3030 Background: To determine whether changes in circulating tumor DNA (ctDNA) levels reflect treatment outcome, Friends of Cancer Research created the ctDNA to Monitor Treatment Response (ctMoniTR) Project with collaborators from industry, government, academia, and advocacy. A prior ctMoniTR effort analyzing 5 clinical trials (CT) showed an association between decreases in ctDNA levels and improved outcomes in patients with advanced non-small cell lung cancer (aNSCLC) treated with an anti-PD-(L)1. The current study expands that work and focuses on CT investigating tyrosine kinase inhibitors (TKI) treatment in oncogene-driven aNSCLC. Methods: We performed a retrospective analysis of patient-level clinical and ctDNA data across 8 CT representing 1,015 patients with aNSCLC treated with TKI (i.e., anti-EGFR, ALK, RET, or MET). Patients included in the analysis had a baseline ctDNA measurement (T0), an on treatment ctDNA measurement within 10 weeks of treatment initiation (for those with multiple ctDNA measurements within 10 weeks, we used the lowest measurement within 10 weeks) (T1), and overall survival (OS) data (n=749). CT used different ctDNA collection timepoints and assays. We randomly divided the dataset into training (2/3 of the data) and validation (1/3 of the data) datasets stratified by CT cohort (i.e., arm), age, tumor stage, and prior lines of therapy, then ran initial analyses on the training dataset (n=501; reported herein). ctDNA change was calculated as the percent change in maximum variant allele frequency (VAF) between T0 and T1 using tumor-derived variants provided by sponsors for each unique patient sample. CT used either ddPCR or an NGS assay. ctDNA limits of detection were assay specific and varied across CT. Multivariate analyses are ongoing and validation dataset analyses will be conducted. Results: At T0, 141 patients had non-detected (ND) ctDNA and 360 patients had detected (D) ctDNA. Of these, 27% (n=136) had ND ctDNA at both T0 and T1 (“ND/ND”), 52% (n=260) had changes from D at T0 to ND at T1 (“D/ND”), 12% (n=60) had at least a 50% decrease from T0 to T1 (“decrease”) and 9% (n=45) had an increase or a less pronounced decrease in ctDNA. In a univariate analysis, patients with ND/ND and D/ND were associated with improved OS compared to the decrease group. In addition to other characteristics, patients with max VAF ≤0.5% or ND at T0 (n=214, 43%) had improved OS (HR=0.44, P<0.001) compared to those with max VAF >0.5% at T0 (n=287, 57%). Conclusions: In a retrospective aggregate analysis of 8 CT, ND ctDNA at T1 was associated with improved OS in patients with aNSCLC treated with TKI. Changes in ctDNA levels, particularly from D to ND, may provide an early indication of treatment benefit and predict long-term outcomes in this population. Additional ctMoniTR analyses are ongoing to validate the potential use of ctDNA as an early endpoint.

BACKGROUND:In response to the COVID-19 global pandemic, multiple platform trials were initiated to accelerate evidence generation of potential therapeutic interventions. Given a rapidly evolving and dynamic pandemic, platform trials have a key advantage over traditional randomized trials: multiple interventions can be investigated under a master protocol sharing a common infrastructure. METHODS:This paper focuses on nine platform trials that were instrumental in advancing care in COVID-19 in the hospital and community setting. A semi-structured qualitative interview was conducted with the principal investigators and lead statisticians of these trials. Information from the interviews and public sources were tabulated and summarized across trials, and recommendations for best practice for the next health crisis are provided. RESULTS:Based on the information gathered takeaways were identified as 1) the existence of some aspect of trial design or conduct (e.g., existing network of investigators or colleagues, infrastructure for data capture and relevant statistical expertise) was a key success factor; 2) the choice of treatments (e.g., repurposed drugs) had major impact on the trials as did the choice of primary endpoint; and 3) the lack of coordination across trials was flagged as an area for improvement. CONCLUSION:These trials deployed during the COVID-19 pandemic demonstrate how to achieve both speed and quality of evidence generation regarding clinical benefit (or not) of existing therapies to treat new pathogens in a pandemic setting. As a group, these trials identified treatments that worked, and many that did not, in a matter of months.

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID-19, based on quantitative modeling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally overestimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender, and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, (1) large RCTs, especially platform studies, be deployed early; (2) any RWS should be large and should have adequate matching of known confounders and long follow-up; (3) reporting standards and data standards for primary endpoints, explanatory factors, and key subgroups should be improved; in addition, (4) appropriate incentives should be in place to enable access to patient-level data; and (5) an overall aggregate view of all available results should be available at any given time.

