In most instances of blinded sample size re-estimation, the timing of the interim analysis that determines whether the sample should be increased is preplanned. Yet such an approach is not necessarily the best for a sponsor. There could be benefits to continuously monitoring a clinical trial to see if a sample size re-estimation is necessary. In such cases, what are the costs to the sponsor in terms of power and sample size? New simulations by Cytel’s Martin Kappler and Ursula Garczarek assess the costs and consequences of this approach.
Cyrus Mehta on the Founding of Cytel
On the occasion of Cytel’s 35th anniversary, co-founder Professor Cyrus Mehta discusses the founding of Cytel, the evolution of the industry over the last 35 years, and the ongoing innovations in software and statistical strategy.
Decision Making in Early Clinical Development
On March 16th and 17th the 5th East User Group Meeting took place in London. This very successful 2 days saw a variety of talks on aspects of clinical trial design innovation. Over the next couple of weeks, we will be reviewing some of the key topics which were addressed during the meeting.
In this post, we’ll take a look at Paul Frewer of Astrazeneca’s presentation on Decision Making in Early Phase Clinical Development. This talk was very well received by the delegates and prompted plenty of discussion afterwards.
Defining Probability of Success in Clinical Trial Design with Commercial Software and R Coding
One of the pivotal metrics considered when designing a clinical trial is the study’s probability of success, which can be measured in several ways. Each definition of probability of success has benefits and limitations both in the way it is measured, and the way it is accounted for in study design and execution.
Here, we examine two definitions of probability of success in similar settings and describe how each can be defined and incorporated into a study design using a combination of commercial software and R coding. In leveraging two tools, statisticians can rely on the power and confidence of commercial software in conjunction with the flexibility of R code.
Bayesian Adaptive Clinical Trial Designs: INLA vs. MCMC
Bayesian methods have continuously played a key role in transforming clinical research in therapeutic areas such as oncology and rare diseases, and in addressing clinical development challenges for COVID-19 drugs, devices, and biologics. From early-phase trials to late-phase development, utilizing Bayesian tools can expedite and/or de-risk trials, even when used to augment a Frequentist framework.
Bayesian Hierarchical Modelling for Histology-Independent Therapies
Pharmaceutical research in oncology is increasingly focused on the development of therapies targeted at newly identified driver mutations. However, the rarity of many of these mutations presents new challenges for trial design and patient recruitment. In particular, it may be difficult in practice to recruit enough patients with the same mutation and tumor histology. One solution has been to evaluate these histology independent therapies (HITs) in basket trials, which recruit patients across multiple histologies that all share a common targetable driver mutation.
Adaptive Multi-Arm Multi-Stage Designs: A Comparison of Methods
The significant time and cost, as well as high failure rates, of clinical trials have necessitated innovative trial methodologies that are more efficient and informative. Adaptive clinical trial designs are a type of study design that allows pre-specified modifications to be made to the trial protocol based on interim looks at accumulating data. These designs are becoming increasingly popular as they offer several scientific, financial, strategic, and ethical advantages over traditional clinical trial designs, such as increased efficiency, reduced cost, and greater flexibility.
One such design type is multi-arm multi-stage designs, or MAMS designs. And the relatively recent cumulative MAMS design is, as we will explain below, the preferred option with respect to power. Read more »
Adaptive Trials at the Mainstream of Drug Development
Adaptive trial designs — that is, trials that include a prospectively planned modification based on an interim analysis – have been traditionally associated with greater efficiency and better prospects for patients without compromising statistical rigor, but new holistic approaches to creating these designs are making room for transformative, even revolutionary, changes to strategic drug development. Cytel has developed a ten-point decision framework to see whether adaptive designs are the way forward for your clinical trial – read more below.
An Introduction to BLRM
Traditional rule-based approaches to dose escalation such as 3+3 are widely used in early
clinical development. They can be appealing due to the simplicity of execution. However, estimates produced may be highly variable and the targeting of true Maximum Tolerated Dose may be poor. Bayesian dose escalation approaches in early phase trials can offer an effective alternative to determining the maximum tolerable dose of a new drug more quickly, as well as ensuring that all of the information available to trial clinicians is taken into account so that the patients enrolled in the trial receive the best possible treatment.
2015 Highlights – Seamless Adaptive Clinical Trials: Now that we get the statistics, what’s really at stake?
Day three of our 2015 Highlight series. Our third most popular blog post is a look at the realities of implementing a seamless trial.
Seamless adaptive clinical trials have gained popularity for reducing the projected time it takes to complete the process of drug development. However, a new study by Cuffe et al., shows that despite a tremendous amount of statistical knowledge about seamless trials, sponsors remain unsure about how to calculate the financial and operational costs of a seamless clinical development program [1]. This in turn results in many unnecessary risks and missed opportunities.
