A Randomized Phase II Trial (TAMIGA) Evaluating the Efficacy and Safety of Continuous Bevacizumab Through Multiple Lines of Treatment for Recurrent Glioblastoma.

BACKGROUND: We assessed the efficacy and safety of bevacizumab (BEV) through multiple lines in patients with recurrent glioblastoma who had progressed after first-line treatment with radiotherapy, temozolomide, and BEV. PATIENTS AND METHODS: TAMIGA (NCT01860638) was a phase II, randomized, double-blind, placebo-controlled, multicenter trial in adult patients with glioblastoma. Following surgery, patients with newly diagnosed glioblastoma received first-line treatment consisting of radiotherapy plus temozolomide and BEV, followed by six cycles of temozolomide and BEV, then BEV monotherapy until disease progression (PD1). Randomization occurred at PD1 (second line), and patients received lomustine (CCNU) plus BEV (CCNU + BEV) or CCNU plus placebo (CCNU + placebo) until further disease progression (PD2). At PD2 (third line), patients continued BEV or placebo with chemotherapy (investigator's choice). The primary endpoint was survival from randomization. Secondary endpoints were progression-free survival in the second and third lines (PFS2 and PFS3) and safety. RESULTS: Of the 296 patients enrolled, 123 were randomized at PD1 (CCNU + BEV, n = 61; CCNU + placebo, n = 62). The study was terminated prematurely because of the high drop-out rate during first-line treatment, implying underpowered inferential testing. The proportion of patients receiving corticosteroids at randomization was similar (BEV 33%, placebo 31%). For the CCNU + BEV and CCNU + placebo groups, respectively, median survival from randomization was 6.4 versus 5.5 months (stratified hazard ratio [HR], 1.04; 95% confidence interval [CI], 0.69-1.59), median PFS2 was 2.3 versus 1.8 months (stratified HR, 0.70; 95% CI, 0.48-1.00), median PFS3 was 2.0 versus 2.2 months (stratified HR, 0.70; 95% CI, 0.37-1.33), and median time from randomization to a deterioration in health-related quality of life was 1.4 versus 1.3 months (stratified HR, 0.76; 95% CI, 0.52-1.12). The incidence of treatment-related grade 3 to 4 adverse events was 19% (CCNU + BEV) versus 15% (CCNU + placebo). CONCLUSION: There was no survival benefit and no detriment observed with continuing BEV through multiple lines in patients with recurrent glioblastoma. IMPLICATIONS FOR PRACTICE: Previous research suggested that there may be value in continuing bevacizumab (BEV) beyond progression through multiple lines of therapy. No survival benefit was observed with the use of BEV through multiple lines in patients with glioblastoma who had progressed after first-line treatment (radiotherapy + temozolomide + BEV). No new safety concerns arose from the use of BEV through multiple lines of therapy.

Best practice for analysis of shared clinical trial data.

BACKGROUND: Greater 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. DISCUSSION: In 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. Increased 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.

Data sharing and the evolving role of statisticians.

BACKGROUND: Greater 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. DISCUSSION: Successful 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. Data 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.