DOI: 10.25934/pr00006477
title: Tositumomab : statistical modeling of expanded access programs and conventional trials

description: 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. Expanded access (EA), also known as early access, pre-approval access or compassionate use (Kimberly et al., 2017), is the formal regulation adopted by the Food and Drug Administration (FDA) in 1987 (Young et al., 1988),  propelled by the HIV/AIDS crisis. In the United States (US) the FDA regulates this process of formalized non-clinical trial access whereas in the European Union (EU) the responsibility lies with individual member states (Darrow et al., 2015).  The numbers of requests for EA are growing  (Jarow et al., 2017) and state and federal legislation, such as Right-to-Try laws in the US, stress the need and interest of patients in having earlier access to medicines that are still under clinical investigation.
EA data can be seen as a type of real-world data (RWD). RWD are ‘information on health care that is derived from multiple sources outside typical clinical research settings’ (Sherman et al., 2016). Recent publications and regulatory frameworks have boosted the promise of RWD  (Jarow et al., 2017; Stower, 2019). It can come in many forms and shapes, such as electronic health records, social media or claims databases.
EA programs also form a source of RWD. Historically though, EA programs were only deemed fit for treatment and not for research. Although the primary purpose of EA is treatment, scholars have argued that there is a moral obligation to collect outcome data  in all cases where patients are treated with investigational medicines (Bunnik, Aarts and van de Vathorst, 2018; Fountzilas, Said and Tsimberidou, 2018; Chapman et al., 2019). The debate on combining data collection and EA has substantially increased in recent years (S. Usdin, no date; Sutter, no date), with FDA-officials confirming beginning 2018 their willingness to review data from EA programs to support drug applications (Chapman et al., 2019). Considering the increasing interest in both expanded access and real-world data, the question arises whether alternative ways of access to novel treatments can provide clinical information and impact regulatory decision making. (Polak, van Rosmalen and Uyl - de Groot, 2020) have shown that there is an increasing trend in the use of RWD from EA by regulators and industry.
There has been a growing interest to include RWD in the medical domain, both from a regulatory and reimbursement perspective. Although randomized controlled trials (RCT) remain the gold standard for evidence generation, results are often obtained within highly controlled and regulated settings. This might limit the generalizability of findings. Another limitation of RCTs is that the sample sizes are often relatively small, especially for rare diseases and in disease areas where patients are reluctant to be randomized. The need for higher external validity of RCT results has led to an increased call for the use of RWD (FDA, 2018). Often, RWD are analyzed separately from the data derived in conventional clinical trials. Analysis of (single arm) RWD – and all other data sources where control groups are absent – has to be done with care. The absence of randomization introduces inherent confounding. Confounding can be partly overcome by statistical techniques, such as propensity score matching (Garrido et al., 2014), instrumental variables or adding the suspected confounding variables in the analysis.
Our research will help clarify the usability of these data. Awareness of the potential value of RWD from EA should facilitate that these data are incorporated in decision making whenever this is feasible and appropriate, and this may impact traditional clinical development. For patients, this better use of available data can result in speedier access to more diverse treatments. This project will yield direct insights, via scientific publications and statistical methodology, into how to best combine data from expanded access programs with data of (randomized) controlled trials. This is directly beneficial to regulators, drug developers and (bio)statisticians.
To illustrate our research, we will apply our methodology on the compound 'tositumomab', a monoclonal CD20-antibody that is available as treatment for non-Hodgkin lymphoma's. We will be jointly working on 5 studies on tositumomab, all in slightly different indications, and the expanded access program that encompasses indications that match the previous 5 studies. These data sets are made available for research via the Vivli Center for Global Clinical Research data (https://vivli.org) and have been made available by the marketing authorization holder GlaxoSmithKline.