Titlebook: Statistical Methods in Biomarker and Early Clinical Development; Liang Fang,Cheng Su Book 2019 Springer Nature Switzerland AG 2019 Biomark

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Predictive Subgroup/Biomarker Identification and Machine Learning Methods,ction for the right patients (with genomic or other types of biomarker). The next two chapters will discuss statistical methods pertinent to biomarkers that are the important ingredient in developing precision medicine.

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Characterize and Dichotomize a Continuous Biomarker,ients into groups (i.e., stratification or subgroup identification for tailoring). However, such categorization may result in significant loss of information. It is important that the analytical method evaluates and incorporates the continuum of biomarker values to guide the optimization of a clinic

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Surrogate Biomarkers,2558 million, according to a new study by the Tufts Center for the Study of Drug Development. The most critical stages of drug development are phase 2 and 3 development. One major reason that the average drug development cost is so high is that there are many failures in later phase clinical develop

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Innovative Designs for Biomarker-Guided Trials, a unique opportunity to guide clinical trial designs. Traditional clinical development of a novel therapy utilizes the “one-size-fits-all” approach by testing treatment effect in the entire patient population with a specific disease. It assumes that response in the disease population is homogeneous

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Statistical Considerations in the Development of Companion Diagnostic Device,le of personalized medicine is the approval of the drug Herceptin in patients with breast cancer who tested positive for human epidermal growth factor receptor 2 (HER2). This is also one of the earliest examples of a co-development drug and companion diagnostic (hereafter CDx) model before there was

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Clinical Application of Molecular Features in Therapeutic Selection and Drug Development,al validity, with an illustrating example of BRAF V600E for both aspects. In this chapter, we focus on a specific type of biomarkers: the ones useful for therapeutic decision and therapeutic drug development in the clinical setting.

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Big Data, Real-World Data, and Machine Learning, is limited when diagnosing complex polygenic human disease or guiding efficient treatment. Large-scale studies of gene expression have much more chance to capture the signal from human disease. Meanwhile, sequencing technology is rapidly advancing enabling us to evaluate millions of genomic feature

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Design and Analysis of Clinical Pharmacology Studies, tolerability of a new drug entity. These studies characterize the absorption, distribution, metabolism, and elimination of a drug in human body, and provide critical information for drug labeling. In this chapter, we will introduce different types of clinical pharmacology studies, their PK and safe