Statistical Methods for Cut Point Analysis in Immunogenicity Assays
Location: Parkville, VIC
Duration: 4-6 months
Proposed start date: ASAP
CSL is a global biotechnology company headquartered in Melbourne. With operations in more than 30 nations, they employ 22,000 people worldwide. CSL develop and deliver biotherapies to prevent and treat people with life-threatening medical conditions. Their broad range of therapies include those to treat disorders such as haemophilia and primary immune deficiencies, and vaccines to prevent influenza. CSL’s Data Science team is situated within Research and works closely with a number of academic collaborators and in-house biomedical research experts to deliver cutting edge solutions in the areas of experimental design, statistical analysis, data visualisation and mathematical modelling.
CSL’s Research Clinical Bioanalytics group is looking to improve their assay development process by optimising statistical approaches when dealing with immunogenicity. Immunogenicity is an immune response against a therapeutic antigen that leads to development of anti-drug antibodies (ADAs), inactivating therapeutic effect of the treatment and in rare cases inducing adverse reactions. It is a significant concern in administration of biologic drugs. Immunogenicity evaluation is required by regulatory agencies and relies on well-developed and validated assays.
One of the important parameters of assay development is determination of a cut point during assay validation. Although many statistical methods and software tools have been suggested and developed, the consensus on the most appropriate method or tool for different experimental designs hasn’t been reached. Methods are either too complicated to be of practical use by scientists without assistance from statisticians, or they lack statistical justification. This is particularly true for the more complex experimental designs that are used by the Clinical Bioanalytics group.
Some of the important statistical considerations that need to be addressed are data normalisation to account for different sources of variability, standards for outlier detection and removal (both biological and analytical) and methods for cut-point determination.
Research to be Conducted
CSL’s Research Clinical Bioanalytics group is looking for recommendations and development of the most appropriate methods of statistical analysis based on their experimental design and generated data. Ideally, the method should maintain a good balance between statistical rigor and implementation simplicity.
The main aims of this project are:
- Successful applicant will familiarise themselves with experimental design and data generated in the Clinical Bioanalytics group and perform a comprehensive literature review on statistical methods and software tools available for immunogenicity data analysis.
- Make recommendations and develop most appropriate method for the Clinical Bioanalytics group based on 1.
- Contribute towards implementation of that method in an easy to use software tool for scientists with limited statistical background.
The successful applicant would work within CSL’s Data Science team situated within CSL Research at the Bio21 institute and be supervised by a senior statistician. The applicant will also have support from an experienced software developer and subject matter experts. This is a unique opportunity to experience working in the dynamic Research department of Australia’s largest biotechnology company while also having scope to publish the recommended method in a peer-reviewed academic journal.
We are looking for a PhD student with the following skills:
- Strong background in statistics or closely related area
- Experience in conducting literature reviews
- Basic programming skills (preferably in R)
- Understanding of basic biology
- Ability to work in a multi-disciplinary team environment and pay attention to detail
- Experience and/or knowledge of assay development process
The primary outcome of this project is recommendation of the most appropriate statistical method for analysing immunogenicity assay data to the Clinical Bioanalytics group’s management team.
The deliverables expected would be project report consisting of comprehensive literature review as well as suggested software model to aid scientists in data analysis without statistical supervision. Bulk of this report should be in the format suitable for scientific publication.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
23 January 2019
INT – 0568