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The modern drug development process generates data in unprecedented amounts and increasing complexities. Optimal handling and analysis of such data maximizes knowledge gain, providing research and business leaders with actionable insights and improved reproducibility. Bio-Statisticians thus, help Scientists maximize experimental Return On Investment (ROI) via Quality Assurance, Pattern Identification/Hypothesis Generation, Hypothesis Testing, Process Refinement and Publication Preparation.
Quality Assurance (QA): Biological data generation is often a multi-step process involving: sampling, shipping and processing. Further complicating matters, processing often involves automated: material handlers, amplification and bio-assays. Thus, modern biological data is commonly confounded with: batch/sampling effects, procedural bias, background noise, etc. Further complication matters, best practices encourage utilization of technical/biological replicates. Bio-Statisticians utilize modern analytical techniques to: evaluate QA metrics, detect and account for introduced biases, integrate information, optimize signal to noise, and detect outliners.
Pattern Identification/Hypothesis Generation: Modern bio-medical research generates unprecedented amounts of complex-content rich information that can be leveraged to create actionable insights at every stage of the drug development process. Optimal utilization of such information is non-trivial and can benefit immensely from the application of machine learning. Thus, enabling subject matter experts with a more efficient process by which to test and generate hypotheses, enhancing future research productivity.
Hypothesis Testing: Evidence driven scientific investigation increasingly requires the application of appropriate statistical testing within the integrated analytical approach. Even common hypothesis testing methods have tradeoffs and assumptions that must be met. Often, permutation testing or mixed modeling can be used to statistically address specific hypotheses in complex experimental designs where simpler tests are not appropriate. Beyond selection of best techniques and rigorous model validation, such statistical tests can be optimized for each specific use case. For example, a group identifying gene regulatory regions intended to be targeted for epigenetic drug discovery may wish to be “extremely confident” that any identified statistical relationship represents reality due to costs to be incurred moving forward. Whereas, groups developing assays to “predict” personalized treatment efficacy may wish to capture additional potential relationships as they refine their product(s).
Process Refinement: The bio-medical community has settled on a p-value of less than 0.05 as indicating a statistically significant result where, a p-value indicates the probability that a given set of observations occurs given that a “null” hypothesis is true. However, this threshold for significance is fundamentally different from being able to state that experimental results are likely to be 95% reproducible. As such, statisticians can work with subject matter experts and stakeholders at each step of the drug development process to determine “likelihood of success”. I.e. optimally detecting reproducible biological effects. Thus, facilitating process refinements/productivity enhancements for the bio-tech sector much like has been long provided by statisticians for other tech, financial and manufacturing segments. For example, a group performing pre-clinical in-vivo work may wish to optimize resources to assure reproducible results prior to publication. Alternatively, a high throughput screening operation section may wish to quantify and improve sensitivity/specificity at each stage of its processes.
Publication Preparation: Ever expanding drug development data generation necessitates increasingly sophisticated/elegant approaches for publication data presentation. While, journals increasingly require progressively greater degrees of statistical rigor to support findings. Bio-statisticians can help streamline the publication process by quickly producing publication ready/best practice compliant depictions of research data. Moreover, they can provide cogent explanations of analytical methodologies for Methods sections as well as precise interpretations of statistical findings for Results, etc. sections.
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