Pre-clinical trials, involving experiments in-vitro (literally ‘in glass’, i.e. in the laboratory, typically involving cells) and in-vivo (‘in animals’) are an essential part of drug development as it is a regulatory requirement to investigate the safety of new drugs in-vitro and/or in-vivo before they are tested in humans. These trials can also be used to investigate the potential efficacy of new compounds.
When running a clinical trial the industry standard is a double-blind placebo‑controlled parallel group trial. This is because it is the best way to ensure that the characteristics of subjects in each treatment group are the same, whilst ensuring the investigators cannot anticipate the treatment of a subject.
The use of population-based disease registries to support ongoing data collection for long-term safety and clinical outcomes is becoming increasingly common. Data collection methods within registries can vary in terms of completeness and quality. This particular example arose from support to a post-registration commitment for marketing authorisation of a paediatric drug and aims to provide some insight to the techniques and strategies used to monitor paediatric development (growth, sexual maturation) and clinical outcomes of varying severity. The challenges of accounting for irregular follow-up and associated biases are illustrated, and potential statistical solutions described. Recommendations for future reporting are presented as part of the conclusions.
This blog post discusses the SAS/Graph Annotation option and how this can be used in combination with SAS Macros to allow the creation of multiple Forest Plots, giving details on what can and cannot be controlled as part of the macro call. The purpose of this paper is to highlight the methods of using the ANNOTATE Option available with the SAS/GRAPH procedures for producing a Forest Plot using the SAS system and how this can be adapted to allow multiple plots to be created using SAS Macros.
A member of Quanticate's statistical consultancy team presented a poster on “Using a Propensity-Pairing Algorithm to Reduce Bias due to Imbalances in Covariates: A Case Study Pooling Data from 5 Kidney Transplant Trials” at a conference for statisticians in the pharmaceutical industry (PSI). This work uses a range of statistical methods including stepwise logistic regression, conditional logistic regression, principal component scores and mixed modeling.
We aim to provide information and support written by our experienced staff. We want to share our knowledge and create an archive of information that you will be able to engage with, share and comment on.