrater reliability; no study has investigated subtype treatment responses. The objective of this analysis was to characterize HS clusters in adult patients with moderate to severe HS and evaluate secukinumab treatment responses between clusters. METHODS: Clusters were identified via an unsupervised machine learning clustering analysis using baseline data from the randomized, placebo-controlled SUNSHINE (NCT03713619) and SUNRISE (NCT03713632) phase 3 trials. To assess treatment responses, patients received secukinumab every 2 (SECQ2W) or 4 weeks (SECQ4W) or placebo, for 16 weeks, after which, placebo patients randomly switched to SECQ2W/SECQ4W, and SECQ2W/SECQ4W patients maintained their original treatment, until week 52. Baseline outcomes included patient characteristics, disease characteristics and severity, HS-associated comorbidities and previous treatment exposures. Treatment response was assessed via the HS clinical response (HiSCR), abscess and inflammatory nodule (AN) count, flares and NRS30 (skin pain). RESULTS: Based on baseline data, three clusters were identified from 1084 patients (Cluster 1: 54.1%, Cluster 2: 17.8%, Cluster 3: 28.1%). Cluster 1 was predominantly female (65.4%) and was characterized by milder HS. Cluster 2 had more patients from the Asia Pacific, Middle East and Africa region (58.5%) and was characterized by moderate HS. Cluster 3 had the highest rates of previous exposure to biologics (45.9%) and prior HS-related surgeries (47.5%) and was characterized by severe HS. SECQ2W and SECQ4W demonstrated efficacy versus placebo in all clusters at week 16; SECQ2W and SECQ4W efficacy was maintained to week 52. SECQ2W treatment showed a trend for greater efficacy versus SECQ4W in Cluster 3 through week 52. CONCLUSIONS: Three HS clusters were identified. Secukinumab demonstrated benefit over placebo in all clusters. However, patients with more severe disease may take longer to respond and more frequent secukinumab dosing may be required for these patients. TRIAL REGISTRATION: SUNSHINE (NCT03713619) and SUNRISE (NCT03713632).

Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials.

The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review some existing simulation work and perform a simulation study to evaluate recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in simulation scenarios that reflect real clinical trials. We provide the R package benchtm that can be used to simulate synthetic biomarker distributions based on real clinical trial data and to create interpretable scenarios to benchmark methods for identification and estimation of treatment effect heterogeneity.

Instability and results after non-operative treatment of large anterior glenoid rim fractures: is there a correlation between fragment size or displacement and recurrence?

INTRODUCTION: There is little data available on non-operative treatment of anterior glenoid rim fractures (GRF). Nothing is known about fracture size and displacement in comparison to clinical outcomes and instability in a mainly middle-aged patient population. The aim of this study was to demonstrate the results of non-operative treatment in anterior glenoid rim fractures with the special focus on potential instability/recurrence. METHODS: The inclusion criteria were non-operatively treated anterior GRF of at least ≥ 5 mm width using the age- and gender-matched Constant/Murley score (a.-/g.-CMS) and the Western Ontario Instability Index (WOSI). Radiographic parameters (fracture morphology, displacement, major tuberosity fractures and Hill-Sachs lesion using initial CT and radiographs) and the proportion of the fractured glenoid were detected (2D-CT-circle-method) and osteoarthritis (A.P. and axial radiographs) was classified according to Samilson/Prieto. Proportion of fractured glenoid and medial displacement were correlated with the recurrence rate and the clinical scores. RESULTS: N = 36 patients could be followed-up after a mean of 4.4 years [12-140 month, average age: 58 (± 13, 33-86) years]. The a.-/g.-CMS was 93 (± 11, 61-100) points, and the WOSI was 81% (± 22%, 35-100%) on average. The mean intraarticular displacement was 4 mm (± 3 mm; 0-14 mm). The 2D-circle-method showed a mean glenoid fracture involvement of 21% (± 11, 10-52%). Two cases of frozen shoulders and one case with biceps pathology were associated with the trauma. Within the followed-up patient group re-instability has occurred in n = 2 patients (6%) within the first two weeks after trauma. Osteoarthritis was found in n = 11 cases. There was no correlation between the scores and the fracture size/displacement [(a.-/g.-CMS vs. displacement: r = - 0.08; p = 0.6; vs. size: r = - 0.29; p = 0.2); (WOSI vs. displacement: r = - 0.14; p = 0.4; vs. size: r = - 0.37; p = 0.06)], but very large (≥ 21%) fractures with displacement ≥ 4 mm showed slightly worse results without significant difference (a.-/g.-CMS p = 0.2; WOSI p = 0.2). The apprehension test was negative in all patients at final follow-up. CONCLUSION: Non-operative treatment of anterior GRF was associated with overall good results within a mainly middle-aged larger patient group. Re-instability is rare and is not associated with fragment size but can occur in the first weeks after trauma. Size and dislocation of the fracture is not a criterion for the prognosis of potential instability. LEVEL OF EVIDENCE: Level IV, retrospective case series.

