Bayesian Analysis with R for Drug Development (2019) Pdf free Download

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Features of Bayesian Analysis with R for Drug Development (2019) PDF free download:


Drug development is an empirical problem-solving process, characterized by trial and error. Knowledge gleaned from the previous study is often used to guide the next experiment. The sequential learning nature of experimentation and reliance on knowledge from various drug development stages calls for statistical methods that enable synthesis of information from disparate sources to aid better decision-making. Bayesian statistics provide a flexible framework for continuous update of learning based on new information. In addition, Bayesian analysis allows for the incorporation of prior knowledge, in terms of either expert opinion or historical data, in its statistical inferences. This not only helps ease the reliance on large sample approximations that are often required for frequentist methods but also often results in greater efficiency in study design.
Furthermore, many practitioners in drug development find it difficult to interpret the frequentist interval estimates and p-values, consequently creating a challenging and confusing situation for decision-makers. In contrast, the results of Bayesian analysis are typically presented through probabilistic statements. For example, after analyzing data from a two-arm comparative study, in which a test drug is assessed against the standard of care (SOC) therapy, both the frequentist and Bayesian conclude that the experimental drug is more effective than the SOC. The Bayesian directly concludes that the experimental drug is 20% more effective than the SOC with 95% probability. In contrast, the frequentist deduces that, conditioned on the data, the effectiveness of the experimental drug cannot be equal to that of the SOC (and thus, the experimental drug must be the better treatment). The straightforward feature of the Bayesian inference is extremely desirable as it brings a risk-based approach to bear for decision-making in drug development, as recommended by the current regulatory guidelines.

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