Comparing oncology clinical programs by use of innovative designs and expected net present value optimization: Which adaptive approach leads to the best result?

Designing an oncology clinical program is more challenging than designing a single study. The standard approaches have been proven to be not very successful during the last decade; the failure rate of Phase 2 and Phase 3 trials in oncology remains high. Improving a development strategy by applying innovative statistical methods is one of the major objectives of a drug development process.

The shrinking or disappearing observed treatment effect.

It is frequently noted that an initial clinical trial finding was not reproduced in a later trial. This is often met with some surprise. Yet, there is a relatively straightforward reason partially responsible for this observation.

A Practical Guide to Data Monitoring Committees in Adaptive Trials.

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials.

Advances in Statistical Approaches Oncology Drug Development.

We describe some recent developments in statistical methodology and practice in oncology drug development from an academic and an industry perspective. Many adaptive designs were pioneered in oncology, and oncology is still at the forefront of novel methods to enable better and faster Go/No-Go decision making while controlling the cost.

Improving Oncology Clinical Programs by Use of Innovative Designs and Comparing Them via Simulations.

The design of an oncology clinical program is much more challenging than the design of a single study. The standard approach has been proven to be not very successful during the past decade; the failure rate of phase 3 studies in oncology is about 66%. Improving the development strategy by applying innovative statistical methods is one of the major objectives for study teams designing and supporting oncology clinical programs.

The practice of pre-marketing safety assessment in drug development.

The last 15 years have seen a substantial increase in efforts devoted to safety assessment by statisticians in the pharmaceutical industry. While some of these efforts were driven by regulations and public demand for safer products, much of the motivation came from the realization that there is a strong need for a systematic approach to safety planning, evaluation, and reporting at the program level throughout the drug development life cycle. An efficient process can help us identify safety signals early and afford us the opportunity to develop effective risk minimization plan early in the development cycle.

Hypothesis testing and Bayesian estimation using a sigmoid Emax model applied to sparse dose-response designs.

Application of a sigmoid Emax model is described for the assessment of dose-response with designs containing a small number of doses (typically, three to six). The expanded model is a common Emax model with a power (Hill) parameter applied to dose and the ED50 parameter. The model will be evaluated following a strategy proposed by Bretz et al.