Assurance methods for designing a clinical trial with a delayed treatment effect.

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur.

An efficient and reliable chemical inventory system at a growing drug discovery company.

Within a growing drug discovery company, hundreds of scientists take and move reagent chemicals on a daily basis. Conveniently updating the locations of tens of thousands of chemical containers in an electronic system is a big challenge. We have an electronic inventory system, but keeping the chemical records up to date relied on scientists finding an available computer in the lab and logging into a system whenever they took or moved a chemical container, taking several minutes. It was all too easy to think that the task could be deferred until later, but the scientist may then forget all about it, leaving the database inaccurate. As a result, searching for the chemicals we need can take a frustratingly long time if they are not where they should be. We have developed an efficient and reliable system, namely ScanStation, to solve this issue. It relies on a low-cost Raspberry Pi attached to a touch screen connected with a barcode scanner. This equipment is always on and placed in strategic locations around all our labs and chemical stores. This new process is much easier. There is no need to remove gloves to log into the computer, and it is just a scan on the barcode and is done. Now when we check the database for a chemical, we get an instant answer that reliably tells us where to find it. All the time we save have a real benefit in our productivity and our scientists can focus on discovering new medicines.