Webinar on Adaptive Designs for Dose Finding: Part 2

August 19, 2020

Bjoern Bornkamp, Statistical Methodologist at Novartis and Jose Pinheiro, Senior Director, Johnson & Johnson provided their insights on adaptive designs for dose finding in Cytel’s latest webinar. The webinar demonstrates how adaptive and Bayesian techniques can be implemented for optimal dose-finding.

This two-part blog series provides a summary of the webinar. Read the first part to get key highlights from the presentation by Jose Pinheiro on the need to conduct dose finding Phase 2 studies, dose selection comparisons and the use of MCP-Mod for dose finding. 

Continue reading this second part to learn about the methods of adaptive dose-finding, presented by Bjoern. Click the button to access the webinar recording and download the presentation slides

Access Replay

Purpose of using adaptive dose-finding

At the design stage of Phase 2 trials you need to make two important choices:

1. Which dose are you going to utilize?
2. How many patients do you need for your study?

It is important at this stage to know, what is the shape of the response curve and what is the magnitude of the treatment effect that you can achieve with the dose range that you are studying? Adaptive designs allow to mitigate some of the uncertainties in the study and make the study design robust with respect to assumptions made at design stage.

You can choose the dose finding study to add or drop doses at the interim analysis or change the allocation ratio to the different doses. This will allow a proper determination of the dose-response curve at the end of the trial. It is important not only for the estimation of dose-response curve or to decide which dose to take forward; but also, for dose-justification.

Methods of adaptive dose-finding

Use of Bayesian methodology and recurrent interim analyses (ASTIN Trial) The approach used a non-parametric (time-series) model to estimate dose-response. A large number of doses were utilized, and a longitudinal model was used to predict patients with incomplete data. Dose-allocation during the study were done to increase estimation of dose-response at ED95. Stopping was based on posterior probabilities of the effect at ED95.

Adaptive Optimal Design – This approach is based on parametric sigmoid Emax model. * The basic idea of this approach is to use optimal design theory and the challenge is that optimal design is based on the correct dose-response curve. As you may not know this at the beginning of the trial, you can use an interim estimate dose-response model and allocate patients according to an optimal design for specific purpose (e.g. D-optimality).

Rule-Based Design – In this approach, we do not use the statistical efficiency criteria and perform updating according to heuristic rules. Bjoern explains the working of this method with the help of Ivanova et al (2008) and Mercier et al (2015) cases.

Adaptation does not always improve design as interim estimates are based on noisy data, and improvement depends on whether the starting design was already good. However, Adaptation does not decrease performance substantially, in particular, if Bayesian updating is performed.

While conducting dose finding studies, it is also important to make certain logistical considerations that are non-statistical in nature. There are three critical metrics you need to consider in terms of logistics - recruitment rate, time to measurement of endpoint (T), time needed to perform interim analysis.

Bjoern presents a case study on Multiple Sclerosis (MS) which is a chronic, inflammatory, degenerative neurological autoimmune disease with no known cure. This trial achieved the planned objective and the adaptive dose-finding was generally well accepted by investigators and regulators. When synthesized with other evidence (e.g. multiple efficacy and safety endpoints), it supported dosing strategy for Phase 3.

Click the button to access the on demand webinar.

Access Replay

*See Dragalin et al (2007) and Dragalin et al (2010)


This webinar is a part of Cytel’s “Introduction to Complex Innovative Trial Design” webinar series. It aims to introduce clinical fellows, early career biostatisticians, and others interested in clinical research, to some of the more commonly used complex innovative trial designs. Register to attend the upcoming webinar with Nitin Patel, Co-founder of Cytel, on designing clinical trials from a program perspective.