Clinical Data Analysis
Clinical data analysis is relevant for a range of applications such as:
- Evaluation of drug efficacy/toxicity
- Determining whether a novel treatment is more beneficial than the standard of care
- Identifying a sub-group of patients that would benefit from a specific treatment, so-called personalised or precision medicine
- Determining whether an adverse effect of a drug treatment could be detected or predicted earlier
Clinical Data Analysis and Biomarkers
Clinical data is generated from a trial or study that has taken place within a clinical setting. Outcomes of a clinical study indicate the consequence of the treatment or intervention on the study population. For example, in oncology, the outcome may be response to treatment defined using the RECIST criteria (complete response, partial response, progressive disease or stable disease). Equally, time to an event can be recorded as an outcome, where the event may be death, disease progression or other metrics.
A central aim of clinical data analysis is to identify biomarkers or phenotypes that are associated with the outcome describing the treatment effect. For example, is the expression level of a certain gene associated with overall survival or does the presence of a certain mutation predictive of improved drug response? The statistical model applied to the data during the analysis stage is context-dependent. In a survival analysis, where the aim is to associate a biomarker with a survival event, a Cox proportional-hazards model could be implemented. If biomarker data has been measured at different time points along the course of a clinical trial, a statistical test suitable for repeated measures data could be used, for example an ANOVA, ANCOVA or a linear mixed-effects model.
Novus can handle a range of formats for clinical data, such as Excel, SAS, R data objects or standardised formats such as ADaM and can help to identify the most suitable statistical model required.
What We Offer
Novus Genomics offers a comprehensive analysis approach for augmenting clinical trial outcomes, ensuring you get the most information out of your research, helping guide future decision making and maximising the return on your investment.
Our team can analyse data from primary outcomes, and other data that are generally collected during the course of a clinical trial. These data may include the following:
- Expression data
- Clinical phenotypes
- Demographic data
- Genotype data
- Clinical chemistry
- Haematological tests
- Flow cytometry
- Immunohistochemistry (IHC)
- Cytokine profiling
- Human leukocyte antigen (HLA) typing
- Pharmacokinetic/pharmacodynamic measurements
Every time our clients work with us, they benefit from:
- A dedicated analyst backed by an experienced team to curate all data, identify the most appropriate statistical approach to take and provide a biological interpretation of results.
- An interactive data analysis report, internally peer-reviewed, including all analysis methods and results.
- Post-report follow ups: upon receipt of our data analysis report, we arrange a teleconference so that our lead analyst can talk through the results.
- Access to large capacity computing and secure data storage facilities.
We have utilized the Bioinformatics team at Novus Genomics for many of our drug discovery projects, as they provide expertise in the analysis of complex bioinformatic datasets. This includes large scale datasets from public sources as well as internally generated datasets. In many instances, at the start of a project, we have planned our large scale transcriptomic/proteomic studies with the Novus team, to ensure that the data generated would provide the information we need, and that our projects had the highest chance of success. We have been consistently impressed with the rigor of Novus’ work, their communication throughout the projects, and the rapid speed at which they complete their analyses.