Win odds: An adaptation of the win ratio to include ties.

The win ratio, a recently proposed measure for comparing the benefit of two treatment groups, allows ties in the data but ignores ties in the inference. In this article, we highlight some difficulties that this can lead to, and we propose to focus on the win odds instead, a modification of the win ratio which takes ties into account. We construct hypothesis tests and confidence intervals for the win odds, and we investigate their properties through simulations and in a case study. We conclude that the win odds should be preferred over the win ratio.

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology-A Machine Learning Approach to Histopathology.

Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.

How can we make better graphs? An initiative to increase the graphical expertise and productivity of quantitative scientists.

Graphics are at the core of exploring and understanding data, communicating results and conclusions, and supporting decision-making. Increasing our graphical expertise can significantly strengthen our impact as professional statisticians and quantitative scientists. In this article, we present a concerted effort to improve the way we create graphics at Novartis. We provide our vision and guiding principles, before describing seven work packages in more detail. The actions, principles, and experiences laid out in this paper are applicable generally, also beyond drug development, which is our field of work. The purpose of this article is to share our experiences and help foster the use of good graphs in pharmaceutical statistics and beyond. A Graphics Principles "Cheat Sheet" is available online at https://graphicsprinciples.github.io/.

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.

The stratified win ratio.

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.

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 © 2016 John Wiley & Sons, Ltd.

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 © 2016 John Wiley & Sons, Ltd.

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 sponsor's 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.

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.

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 Crohn's disease. DESIGN: In a double-blind, randomised, placebo-controlled proof-of-concept study, 59 patients with moderate to severe Crohn's disease (Crohn's 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.

DMC reports in the 21st century: towards better tools for decision-making.

Data Monitoring Committees (DMCs) have the important task to protect the safety of current and future patients during the conduct of a clinical study. Unfortunately, their work is often made difficult by voluminous DMC reports that are poorly structured and difficult to digest. In this article, we suggest improved solutions. Starting from a principled approach and building upon previous proposals, we offer concrete and easily understood displays, including related computer code. While leveraging modern tools, the most important is that these displays support the DMC's workflow in answering the relevant questions of interest. We hope that the adoption of these proposals can ease the task of DMCs, and importantly, lead to better decision-making for the benefit of patients.

A Bayesian approach to clinical trial designs in dermatology with multiple simultaneous treatments per subject and multiple raters.

We consider the statistical analysis of clinical trial designs with multiple simultaneous treatments per subject and multiple raters. The work is motivated by a clinical research project in dermatology where different hair removal techniques were assessed based on a within-subject comparison. We assume that clinical outcomes are assessed by multiple raters as continuous or categorical scores, e.g. based on images, comparing two treatments on the subject-level in a pairwise manner. In this setting, a network of evidence on relative treatment effects is generated, which bears strong similarities to the data underlying a network meta-analysis of clinical trials. We therefore build on established methodology for complex evidence synthesis and propose a Bayesian approach to estimate relative treatment effects and to rank the treatments. The approach is, in principle, applicable to situations with any number of treatment arms and/or raters. As a major advantage, all available data is brought into a network and analyzed in one single model, which ensures consistent results among the treatment comparisons. We obtain operating characteristics via simulation and illustrate the method with a real clinical trial example.