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Short Report
24 May 2024

Comparing prospectively assigned trial and real-world lung cancer patients

Authors: Brigham Walker https://orcid.org/0000-0002-5617-577X [email protected], Herman E Ray https://orcid.org/0000-0003-3444-098X, Ping Shao https://orcid.org/0000-0001-6052-7718, Claudio D'Ambrosio, Craig White, and Mark S Walker https://orcid.org/0000-0002-3276-2689Author Info & Affiliations
Volume 13, Number 7
https://doi.org/10.57264/cer-2023-0176
PDFFormats

Abstract

Aim: To evaluate the comparability of a probable clinical trial (CT) cohort derived from electronic medical records (EMR) data with a real-world cohort treated with the same therapy and identified using the same inclusion and exclusion criteria to emulate an external control. Methods: We utilized de-identified patient-level structured data sourced from EMRs. We then compared patterns of overall survival (OS) between probable CT patients with those drawn from non-contemporaneous real-world data (RWD) using a two-sided log-rank test, hazard ratios (HRs) using a Cox proportional-hazards model and Kaplan–Meier (KM) survival curves. Each regression estimate was calculated with a corresponding 95% confidence interval. We additionally conducted multiple matching methods to assess their relative performance. Results: Median (standard deviation) OS was 10.2 (0.7) months for the RWD arm and 11.3 (1.3) for the probable CT arm with a Log rank p-value equal to 0.4771. OS in both cohorts is longer than the reported CT median OS of 9.2 (0.6). The HRs generated under all five assessed matching methods (including without adjustment) were not statistically significant at the 95% confidence level. Conclusion: Our results suggest, with caveats noted, that survival patterns between real-world and CT cohorts in this NSCLC setting are not statistically significantly different.

Shareable abstract

Researchers found that survival patterns between probable randomized controlled trial and non-contemporaneous real-world non-small-cell lung cancer patients were not statistically significantly different. This finding, and its caveats, may inform emerging drug evaluation methods.

Plain language summary

Comparing trial & real-world lung cancer patients

What is this article about?

Randomized controlled trials are used to evaluate whether a drug is effective by randomly assigning patients to receive that drug or a control, thereby limiting explanations other than the drug for differences in outcomes. However, due to a variety of issues including a limited number of patients available for a trial, some drugs are approved without a control group which raises questions like “how would the patient have fared without treatment or with other drugs?” One solution is to utilize data from patients who have already been treated to construct an external or synthetic control group. This may improve upon a non-controlled study but requires more evidence.

What were the results?

We utilized data from electronic medical records and compared survival patterns between patients treated with a drug prior to its regulatory approval (i.e., the probable clinical trial patients) to those treated with the same drug after its approval (i.e., the real-world patients). Since these patients were not randomly assigned, they differed in potentially important ways such as age or smoking habits. So, we explored different methods to account for these differences. We find that the survival patterns are not statistically significantly different between the groups, but we also highlight important limitations concerning sample size that may inform the suitability of these comparisons in other settings.

What do the results mean?

Survival patterns between real-world and clinical trial cohorts in this setting are not statistically significantly different, suggesting a potentially appropriate role for external controls.

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