This post offers advice on what you need to keep in mind in order to implement a successful seamless adaptive clinical study.
What is a Seamless Trial?
Also called a combined-phase study, the idea behind a seamless trial is simple: Instead of conducting several phases of a study, plan one adaptive trial where the phases are separated by interim looks. This tends to save time and reduce the number of patients.
For example, the seamless Phase 2/3 ADVENT trial (click image at right to read case study), took a clinical program whose traditional design would have been four Phase 2 studies and one large Phase 3 study, and combined it into a four arm trial, which dropped the two less successful arms after the interim look [1]. The data obtained from the successful active arm prior to the interim look was able to play the dual role of confirming safety, and thereafter establishing efficacy. Employing a seamless adaptive late phase trial reduced sample size from 520 to 350 [2].
When conducting early phase trials, seamless proof-of-concept and dose-finding trials have also become more popular. Their benefit lies in the fact that combining these two trials into one larger trial allows significant reduction in trial time. Cytel Consultants recently reduced trial time by an expected 9-12 months (and 100 fewer patients) by employing such a design.
Varieties of Seamless Trials:
Most seamless trials can be split into two broad categories. An inferentially seamless trial is one where some of the data used prior to the interim look also plays an inferential role after the interim look. Consider once again the combined Phase 2/3 ADVENT trial. The four arm trial prior to the interim look was meant to establish safety. This means that the data from the arm chosen to continue to the confirmatory part of the trial played two roles: prior to the interim look it helped establish safety, after the interim look the same data from this arm, combined with the data collected post-look, also helped to establish efficacy. As a result, the two look trial was inferentially seamless.
An operationally seamless trial, by contrast, is one where the data evaluated after the interim look is kept distinct from the data evaluated prior to the interim look. Each set of data has its distinctive purpose.
The Purported Inflexibility of Seamless Studies:
The flexibility of an adaptive design is often touted as one of its greatest advantages. Based on data collected at an interim look, DMCs can decide how to move forward in a manner which gives the new drug, device or biologic the best possible chance to prove its safety or efficacy.
Seamless studies are beneficial for reducing sample size and increasing the speed of the trial. Unlike other adaptive designs, however, seamless studies may be somewhat less flexible. This is particularly true for inferentially seamless trials. Cuffe et al., cite two reasons for this:
- In traditional trial designs, clinical trial sponsors are able to look at the entire set of data collected after a given phase, and make key decisions about the designs of the phases that follow. By contrast, unless seamless adaptive studies allow for data to be unblinded at an interim look, DMC members must rely on go/no-go decision rules for the second stage of the trial. These rules will have to be determined even before the first stage of the trial begins, which means it may not be possible to take advantage of all of the information which the first stage can provide.
- In inferentially seamless trials, the final analysis combines a part of the data that was collected prior to the interim look with data collected post-interim look. In order to use this data, certain constraints must be placed on the entire trial design. As Cuffe et al., explain, “[I]t is worth noting that a seamless study allows only limited changes to the Phase III portion: substantial changes to study conduct can mean that the two portions answer different clinical questions.” [1]
The fact is that in a combined phase study, the basic structure of the post-look portion of the trial has to be determined prior to the interim look. Unlike in a traditional clinical program, a combined Phase 2/3 trial may not allow sponsors to look at all of the unblinded interim data to take advantage of new information which could affect design decisions.
Overcoming the Inflexibility of a Seamless Study:
Although there is no doubt that seamless trials place certain restraints on late phase trials, sponsors have several reasons to employ a seamless design.
- Early Phase Advantages: Cuffe et al., find that in practice, many of the restrictions cited above only apply to late phase studies. Their findings reveal that an early phase seamless adaptive study ‘incorporated multiple adaptations and took advantage of safety data from a dose-escalation study to increases the range of doses in the second portion…’ [1]
- Late Phase Advantages: Although late phase seamless studies might not allow for as much flexibility as other adaptive trials, they have the potential to reduce sample size rather dramatically. In the Phase 3 ADVENT trial designed by Cytel, the use of a seamless study reduced sample size from 520 to 350. Securing a significantly smaller trial made the inflexibility worth it for the sponsors of the ADVENT trial.
Notes:
[1]: Cuffe, Robert L., et al. “When is a seamless study desirable? Case studies from different pharmaceutical sponsors.” Pharmaceutical statistics 13.4 (2014): 229-237.
[2] Operationally Seamless & Inferentially Seamless Adaptive Designs