Hans Ulrich Burger

Left Ventricular Geometry Predicts Cardiovascular Outcomes Associated with Anemia Correction in CKD

Partial correction of anemia in patients with chronic kidney disease (CKD) reduces left ventricular hypertrophy (LVH), which is a risk factor for cardiovascular (CV) morbidity, but complete correction of anemia does not improve CV outcomes. Whether LV geometry associates with CV events in patients who are treated to different hemoglobin (Hb) targets is unknown. One of the larger trials to study the effects of complete correction of anemia in stages 3 to 4 CKD was the Cardiovascular Risk Reduction by Early Anemia Treatment with Epoetin Beta (CREATE) trial. Here, we analyzed echocardiographic data from CREATE to determine the prevalence, dynamics, and prognostic implications of abnormal LV geometry in patients who were treated to different Hb targets. The prevalence of LVH at baseline was 47%, with eccentric LVH more frequent than concentric. During the study, LVH prevalence and mean left ventricular mass index did not change significantly, but LV geometry fluctuated considerably within 2 yr in both groups. CV event-free survival was significantly worse in the presence of concentric LVH and eccentric LVH compared with the absence of LVH (P = 0.0009 and P < or = 0.0001, respectively). Treatment to the higher Hb target associated with reduced event-free survival in the subgroup with eccentric LVH at baseline (P = 0.034). In conclusion, LVH is common and associates with poor outcomes among patients with stages 3 to 4 CKD, although both progression and regression of abnormal LV geometry occur. Complete anemia correction may aggravate the adverse prognosis of eccentric LVH.

Comments on the Draft Guidance on “Adaptive Design Clinical Trials for Drugs and Biologics” of the U.S. Food and Drug Administration

The U.S. FDA has published a draft guidance on “Adaptive Design Clinical Trials for Drugs and Biologics”, which gives regulatory guidance on methodological issues in exploratory and confirmatory clinical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.

Statistical guidance for responsible data sharing: an overview

Since at least 2004 there has been a focus on data sharing and clinical trial disclosure with the requirements for protocols to be registered in clinicaltrials.gov and for subsequent manuscripts of the study results to be accepted for publication by major journals [1]. However, sponsors of clinical trials have for many years been widely criticised for not adhering to these requirements and failing to disclose clinical trials in a timely fashion (e.g., www.alltrials.net].

Data sharing and the evolving role of statisticians.

BackgroundGreater transparency and, in particular, sharing of clinical study reports and patient level data for further research is an increasingly important topic for the pharmaceutical and biotechnology industry and other organisations who sponsor and conduct clinical research as well as academic researchers and patient advocacy groups. Statisticians are ambassadors for data sharing and are central to its success. They play an integral role in data sharing discussions within their companies and also externally helping to shape policy and processes while providing input into practical solutions to aid data sharing. Data sharing is generating changes in the required profile for statisticians in the pharmaceutical and biotechnology industry, as well as academic institutions and patient advocacy groups.DiscussionSuccessful statisticians need to possess many qualities required in today’s pharmaceutical environment such as collaboration, diplomacy, written and oral skills and an ability to be responsive; they are also knowledgeable when debating strategy and analytical techniques. However, increasing data transparency will require statisticians to evolve and learn new skills and behaviours during their career which may not have been an accepted part of the traditional role. Statisticians will move from being the gate-keepers of data to be data facilitators. To adapt successfully to this new environment, the role of the statistician is likely to be broader, including defining new responsibilities that lie beyond the boundaries of the traditional role. Statisticians should understand how data transparency can benefit them and the potential strategic advantage it can bring and be fully aware of the pharmaceutical and biotechnology industry commitments to data transparency and the policies within their company or research institute in addition to focusing on reviewing requests and provisioning data.SummaryData transparency will evolve the role of statisticians within the pharmaceutical and biotechnology industry, academia and research bodies to a level which may not have been an accepted part of their traditional role or career. In the future, skills will be required to manage challenges arising from data sharing; statisticians will need strong scientific and statistical guiding principles for reanalysis and supplementary analyses based on researchers’ requests, have enhanced consultancy skills, in particular the ability to defend good statistical practice in the face of criticism and the ability to critique methods of analysis. Statisticians will also require expertise in data privacy regulations, data redaction and anonymisation and be able to assess the probability of re-identification, an ability to understand analyses conducted by researchers and recognise why such analyses may propose different results compared to the original analyses. Bringing these skills to the implementation of data sharing and interpretation of the results will help to maximise the value of shared data while guarding against misleading conclusions.

