Reporting and communication of sample size calculations in adaptive clinical trials: a review of trial protocols and grant applications

BACKGROUND

An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials.

METHOD

We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed.

RESULTS

We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%).

CONCLUSION

Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved.

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.

Not too big, not too small: a goldilocks approach to sample size selection

We present a Bayesian adaptive design for a confirmatory trial to select a trial's sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, "Is the sample size too big, too small, or just right?" We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints.

The other randomization - methods for labeling drug kits

PURPOSE

Most phases 2 and 3 blinded randomized clinical trials package study drug, e.g., active and placebo, into drug kits for distribution to investigational sites. Kits are made so that it is not possible to determine the type of drug in the kit. This enables investigators to administer drug to patients in a manner that blinds investigators and patients. Kits are labeled with unique kit IDs that code for the drug type. Kit lists contain the assignment of kit IDs to drug. Algorithms for making kit lists, like algorithms for randomizing patients, incorporate randomness to ensure investigators and patients are blind to the process. This paper reviews three types of kit list: blocked, double randomized, and scrambled, and discusses the operational benefits of what a pharmaceutical company might obtain using scrambled lists along with an overview of the challenges of generating and extending the lists for large trials.

METHODS

We reviewed the operational characteristics of three types of kit list: blocked, double randomized, and scrambled.

RESULTS

Blocked kit lists were a popular choice until their unblinding and operational deficiencies became well known. The many difficulties associated with blocked kit lists are unnecessary and can be avoided by using a double randomized kit list or a scrambled kit list. Compared to double randomized kit lists, scrambled kit lists can be more easily extended due to their advantage of having randomly sized gaps between kit IDs.

CONCLUSIONS

Among the three types of kit list, scrambled kit lists offer the most flexibility. The adoption of scrambled kit lists has in practice provided the many operational benefits described in this paper.

Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges

In this paper, we describe developments in adaptive design methodology and discuss implementation strategies and operational challenges in early phase adaptive clinical trials. The BATTLE trial - the first completed, biomarker-based, Bayesian adaptive randomized study in lung cancer - is presented as a case study to illustrate main ideas and share learnings.