Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

Use of external control arms in immune-mediated inflammatory diseases: a systematic review

OBJECTIVES

External control arms (ECAs) provide useful comparisons in clinical trials when randomised control arms are limited or not feasible. We conducted a systematic review to summarise applications of ECAs in trials of immune-mediated inflammatory diseases (IMIDs).

DESIGN

Systematic review with an appraisal of ECA source quality rated across five domains (data collection, study populations, outcome definitions, reliability and comprehensiveness of the dataset, and other potential limitations) as high, low or unclear quality.

DATA SOURCES

Embase, Medline and Cochrane Central Register of Controlled Trial were searched through to 12 September 2023.

ELIGIBILITY CRITERIA

Eligible studies were single-arm or randomised controlled trials (RCTs) of inflammatory bowel disease, pouchitis, rheumatoid arthritis, juvenile idiopathic arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis and atopic dermatitis in which an ECA was used as the comparator.

DATA EXTRACTION AND SYNTHESIS

Two authors independently screened the search results in duplicate. The characteristics of included studies, external data source(s), outcomes and statistical methods were recorded, and the quality of the ECA data source was assessed by two independent authors.

RESULTS

Forty-three studies met the inclusion criteria (inflammatory bowel disease: 16, pouchitis: 1, rheumatoid arthritis: 12, juvenile idiopathic arthritis: 1, ankylosing spondylitis: 5, psoriasis: 3, multiple indications: 4). The majority of these trials were single-arm (33/43) and enrolled adult patients (34/43). All included studies used a historical control rather than a contemporaneous ECA. In RCTs, ECAs were most often derived from the placebo arm of another RCT (6/10). In single-arm trials, historical case series were the most common ECA source (19/33). Most studies (31/43) did not employ a statistical approach to generate the ECA from historical data.

CONCLUSIONS

Standardised ECA methodology and reporting conventions are lacking for IMIDs trials. The establishment of ECA reporting guidelines may enhance the rigour and transparency of future research.