Minimizing control group allocation in randomized trials using dynamic borrowing of external control data – An application to second line therapy for non-small cell lung cancer

Abstract

BackgroundEnrollment of participants to control arms in clinical trials can be challenging. This is particularly an issue in oncology trials where the standard-of-care is shifting rapidly and several promising experimental treatments are undergoing phase III testing. Novel methods for utilizing external control data may mitigate these challenges, but applied examples are sparse. Here, we therefore illustrate how Bayesian dynamic borrowing of external individual patient level control data from similar clinical trials can often reduce randomization to the control intervention without substantially trading-off precision. We further explore which types of scenarios yield viable trade-offs, and which do not.Patients and methodsWe obtained individual patient data on patients being treated with second-line therapy for non-small cell lung cancer from Project Data Sphere with minimal in/exclusion criteria restrictions, and applied Bayesian hierarchical models with uninformative priors to generate illustrative synthetic control groups.ResultsFour phase III clinical trials were identified and utilized in our analysis. Even when studies which are knowingly incongruent with one another are selected to generate a synthetic control, the nature of this methodology minimizes improper borrowing from historical data. The use of a small concurrent control group within a trial greatly reduces penalized selection, and our results demonstrate the ability to reduce allocation to the control group by up to 80% with a minimal increase in uncertainty when closely matched historical data is available.ConclusionDynamic borrowing using Bayesian hierarchical models with uninformative priors represents a novel approach to utilizing external controls for comparative estimates using single arm evidence.

Combinatorial benefit without synergy in recent clinical trials of immune checkpoint inhibitors

“…Thus our test of independence likely underestimated atezolizumab’s single-agent activity. To address this we used clinically observed differences in atezolizumab activity by line of therapy 28 to construct a synthetic arm - as recently discussed for NSCLC 30 - of first-line atezolizumab for nonsquamous NSCLC ( Figure 2k ; Supplementary Figure 1 ) and found that IMpower150 now closely matched the expectation of independence (Hazard Ratio 1.04, P = 0.46, n = 356; Figure 3a , dotted line denotes adjusted hazard ratio).…”

Predictable clinical benefits without synergy in trials of combination therapies with immune checkpoint inhibitors

“…Thus, our initial test of independence likely underestimated atezolizumab's single-agent activity. To address this we used clinically observed differences in atezolizumab activity by line of therapy 28 to construct a synthetic arm -as recently discussed for NSCLC 30 -of atezolizumab for firstline treatment of nonsquamous NSCLC (Figure 2k; Supplementary Figure 1) and found that IMpower150 now closely matched the expectation of independence (Hazard Ratio 1.04, P = 0.46, n = 356; Figure 3a, dotted line denotes adjusted hazard ratio).…”

“…Thus, our initial test of independence likely underestimated atezolizumab's single-agent activity. To address this we used clinically observed differences in atezolizumab activity by line of therapy 28 to construct a synthetic arm -as recently discussed for NSCLC 30 We are therefore left with two competing hypotheses about the four-drug combination tested in IMpower150: (a) atezolizumab is slightly less active at second-line than first-line, and independent action explains the benefit of adding atezolizumab to bevacizumab plus carboplatin plus paclitaxel, (b) atezolizumab is equally active at first and later lines of therapy and drug additivity or synergy is observed. Because atezolizumab is known to be more effective at the first-line of therapy in other NSCLC cohorts 28 we conclude that hypothesis (a) is more likely.…”

<p>Synthetic and External Controls in Clinical Trials – A Primer for Researchers</p>

“…For example, prior work utilizing Bayesian dynamic borrowing identified that when historic data with similar patient demographics were combined, uncertainty was reduced significantly per each additionally included trial external control data source. 25 In contrast, when incongruent clinical trial data were combined, no additional improvement to uncertainty was noted regardless of the number of trials of external control data added.…”

“…Even if pertinent RCTs are not highly identical, much strength can still be gained. 25 Where it is either unethical or infeasible to enrol patients to a control intervention, external data should be selected from the best possible source of data and synthetic controls can be used as a substitute for an absent control arm. In this setting (often a rare disease setting), pertinent RCTs are typically not available.…”