Combining data from expanded access programs and conventional clinical trials: a statistical application to vemurafenib

Lead Investigator: Tobias Polak, Erasmus Medical Center
Title of Proposal Research: Combining data from expanded access programs and conventional clinical trials: a statistical application to vemurafenib
Vivli Data Request: 6475
Funding Source: Health~Holland grant EMCLSH20012 (Grant from the Dutch Government)
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Patients suffering from seriously debilitating or life-threatening conditions who are not eligible for further treatments or any clinical trials, may resort to ‘expanded access’: pre-approval access to investigational treatments. These programs are increasingly used to generate data in patients that are older, frailer, and therefore not suited for clinical trials. As these programs are non-randomized, several selection bias issues need to be accounted for.

This research investigates statistical methods to incorporate data from expanded access programs in the analyses of (randomized) controlled trials. In this work, we will illustrate novel techniques on the basis of trial data and expanded access data for vemurafenib. Although expanded access is not primarily seen as research-oriented, we sought to improve the useability of data from all patients that are treated in this setting with experimental medicine. This project will yield direct insights, via scientific publications and statistical methodology, that are directly beneficial to regulators, drug developers and (bio)statisticians. This project is different from proposal 6476, as we will cover a different drug under investigation (with various differing data sources).

Statistical Analysis Plan:

We aim to develop and evaluate methods for (i) dynamic borrowing of other data scores and (ii) methods for attenuating bias by confounding. We here provide further background on these methods and our plans.

In the analysis of clinical trials, there is often data from relevant previous studies available. A common example is the availability of data from a previous RCT in which the control arm patients received the same treatment as in the new (current) study. Provided that the studies are sufficiently similar in terms of setting and outcomes, the data of the historical controls can be incorporated into the analysis of the current trial. Due to the possibility of between-study heterogeneity (e.g. differences in study populations between trials), a naïve pooling of historical controls and the current controls is rarely acceptable. Statistical methods have been devised to appropriately downweight the historical data, based on their characteristics and the observed differences with the current controls. A well-known technique for downweighting the historical controls is the power prior (Duan, Smith and Ye, 2006; Neuenschwander, Branson and Spiegelhalter, 2009; van Rosmalen et al., 2018).

We are proposing to extend the ideas of the power prior and other methods for borrowing historical data to RWD data from EA programs. In this case, we downweight the RWD instead of the historical controls. The downweighting serves to account for a) the lower grade of evidence of EA data (compared to RCT data) due to the lack of randomization and the less controlled setting and b) center-specific and study-specific effects that may differ between EA data and RCT data. In the power prior method, this is done by raising the likelihood for the RWD, Λ(θ│Y_RWD ), to a power of α, with α between 0 and 1, whereas the likelihood of the RCT, Λ(θ│Y_RCT ), is not discounted . This leads to

p(θ│α,Y_RCT,Y_RWD )∝Λ(θ│Y_RCT )×Λ(θ│Y_RWD )^α×p(θ)

The power of α can be interpreted as a discounting factor between the data from randomized controlled trials and the data from the real-world. With α = 0, the real-world data is completely ignored and with α=1, the datasets are simply pooled. However, in the above specification, the power α has to be set beforehand. One may resort to Bayesian techniques to estimate α:

p(θ│α,Y_RCT,Y_RWD )∝1/C(α) Λ(θ│Y_RCT )×Λ(θ│Y_RWD )^α×p(θ)×p(α),

where C(α) is a normalizing constant. Estimation of α using the available data leads to adaptive borrowing of information from the RWD: when the RCT data and the RWD are sufficiently similar, α will be estimated as high, and the RWD will be mostly included in the analysis; however when RWD are in conflict with the RCT data, α will be estimated to have a low value, and the RWD will be effectively discarded. This approach is based on the principle that the RCT data represent the highest level of evidence, whereas the RWD have an observational study design with a lower level of evidence.

It will also be necessary to account for differences in disease severity and other patient characteristics between EA data and RCT data. This can be done by combining the power prior approach either with covariate adjustment or with propensity score methods. This has been recently published by (Wang and Rosner, 2019; Wang et al., 2019; Chen et al., 2020). and an RPackage ‘psrwe’ accompanies their findings. They have not, however, evaluated their methods with extensive simulation. We plan to do this and subsequently apply our findings to a non-simulated dataset.

There are also other methods for discounting historical data, such as methods based on a meta-analysis of all available studies, i.e. the meta-analytic-predictive prior (Neuenschwander et al., 2010; Schmidli et al., 2014). Our approach will be compared with these existing alternatives in terms of frequentist characteristics (type I error rate, mean squared error, and statistical power) as well as in terms of performance in real data sets.

Requested Studies:

Data Contributor: Roche
Study ID: NCT01248936
Sponsor ID: ML25597

An Open-label Multicenter Study on the Efficacy of Continuous Oral Dosing of Vemurafenib on Tumour Response in Previously Treated Patients With Metastatic Melanoma
Data Contributor: Roche
Study ID: NCT00949702
Sponsor ID: NP22657

BRIM 3: A Randomized, Open-Label, Controlled, Multicenter, Phase III Study in Previously Untreated Patients With Unresectable Stage IIIC or Stage IV Melanoma With V600E BRAF Mutation Receiving Vemurafenib (RO5185426) or Dacarbazine
Data Contributor: Roche
Study ID: NCT01006980
Sponsor ID: NO25026

An Open-label, Single-arm, Phase II, Multicenter Study, to Evaluate the Efficacy of Vemurafenib in Metastatic Melanoma Patients With Brain Metastases
Data Contributor: Roche
Study ID: NCT01378975
Sponsor ID: MO25743

Dutch Melanoma Treatment Registry – a real-world registry maintained by Dutch physicians looking at the real-world treatment regimens, safety, effectiveness and costs of treatment regimens for melanoma.
Data Contributor: I WILL BRING MY OWN
Sponsor ID: Dutch Melanoma Treatment Registry

Public Disclosures:

Polak, T.B., Labrecque, J.A., Groot, C.A. and van Rosmalen, J., 2023. Augmenting treatment arms with external data through propensity-score weighted power-priors: an application in expanded access. arXiv preprint. doi: arXiv:2306.01557.