Biostatistics

How Can Biostatistics Help?

Regarded as one of the core disciplines of pharmaceutical research, biostatistics relies on the collection of objective data to demonstrate clinical effect. Biostatistical consulting covers issues from study design (preclinical and clinical) and strategies, to the analysis of study data and informed interpretation of results using specialist software.

Biostatistics is useful at the preclinical/translational stage to review and help you design appropriately powered preclinical studies, giving you confidence that your compound is safe and efficacious for medical use. It can also help more generally at the preclinical stage by clarifying the hypotheses that each study is aiming to address, and by ensuring that the time point selection, dose levels and dosing schedules etc. are optimally selected.

This will ensure that your studies provide crisp results and can therefore give you the clarity you need for a smooth decision-making process. Having well-designed and thought-out preclinical studies will also greatly help you receive approval from regulatory agencies for your First-In-Human trial. 

It is often essential, yet frequently overlooked, to involve a biostatistician in the early stages of the preclinical study design and research. Once the study is approved and running, data analysis may be able to reveal any study weaknesses (how it should have been designed to maximize the data output and minimize duration and expense), but by then it may be too late: “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” (R.A. Fisher)

The best-established role of statistics in drug design is clinical statistics. This can take the form of preparing Statistical Analysis Plans (SAP), performing power studies (sample size calculations), especially for phase II and III trials, and more advanced techniques such as re-assessment at interim stages and futility for clinical trials, as well as clinical trial data analysis itself and interpretation of results. Another important component involves trial design, randomization, sub-population selection, etc. 

Finally, non-clinical statistics deals with issues such as bioequivalence testing (commonly used for generics approval), CMC data analysis, etc.

ApPharMS can help you with all of the above and at all stages of the drug design process.