The inclusion of real world evidence in clinical development planning

Abstract

Background: When designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects are typically obtained from randomised controlled clinical trials (RCTs). Based on the observed estimates of treatment effect, variability and the minimum clinical relevant difference to detect, the sample size for a subsequent trial is estimated. However, data from real world evidence (RWE) studies, such as observational studies and other interventional studies in patients in routine clinical practice, are not widely used in a systematic manner when designing studies. In this paper, we propose a framework for inclusion of RWE in planning of a clinical development programme.

Methods: In our proposed approach, all evidence, from both RCTs and RWE (i.e. from studies in routine clinical practice), available at the time of designing of a new clinical trial is combined in a Bayesian network meta-analysis (NMA). The results can be used to inform the design of the next clinical trial in the programme. The NMA was performed at key milestones, such as at the end of the phase II trial and prior to the design of key phase III studies. To illustrate the methods, we designed an alternative clinical development programme in multiple sclerosis using RWE through clinical trial simulations.

Results: Inclusion of RWE in the NMA and the resulting trial simulations demonstrated that 284 patients per arm were needed to achieve 90% power to detect effects of predetermined size in the TRANSFORMS study. For the FREEDOMS and FREEDOMS II clinical trials, 189 patients per arm were required. Overall there was a reduction in sample size of at least 40% across the three phase III studies, which translated to a time savings of at least 6 months for the undertaking of the fingolimod phase III programme.

Conclusion: The use of RWE resulted in a reduced sample size of the pivotal phase III studies, which led to substantial time savings compared to the approach of sample size calculations without RWE.

Keywords: Clinical development plan; Clinical trial simulation; Negative binomial model; Network meta-analysis; Relapse rate; Sample size.

Fig. 1

Left panel: network diagram of RCTs. Right panel: network diagram of RWE studies

Fig. 2

Network diagram including both RCTs and RWE studies up to the HTA submissions for fingolimod

Fig. 3

Graphical illustration of inclusion of RWE in the clinical development strategy

Fig. 4

Power curve of the simulated alternative TRANFORMS study (1000 simulations)

Fig. 5

Probability of achieving results observed in the original TRANSFORMS study for varying sample sizes

Fig. 6

Power curves based on 1000 trial simulations of a trial of active vs comparator

Fig. 7

Recruitment times in original TRANSFORMS and projected recruitment times in simulated TRANSFORMS for two alternatives

Fig. 8

Heatmap of NMA based on original trials (left) and simulated trials (right)