Generalized Competing Event Models Can Reduce Cost and Duration of Cancer Clinical Trials

Pipeline progress and portfolio management of the top 30 pharma companies over the past two decades

For pharmaceutical companies, it is essential to define their long-term corporate strategy. This especially involves the pipeline progress of pharmaceuticals and portfolio management. The objective of this study was to give a broad overview of study durations of the clinical trials from the top 30 pharmaceutical companies worldwide and to investigate what could possibly impact these study durations (e.g., indication areas, companies themselves, etc.) We worked with the clinicaltrials.gov database to examine the pipeline (phase 1-3) and portfolio (after regulatory approval) of the top 30 pharma companies worldwide over 20 years (from 2000-2020). We further calculated the study duration of each clinical study as the difference between the start date and end date. To analyze changes in our measure we estimated multiple linear regression to evaluate the impact of indication areas and companies on the study duration. Most of the clinical studies were conducted in the areas of ONCIM (N = 2720), and META (N = 1993). The indication with the highest study duration was ONCIM (on average 3.9 years per clinical study, SD: 0.8). Values for the study duration vary widely across companies. Mostly they range between 1 and 4 years (e.g., Merck Sharp & Dohme (MSD) on average 2.2 years per clinical study, SD: 1.0). Correlation analysis showed that study phases were positively correlated with the study duration (+ 0.36, p < 0.000), i.e., the higher the study phase, the higher the study duration. Furthermore, we found that indication areas influenced the study duration significantly (+ 0.17, p < 0.000). However, there were wide variations in effect sizes across indications. The results suggest that different indication areas influence the study duration to different extents. Pipeline progress and portfolio management differ widely between indications, companies and over the years. Research findings could help corporate strategy managers to make more informed decisions regarding their business development strategy.

Quantifying the feasibility of shortening clinical trial duration using surrogate markers

The potential benefit of using a surrogate marker in place of a long-term primary outcome is very attractive in terms of the impact on study length and cost. Many available methods for quantifying the effectiveness of a surrogate endpoint either rely on strict parametric modeling assumptions or require that the primary outcome and surrogate marker are fully observed that is, not subject to censoring. Moreover, available methods for quantifying surrogacy typically provide a proportion of treatment effect explained (PTE) measure and do not directly address the important questions of whether and how the trial can be ended earlier using the surrogate marker. In this article, we specifically address these important questions by proposing a PTE measure to quantify the feasibility of ending trials early based on endpoint information collected at an earlier landmark point t 0 in a time-to-event outcome setting. We provide a framework for deriving an optimally predicted outcome for individual patients at t 0 based on a combination of surrogate marker and event time information in the presence of censoring. We propose a non-parametric estimator for the PTE measure and derive the asymptotic properties of our estimators. Finite sample performance of our estimators are illustrated via extensive simulation studies and a real data application examining the potential of hemoglobin A1c and fasting plasma glucose to predict treatment effects on long term diabetes risk based on the Diabetes Prevention Program study.