The COVID-19 pandemic presents unprecedented challenges for drug developers seeking to evaluate the safety and efficacy of potential treatments for COVID-19. Clinical researchers must work quickly and adapt to emerging data. Building upon the FDA guidance document and Duke-Margolis' critical path to rapid development and access to safe and effective COVID-19 therapeutics, this article focuses on statistical opportunities for nimble and accelerated development for COVID-19 therapeutics. We focus on acceleration opportunities by way of increasing clinical trial efficiency, facilitating robust collection of key clinical outcomes, and enabling quantitative decision making to bring safe and effective therapeutics to the market. We present adaptive elements of trial designs which allow trials to evolve as new information emerges and discuss participation in master protocols to optimize use of resources, ensure scientific rigor and enhance interpretation of study results. Key clinical outcomes of importance to public health and core data elements are proposed to facilitate broader data sharing and robust decision making. Available statistical methods and efficient approaches to rapidly generate and synthesize meaningful evidence are presented contributing to quantitative decision making on the effectiveness and safety of COVID-19 therapeutics. In this article, we share innovative trial designs, clinical outcome and data standards, and existing statistical methods to accelerate the development of COVID-19 therapeutics and recommend that they should be applied immediately in a harmonized fashion.

PURPOSE As immune checkpoint inhibitors (ICI) become increasingly used in frontline settings, identifying early indicators of response is needed. Recent studies suggest a role for circulating tumor DNA (ctDNA) in monitoring response to ICI, but uncertainty exists in the generalizability of these studies. Here, the role of ctDNA for monitoring response to ICI is assessed through a standardized approach by assessing clinical trial data from five independent studies. PATIENTS AND METHODS Patient-level clinical and ctDNA data were pooled and harmonized from 200 patients across five independent clinical trials investigating the treatment of patients with non–small-cell lung cancer with programmed cell death-1 (PD-1)/programmed death ligand-1 (PD-L1)–directed monotherapy or in combination with chemotherapy. CtDNA levels were measured using different ctDNA assays across the studies. Maximum variant allele frequencies were calculated using all somatic tumor-derived variants in each unique patient sample to correlate ctDNA changes with overall survival (OS) and progression-free survival (PFS). RESULTS We observed strong associations between reductions in ctDNA levels from on-treatment liquid biopsies with improved OS (OS; hazard ratio, 2.28; 95% CI, 1.62 to 3.20; P < .001) and PFS (PFS; hazard ratio 1.76; 95% CI, 1.31 to 2.36; P < .001). Changes in the maximum variant allele frequencies ctDNA values showed strong association across different outcomes. CONCLUSION In this pooled analysis of five independent clinical trials, consistent and robust associations between reductions in ctDNA and outcomes were found across multiple end points assessed in patients with non–small-cell lung cancer treated with an ICI. Additional tumor types, stages, and drug classes should be included in future analyses to further validate this. CtDNA may serve as an important tool in clinical development and an early indicator of treatment benefit.

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Data-driven digital health technologies have the power to transform health care. If these tools could be sustainably delivered at scale, they might have the potential to provide everyone, everywhere, with equitable access to expert-level care, narrowing the global health and wellbeing gap. Conversely, it is highly possible that these transformative technologies could exacerbate existing health-care inequalities instead. In this Viewpoint, we describe the problem of health data poverty: the inability for individuals, groups, or populations to benefit from a discovery or innovation due to a scarcity of data that are adequately representative. We assert that health data poverty is a threat to global health that could prevent the benefits of data-driven digital health technologies from being more widely realised and might even lead to them causing harm. We argue that the time to act is now to avoid creating a digital health divide that exacerbates existing health-care inequalities and to ensure that no one is left behind in the digital era.

Every medical product requires additional study even after regulatory approval. We highlight several lines of enquiry to advance our understanding of COVID19 vaccines post authorization: identifying key population segments warranting more study, assessment of efficacy, and of safety data, harmonization of data relating to immune response and developing mechanisms for data and knowledge sharing across countries. We show how innovative trial designs and sources from real world data play a critical role in generating evidence.

Abstract Dose ranging trials are used frequently in drug development to learn about dose response for both desirable and undesirable effects and to provide sponsors with confidence that investment in confirmatory trials for market access is likely to be successful. The design of crossover studies and analysis of data that develops from such crossover trials are discussed.

The International Conference on Harmonisation E14 (2004) calls for public comment by statisticians on the practical implications of the proposed guidance to monitor for QTc prolongation. Of particular interest is consideration of the statistical properties of various end points proposed for measurement. Methods of analysis and statistical inference are developed for the QTc end points considered.

Objective: To demonstrate average bioequivalence, the ninety-percent confidence intervals (CI) on the ratio of geometric means for area under the concentration–time curve (AUC) and maximum observed plasma concentration (Cmax) must lie within 0.80–1.25. Demonstration of average bioequivalence (ABE) for highly variable drug products requires large numbers of subjects in a standard, adequately powered, two-period crossover. Methods: Application of non-traditional study designs can help to meet this hurdle. Study design and analysis for replicate and group sequential–replicate study designs are presented and illustrated using examples. It is demonstrated how to use such approaches to meet the difficult regulatory hurdle of average bioequivalence for a highly variable drug product. Results: To illustrate, data are provided from three separate ABE studies for a highly variable drug product at three dosage strengths. In all three studies, a replicate study design was used to compensate for high intrasubject variation. Additionally, for the last study, a group sequential study design was imposed to provide early evidence of conclusive results. Conclusion: Replicate designs and group-sequential designs in bioequivalence should be used to demonstrate average bioequivalence for highly variable drug products or when uncertain of true intrasubject variability in order to ensure conclusive study results.