Quantitative bias analysis for external control arms using real-world data in clinical trials: a primer for clinical researchers

Development of medicines in rare oncologic patient populations are growing, but well-powered randomized controlled trials are typically extremely challenging or unethical to conduct in such settings. External control arms using real-world data are increasingly used to supplement clinical trial evidence where no or little control arm data exists. The construction of an external control arm should always aim to match the population, treatment settings and outcome measurements of the corresponding treatment arm. Yet, external real-world data is typically fraught with limitations including missing data, measurement error and the potential for unmeasured confounding given a nonrandomized comparison. Quantitative bias analysis (QBA) comprises a collection of approaches for modelling the magnitude of systematic errors in data which cannot be addressed with conventional statistical adjustment. Their applications can range from simple deterministic equations to complex hierarchical models. QBA applied to external control arm represent an opportunity for evaluating the validity of the corresponding comparative efficacy estimates. We provide a brief overview of available QBA approaches and explore their application in practice. Using a motivating example of a comparison between pralsetinib single-arm trial data versus pembrolizumab alone or combined with chemotherapy real-world data for RET fusion-positive advanced non-small cell lung cancer (aNSCLC) patients (1–2% among all NSCLC), we illustrate how QBA can be applied to external control arms. We illustrate how QBA is used to ascertain robustness of results despite a large proportion of missing data on baseline ECOG performance status and suspicion of unknown confounding. The robustness of findings is illustrated by showing that no meaningful change to the comparative effect was observed across several ‘tipping-point’ scenario analyses, and by showing that suspicion of unknown confounding was ruled out by use of E-values. Full R code is also provided.

Navigating the complexities of cancer research in rare cases is challenging. Real-world data comparison is a game-changer, but it comes with hurdles. Enter Quantitative Bias Analysis - a tool that helps us understand and correct errors in the data. Our article dives into this, using lung cancer as a case study. Ensuring the reliability of our findings, even when faced with missing information. Check it out for insights into advancing cancer research! #CancerResearch #RealWorldData #QuantitativeBiasAnalysis

Doctors and biomedical researchers are working hard to develop new medicines for rare types of cancer, but conducting traditional, strong clinical trials can be very difficult or even wrong in these cases. To help with this, researchers are increasingly using information from real-world data such as patient records to support their studies when there isn't much data available for comparison. This information is used to create a ‘control group’ that should be similar to the group receiving the new treatment. Such ‘control groups’ are necessary because practical and ethical challenges of randomly assigning ineffective standard of care to patients in clinical trials.

However, using real-world data comes with challenges like missing information, mistakes in measurements, and the possibility that there are other factors influencing the results that we didn't measure. Quantitative bias analysis (QBA) is a set of methods that helps us understand and measure the errors in the data that can't be fixed with regular statistical methods.

In this article, we talk about different ways to use QBA and show an example comparing a new treatment for a specific type of lung cancer with information from patients who received a different treatment. We use QBA to make sure our results are solid even when we don't have all the information we need about the patients at the start of the study. We share the computer code we used, so other researchers can learn from our approach.

So, this primer helps researchers understand and deal with the challenges of using real-world data in cancer studies, making sure their findings are reliable and helpful.