Due to the careful scoping of projects and the clinical knowledge of our multidisciplinary Statistics team, we identify the most appropriate methods to perform effective and efficient statistical analysis from the project outset. We therefore believe that we can offer a high-quality statistical analysis service that will fit your requirements, data availability and disease area.
We have worked on a diverse range of projects and are committed to expanding our technical expertise; members of our Statistics team have attended formal training given by leaders in data analysis techniques and obtained higher degrees and certificates in statistics. Our team also undertakes novel research (for example into emerging methods such as hierarchical network meta-analysis) and has expertise in a variety of statistical software.
Our extensive experience of working with clinical studies allows us to critically evaluate the research that feeds into our statistical analysis. The scientific skills possessed by the multidisciplinary Statistics team also ensure that the results and clinical implications of the statistical analyses are interpreted and communicated correctly and persuasively to align with the strategic purpose of the project.
Costello Medical’s Statistics team works closely with several other specialised teams within the company, meaning that any associated projects (such as literature reviews, economic models, HTA submissions, value materials or publications) benefit from the involvement of dedicated specialists as well as the supervision and oversight of the original project manager and statistician throughout. Such an example of this seamless continuity between projects is the statistical derivation of transition probabilities or utility values which subsequently inform our health economic models.
Our experienced Statistics team contributes to advancing the field of evidence-based medicine and regularly publishes articles and opinion pieces on the future of evidence synthesis for decision-making in healthcare.
Recent examples include pieces on incorporating minimally important differences into indirect treatment comparisons, as well as the potential and risks of using artificial intelligence in network meta-analyses for health technology assessments.
