Clinical Forecasting: A Novel Bayesian Tool for Predicting Phase III Outcomes

By Asher D. Schachter, MD

Tables

Table 1.1. Advantages of Zebrafish in Drug Development

Table 4.1. Impact of 78% Accurate Clinical Forecasting on Public Companies

Table 1A. Preference Table for Value Node in Figure 1A

Figures

Figure 1.1. Example of a Pharmacokinetic Profile

Figure 1.2. Role of Bayesian Networks in Phase IV

Figure 2.1. Clinical Variables Believed Most Crucial to NCE Clinical Success

Figure 2.2. Overview of Algorithm for Constructing Leaf Node CPTs

Figure 2.3. Clinical Forecasting Models Empower Market Forecasts

Figure 3.1. Prior and Posterior Probability Distributions: Clinical Success for rhAPC

Figure 3.2. Prior and Posterior Probability Distributions: Safety and Efficacy for rhAPC

Figure 3.3. Effect of Setting Prior Bias to "Optimistic" on Prior and Posterior Probability Distributions: Clinical Success for rhAPC

Figure 5.1. Societal Impact of Widespread Adoption of Accurate Clinical Forecasting Methods

Figure 5.2. Time Lag from Initial NDA Approval to Pediatric sNDA Submission

Figure 1A. A Simple Influence Diagram (ID)

Figure 2A. A 3-Layer Bayesian Network