How Assessment-Schedule Matching Limits Bias When Comparing Progression-Free Survival in Single-Arm Studies: An Application in Second-Line Urothelial Carcinoma Treatments

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.

Increasing transparency in indirect treatment comparisons: is selecting effect modifiers the missing part of the puzzle? A review of methodological approaches and critical considerations

Failure to adjust for effect modifiers (EMs) in indirect treatment comparisons (ITCs) can produce biased and uncertain effect estimates. This is particularly important for health technology assessments (HTAs), where the availability of new treatments is based on comparative effectiveness results. Much emphasis has been placed on advancing ITC methods to adjust for EMs, yet whether EMs are appropriately identified for the conduct of ITCs in the first place is unclear. To understand the extent of guidance and requirements for the selection of EMs for ITCs currently available and if and how this guidance is applied in practice, a series of pragmatic reviews of guidance documents from HTA and non-payer organizations, primary published ITC analyses, and prior HTA submissions in two indications (non-small cell lung cancer and psoriasis) was conducted. The reviews showed that current ITC guidance mainly focused on developing analytical methods to adjust for EMs. Some organizations, such as HTA bodies in the UK, France and Germany, recommended the use of literature reviews, expert opinion and statistical methods to identify EMs. No detailed guidance on the selection process or the appropriate literature review approach was found. Similar trends were identified through the database search and review of prior HTA submissions; only few published ITCs and submissions included information on the EM selection process which was either based on findings from the literature, trial subgroup analyses, or clinical input. No reference to a systematic selection approach was found. There is an urgent need to fill the guidance gap identified across the reviews by including a step in ITC guidelines on how EMs should be identified through systematic reviews, formal expert elicitation, and a quantitative assessment of the EM distribution. Researchers and manufacturers are also encouraged to improve transparent reporting and justification of their selection of EMs to allow for an independent review of the set of factors being considered for adjustment. Both will contribute toward reducing bias in the ITC results and ultimately increase confidence in decision-making.