For the last decades, people have paid much attention to the high-quality statistical designs in global pharmaceutical development. Because they want to make sure that the candidate compound under investigation could be with good drug characteristics (such as identity, strength, purity, quality, safety, efficacy and stability) both before and post approval. There are usually three stages during a pharmaceutical development process: 1) non-clinical, conducting assay validation and stability testing. 2) pre-clinical, conducting animal and bioavailability/bioequivalence studies. 3) clinical, conducting phase 1 to phase 3 clinical trials. According to the United States Pharmacopeia and National Formulary (USP/NF), we have summarized several commonly used statistical designs at different stages of non-clinical, pre-clinical, and clinical development. We also discussed the potential use of various adaptive trial designs in clinical development. This design strategy not only accelerates the development process but also makes full use of the limited available resources to increase the success ratio of the development process.
The crossover design is a repetitive measurement design. In this design, each experimental unit (patient) receives a different treatment during different time periods, i.e., the patient spans from one treatment to another during the trial.
When every participant receives one and only one treatment in a random manner, this kind of complete randomized design is called a parallel group design. The most important element of this design is randomization which means participants are randomly placed into a group to lower the risk of statistical bias or other kinds of erroneous results.
A cluster randomized design is a trial design using clusters as experimental units which take a consideration of groups of participants as randomization units rather than individuals. If it's hard to give a treatment to individual patients in a community or social unit and the outcome is not affected in the standard care arm of the trial, the cluster randomization could be done.
When design a dose escalation plan in a phase I trial, we usually accept guiding principles as follows: avoiding unnecessary exposure of patients to sub-therapeutic dose of an agent which means treating maximum number of patients within the therapeutic dose range; keeping safety; and maintaining rapid accrual.
Flexible Sample Size Re-estimation Design
In the development process of drug products, biologic products and devices, there are a lot of uncertainly key factors which make sample size estimation essential for confirmatory clinical trials. This uncertainty may be from the quantitative information used for the calculation of sample size, such as the effect size and any other nuisance parameters including the variability associated with primary outcome and the control group event rate.
As a particular kind of configuration of integers, letters of the alphabet or other symbols, Latin square has received a lot of researchers' interest for a long time. According to Fisher (1925), Latin square design could be very useful in experimental researches to control the effects of extraneous variables.
The randomized block design is equivalent to the stratified random sampling in research designs. A block is a group of experiments subjects that are known to be somehow similar before conducting the experiment and the way in which they are similar is expected to have an effect on the response to the treatments.
When the patent of an innovative (or branded) drug is about to expire, a pharmaceutical company or generic company can submit an abbreviated new drug application (ANDA) for generic drug approval. The definition of generic drugs is drug products which are identical to an innovative (branded) drug that has an NDA approved by FDA.
As the most basic type of study design, the completely randomized design (CRD) forms the basis for many other complex designs. CRD is one of the most popular study designs and can be applied in a wide range of research areas such as behavioral sciences and agriculture sciences.
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