Estimating Probabilities of Success of Clinical Trials for Vaccines and Other Anti-Infective Therapeutics

Chi Heem Wong; Kien Wei Siah; Andrew W Lo

(A) DIFF = Constant + α1·TIME (B) DIFF = Constant + α1·TIME + α2·TIME 2 (C) DIFF = Constant + α1·TIME + α2·TIME 2 + α3·TIME 3 (D) DIFF = Constant + α1·TIME + α2·TIME 2 + α3·TIME 3 + α4·TIME 4 (E) DIFF = g(TIME), where g is a locally linear Gaussian kernel Models (A) to (D) are performed on Microsoft Excel 2019 while Model (E) is implemented using the statsmodel package in Python. The bandwidth for the non-parametric kernel regression is selected using the least-squares cross-validation method. The results of the regressions are reported in Table 11 . Our best fitting model, Model E, indicates that the difference between the number of non-vaccine treatment development programs initiated All Rights Reserved and the number of vaccine programs initiated widened between January 2000 and May 2014 before narrowing. The difference between the number of initiated programs is close to zero in December 2019, possibly due to the withdrawal of pharmaceutical companies from the antibiotics business and bankruptcy of antibiotics companies. . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.