European Federation of Statisticians in the Pharmaceutical Industry's position on access to clinical trial data

The European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) believes access to clinical trial data should be implemented in a way that supports good research, avoids misuse of such data, lies within the scope of the original informed consent and fully protects patient confidentiality. In principle, EFSPI supports responsible data sharing. EFSPI acknowledges it is in the interest of patients that their data are handled in a strictly confidential manner to avoid misuse under all possible circumstances. It is also in the interest of the altruistic nature of patients participating in trials that such data will be used for further development of science as much as possible applying good statistical principles. This paper summarises EFSPIs position on access to clinical trial data. The position was developed during the European Medicines Agency (EMA) advisory process and before the draft EMA policy on publication and access to clinical trial data was released for consultation; however, the EFSPIs position remains unchanged following the release of the draft policy. Finally, EFSPI supports a need for further guidance to be provided on important technical aspects relating to re-analyses and additional analyses of clinical trial data, for example, multiplicity, meta-analysis, subgroup analyses and publication bias.

Bayesian predictive power: choice of prior and some recommendations for its use as probability of success in drug development.

Bayesian predictive power, the expectation of the power function with respect to a prior distribution for the true underlying effect size, is routinely used in drug development to quantify the probability of success of a clinical trial. Choosing the prior is crucial for the properties and interpretability of Bayesian predictive power. We review recommendations on the choice of prior for Bayesian predictive power and explore its features as a function of the prior. The density of power values induced by a given prior is derived analytically and its shape characterized. We find that for a typical clinical trial scenario, this density has a u-shape very similar, but not equal, to a β-distribution. Alternative priors are discussed, and practical recommendations to assess the sensitivity of Bayesian predictive power to its input parameters are provided. Copyright

Best practice for analysis of shared clinical trial data.

BackgroundGreater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention.DiscussionIn order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas.SummaryIncreased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.

Nonparametric adaptive enrichment designs using categorical surrogate data: Non-parametric adaptive enrichment designs

Adaptive survival trials are particularly important for enrichment designs in oncology and other life-threatening diseases. Current statistical methodology for adaptive survival trials provide type I error rate control only under restrictions. For instance, if we use stage-wise P values based on increments of the log-rank test, then the information used for the interim decisions need to be restricted to the primary survival endpoint. However, it is often desirable to base interim decisions also on correlated short-term endpoints like tumor response. Alternative statistical approaches based on a patient-wise splitting of the data require unnatural restrictions on the follow-up times and do not permit to efficiently account for an early rejection of the primary null hypothesis. We therefore suggest new approaches that enable us to use discrete surrogate endpoints (like tumor response status) and also to incorporate interim rejection boundaries. The new approaches are based on weighted Kaplan-Meier estimates and thereby have additional advantages. They permit us to account for nonproportional hazards and are robust against informative censoring based on the surrogate endpoint. We will show that nonproportionality is an intrinsic and relevant issue in enrichment designs. Moreover, informative censoring based on the surrogate endpoint is likely because of withdrawals and treatment switches after insufficient treatment response. It is shown and illustrated how nonparametric tests based on weighted Kaplan-Meier estimates can be used in closed combination tests for adaptive enrichment designs, such that type I error rate control is achieved and justified asymptotically.

Comparison of design strategies for a three-arm clinical trial with time-to-event endpoint: Power, time-to-analysis, and operational aspects.

To optimize resources, randomized clinical trials with multiple arms can be an attractive option to simultaneously test various treatment regimens in pharmaceutical drug development. The motivation for this work was the successful conduct and positive final outcome of a three-arm randomized clinical trial primarily assessing whether obinutuzumab plus chlorambucil in patients with chronic lympocytic lymphoma and coexisting conditions is superior to chlorambucil alone based on a time-to-event endpoint. The inference strategy of this trial was based on a closed testing procedure. We compare this strategy to three potential alternatives to run a three-arm clinical trial with a time-to-event endpoint. The primary goal is to quantify the differences between these strategies in terms of the time it takes until the first analysis and thus potential approval of a new drug, number of required events, and power. Operational aspects of implementing the various strategies are discussed. In conclusion, using a closed testing procedure results in the shortest time to the first analysis with a minimal loss in power. Therefore, closed testing procedures should be part of the statisticians standard clinical trials toolbox when planning multiarm clinical trials.