This article introduces a manually curated data collection for gene expression meta-analysis of patients with ovarian cancer and software for reproducible preparation of similar databases. This resource provides uniformly prepared microarray data for 2970 patients from 23 studies with curated and documented clinical metadata. It allows users to efficiently identify studies and patient subgroups of interest for analysis and to perform meta-analysis immediately without the challenges posed by harmonizing heterogeneous microarray technologies, study designs, expression data processing methods and clinical data formats. We confirm that the recently proposed biomarker CXCL12 is associated with patient survival, independently of stage and optimal surgical debulking, which was possible only through meta-analysis owing to insufficient sample sizes of the individual studies. The database is implemented as the curatedOvarianData Bioconductor package for the R statistical computing language, providing a comprehensive and flexible resource for clinically oriented investigation of the ovarian cancer transcriptome. The package and pipeline for producing it are available from http://bcb.dfci.harvard.edu/ovariancancer.Database URL: http://bcb.dfci.harvard.edu/ovariancancer

ObjectiveTo evaluate the accuracy of a 2015 cross-sectional analysis published in the BMJ Open which reported that pharmaceutical industry compliance with clinical trial registration and results reporting requirements under US law was suboptimal and varied widely among companies.DesignWe performed a reassessment of the data reported in Miller et al to evaluate whether statutory compliance analyses and conclusions were valid.Data sourcesInformation from the Dryad Digital Repository, ClinicalTrials.gov, Drugs@FDA and direct communications with sponsors.Main outcome measuresCompliance with the clinical trial registration and results reporting requirements under the Food and Drug Administration Amendments Act (FDAAA).ResultsIndustry compliance with FDAAA disclosure requirements was notably higher than reported by Miller et al. Among trials subject to FDAAA, Miller et al reported that, per drug, a median of 67% (middle 50% range: 0%–100%) of trials fully complied with registration and results reporting requirements. On reanalysis of the data, we found that a median of 100% (middle 50% range: 93%–100%) of clinical trials for a particular drug fully complied with the law. When looking at overall compliance at the trial level, our reassessment yields 94% timely registration and 90% timely results reporting among the 49 eligible trials, and an overall FDAAA compliance rate of 86%.ConclusionsThe claim by Miller et al that industry compliance is below legal standards is based on an analysis that relies on an incomplete dataset and an interpretation of FDAAA that requires disclosure of study results for drugs that have not yet been approved for any indication. On reanalysis using a different interpretation of FDAAA that focuses on whether results were disclosed within 30 days of drug approval, we found that industry compliance with US statutory disclosure requirements for the 15 reviewed drugs was consistently high.

Background: Composite endpoints (CEP), such as progression-free survival, are commonly used in cancer research. Notwithstanding their popularity, however, CEP analyses suffer from a number of drawbacks, especially when death is combined with a nonterminal event (ie, progression or recurrence), exemplifying the semicompeting risks setting. We investigated the semicompeting risks framework as a complementary analysis strategy that avoids certain drawbacks of CEPs. Methods: The illness-death model under the semicompeting risks framework was compared with standard analysis approaches: CEP analyses and (separate) univariate analyses for each component endpoint. Data from a previously published phase III randomized clinical trial in metastatic colon cancer including 1419 participants in the N9741 trial (conducted between 1997 and 2003) were used to determine the impact of the loss of information associated with combining multiple endpoints, as well as of ignoring the potentially informative role of death. A simulation study was conducted to further explore these issues. Results: Failure to account for critical features of semicompeting risks data can lead to potentially severely misleading conclusions. Advantages of semicompeting risks analyses include a clear delineation of treatment effects on both events, the ability to draw conclusions about a patient's joint risk of the two events, and an assessment of the dependence between the two event types. Conclusions: Embedding and analyzing component outcomes in the semicompeting risks framework, either as a supplement or alternative to CEP analyses, represents an important, underutilized, and feasible opportunity for cancer research.