We partner with small to medium size research and development companies to fulfill biostatistics and bioinformatics consulting needs in the areas of CRO, MRO, biotechnology, medical device and pharmaceuticals. Predominately this comes in the form of statistical support for pre-clinical and clinical trials. We're always open consider to new and innovative forms of collaboration.
The following is an overview of clinical trial components we can be contracted for:
Pre-clinical trials, whether in-vitro or in-vivo, are an essential regulatory requirement in ensuring the safety and efficacy of a novel drug or device prior to testing on humans in phase I to IV trials.
As with pilot studies in other clinical domains, while not mandated, it is of great advantage to recruit the services of a qualified biostatistical consultant in the design, implementation and analysis of any pre-clinical trial. Doing so will enhance your ability to move your research to the human clinical trial phase and to complete the process with greater efficiency.
Our experienced biostatisticians can be of assistance with the following components of your pre-clinical trial:
- Study design including experimental framework, statistical methodology and data collection methods that best address your research objectives.
- Development of a detailed study protocol in line with good laboratory practice (GLP) guidelines that will help to reduce bias and offer clarity to all laboratory staff.
- Customised randomization schedules to minimise experimental bias. Commonly these will include one of the following based on your individual study needs: randomised block and Latin squares design for in-vitro studies, or randomized, randomized block, factorial, sequential, crossover and Latin square designs for in-vivo studies.
- Sample size calculation. This will be tailored to the specific statistical requirements of your data and will optimise statistical power of your final analysis while working within practical limitations.
- Development and adaption of an efficient statistical analysis plan (SAP).
- All aspects of statistical analysis from data preparation to reporting and manuscript production.
Getting things right first time will save you time and money, minimising resource expending mistakes and the ethical concerns of unnecessarily repeating animal experiments. You will be able to draw robust conclusions from your research which will have benefited from a minimisation in bias.
Phase I to IV trials:
In addition to the above activities, our contribution to phase I to IV trials can consist of:
- Design and implementation of equivalence or non-inferiority trials, Dose effect trials, or Phase II/III combination trials.
- Development of adaptive designs with sub-group analyses or multiple endpoints. Risk reduction through adaptive trial design elements like sample-size re-estimation, dose-selection and general group-sequential designs and analyses and setting up appropriate “go/no-go decision points” and proposals for quantitative evaluations to support such decisions.
- Trial simulation: clinical trial simulations can be used to document and evaluate the statistical properties of complex designs and/or to aid in selecting between several alternative design possibilities.
- Exploratory analyses (e.g. subgroup analyses and characterisation of participant demographics) to generate hypotheses for further research and next steps of development.
- Missing data. The expectation of missing data, such as loss-to-follow-up, is incorporated into all clinical trial design into the trial design and adapt the statistical analysis accordingly.
- Multiplicity. Adjusting for multiplicity necessitated by situations such as: multiple subgroup comparisons, comparisons across multiple treatment arms, the analysis of multiple outcomes, and multiple analyses of the same outcome at different times or between different variables.
- Meta-analyses to integrate evidence from multiple related clinical trials using random effects or other models, for example to consolidate existing findings as to the efficacy of an existing treatment.
- Sensitivity/robustness analyses to assess the influence of key assumptions about the study population, or variations in trial methodology and/or statistical methods. Methodological and statistical concerns that are frequently the subject of robustness analyses include: non-compliance or protocol deviations, missing data, outcomes definitions such as end-points, accounting for clustering or correlation, overlapping risks in studies with composite outcomes, baseline imbalance in sample size or sample characteristics.
- Advisory board participation and discussions with regulatory authorities on development plans and trial design matters such as Special Protocol Assistance, Pre-IND, End of Phase II and scientific advice meetings.
- Consulting in advanced biostatistics (Bayesian statistics, multi-state modelling, latent variable modelling, generalized linear mixed modelling.