The analysis of data is in many respects the most component of assessing a clinical trial. Unfortunately, in the conduct of cancer clinical trials, the methods of analysis have not changed appreciably for decades, and despite an immense effort to collect data, response rate, and time to progression or death remain as the only variable harvested. This leaves behind an enormous amount of data that despite its collection is not analyzed and not leveraged for cutting edge data interpretation. The role of academic investigators is to provide independent, unbiased support for such analyses but with important academic goal of developing novel methods that can improve such analyses and importantly harvest the trove of untapped data collected
Providing consultation, explanations and clarifications to clinical investigators and basic scientists on the experimental design of clinical trials and statistical research approaches, requirements, and scientific standards to ensure the results are verifiable and can be reproduced. This is accomplished by in person or virtual presentations of the data during which presentations of the novel methods of analysis and their value are explained, and their advantages described
The performance of descriptive analyses of various types of data in clinical trials including numerical, categorical, and survival data as well as other quantifiable results.
Finding correlations and dependencies between desired outcome data and various available data sources that would be predictive of it is an important component of developing novel methods of analysis and often involves an iterative process
Interpreting data using exploratory mathematical and statistical techniques that will allow for hypothesis testing. Amongst the skills required are, linear regression, modeling, simulation, analysis of variance, survival analyses, parametric and non-parametric methods
Interpreting data using exploratory mathematical and statistical techniques that will allow for hypothesis testing. Amongst the skills required are, linear regression, modeling, simulation, analysis of variance, survival analyses, parametric and non-parametric methods.
Building of predictive models, that have had their features previously selected and optimizing classifiers using machine learning techniques/algorithms. This is especially important in the setting of radiomic analyses
Working with investigators to formalize analysis plans and reporting specifications. Providing advice as to the choice of statistical analysis strategies, reliability of measurements and identifiability of models, and interpretation and presentation of statistical results. Ongoing collaborations with several large pharmaceutical companies will allow for these approaches developed by Columbia University investigators to become endpoints used to assess clinical trial efficacy
Assisting in the preparation of clinical trial documents and reports, protocols, scientific abstracts, and peer-reviewed manuscripts
Using a variety of advanced statistical software, methods, and techniques to gather, analyze, and interpret research data to derive useful information for research data. Advising and assisting in the development of inferences and conclusions, as appropriate
Develop enhancements to statistical software, as appropriate, by programming new techniques. This requires maintaining knowledge of current and emerging trends in statistical analysis methodology. This also importantly requires the ability to write novel programs/macros to automate repetitive statistical and data tasks required.
Communicating outcomes via reports, visualizations, dashboards, and code notebooks.
Utilizes strong analytical skills to solve complex problems; exercises developed judgment based on the analysis of multiple sources of information.
Decision Making/Autonomy (Must equal 10)
Level of supervision required, on a scale of 1 (least) to 10 (most) – 2
Works under minimal supervision on complex assignments that require a high degree of initiative and independent judgment
Degree of independent judgment expected, on a scale of 1 (least) to 10 (most) – 8
Acts as a resource for new team members; manages schedule with PI's direction to ensure deadlines are met
Broad understanding of research techniques, software and instruments used in project
Ability to translate information to general terms and help others understand impact of the information; assists PI in preparation of publications, including funding proposals, manuscripts and other research-oriented documentation
Master's degree or equivalent in education/experience, and at least three years of related experience
Experience with quantitative data management and analysis
Proficiency with statistical software: R, SAS and SQL
4-6 years' experience
Columbia University is an Equal Opportunity Employer / Disability / Veteran
Pay Transparency Disclosure
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.
Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis. It seeks to attract a diverse and international faculty and student body, to support research and teaching on global issues, and to create academic relationships with many countries and regions. It expects all areas of the university to advance knowledge and learning at the highest level and to convey the products of its efforts to the world.