Statistical Data Analysis
KJC Statistics offers broad experience in clinical trial data analysis, from linear mixed effects modelling, to survival analyses, to meta-analyses, to non-parametric methods and beyond. Further, with increasing transparency and data availability, exploratory analysis methodologies are also offered.
Meta Analyses and Rare Event Meta-Analyses
Aggregation of data across trials is often necessary to better appreciate the true effect of treatment. KJC Statistics has broad experience of meta-analyses and meta-analytic methodologies, including fixed and random effect approaches, risk difference and risk ratio approaches, exact methods and tricky rare event meta-analyses where there are often trials with no events.
Sample Size and Power Calculations
Ensuring clinical studies are correctly sized is essential to secure a conclusive result. Equally, studies that are oversized can cost needlessly in both dollars and time. KJC Statistics will work with you to strike the best balance between size, power and cost for you and your project.
Bayesian Data Analysis
Bayesian data analysis can be useful in many areas including, but not limited to, decision making, non-inferiority trial design and analysis, exploratory subset analyses, multi-regional trial analysis and rare event meta-analyses. KJC Statistics can help guide you in terms of the opportunities for helpful Bayesian analyses in your project.
PK/PD Modelling
Understanding the relationship between exposure and effect is often crucial in determining the most appropriate dose to take forward into later stages of development. KJC Statistics has experience with non-linear mixed effects PK-PD modelling to help you to better understand your data and dose-response.