Differential frequency in imaging-based outcome measurement: Bias in real-world oncology comparative-effectiveness studies

Core Concepts in Pharmacoepidemiology: Time-To-Event Analysis Approaches in Pharmacoepidemiology

AIM

This article provides an overview of time-to-event (TTE) analysis in pharmacoepidemiology.

MATERIALS & METHODS

The key concept of censoring is reviewed, including right-, left-, interval- and informative censoring. Simple descriptive statistics are explained, including the nonparametric estimation of the TTE distribution as per Kaplan-Meier method, as well as more complex TTE regression approaches, including the parametric Accelerated Failure Time (AFT) model and the semi-parametric Cox Proportional Hazards and Restricted Mean Survival Time (RMST) models. Additional approaches and various TTE model extensions are presented as well. Finally, causal inference for TTE outcomes is addressed.

RESULTS

A thorough review of the available concepts and methods outlines the immense variety of available and useful TTE models.

DISCUSSION

There may be underused TTE concepts and methods, which are highlighted to raise awareness for researchers who aim to apply the most appropriate TTE approach for their study.

CONCLUSION

This paper constitutes a modern summary of TTE analysis concepts and methods. A curated list of references is provided.

Time-to-Event Endpoints in Imaging Biomarker Studies

Time-to-event endpoints are widely used as measures of patients' well-being and indicators of prognosis. In imaging-based biomarker studies, there are increasingly more studies that focus on examining imaging biomarkers' prognostic or predictive utilities on those endpoints, whether in a trial or an observational study setting. In this educational review article, we briefly introduce some basic concepts of time-to-event endpoints and point out potential pitfalls in the context of imaging biomarker research in hope of improving radiologists' understanding of related subjects. Besides, we have included some review and discussions on the benefits of using time-to-event endpoints and considerations on selecting overall survival or progression-free survival for primary analysis. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

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.

Real-World Progression-Free Survival as an Endpoint in Lung Cancer: Replicating Atezolizumab and Docetaxel Arms of the OAK Trial Using Real-World Data

Evaluating cancer treatments in real-world data (RWD) requires informative endpoints. This study replicated the atezolizumab and docetaxel arms of the OAK trial using RWD and compared progression-free survival (PFS) outcomes derived from abstracted physician's notes in RWD (rwPFS) against PFS outcomes derived from the clinical trial PFS (ctPFS). Atezolizumab and docetaxel arms of the phase III OAK randomized controlled trial (RCT; NCT02008227) were replicated in a US nationwide real-world database using selected OAK inclusion/exclusion criteria and propensity score-based adjustment for baseline prognostic variables. Concordance of outcomes was assessed using Kaplan-Meier medians and hazard ratios (HRs). The RWD cohorts comprised 133 patients on atezolizumab and 479 patients on docetaxel. After adjustment, prognostic variables were balanced between RCT arms and corresponding RWD cohorts. The rwPFS and ctPFS outcomes showed better concordance for docetaxel (2.99 vs. 3.52 months; HR: 0.99, 95% confidence interval (CI): 0.85-1.15) than for atezolizumab (3.71 vs. 2.76 months; HR: 0.8, 95% CI: 0.61-1.02). Excluding events labeled "pseudo-progression" from both RWD and RCT improved concordance for atezolizumab (4.24 vs. 4.14 months; HR: 0.95, 95% CI: 0.70-1.25). These findings were robust across sensitivity analyses. Replicating RCTs using RWD and comparing outcomes can help characterize RWD endpoints. Similarity of results between rwPFS and ctPFS at the cohort level may depend on drug category, highlighting the need for further studies to verify and understand when the corresponding outcomes can be compared, including within the same patient.

Overview of approaches to estimate real-world disease progression in lung cancer

BACKGROUND

Randomized clinical trials of novel treatments for solid tumors normally measure disease progression using the Response Evaluation Criteria in Solid Tumors. However, novel, scalable approaches to estimate disease progression using real-world data are needed to advance cancer outcomes research. The purpose of this narrative review is to summarize examples from the existing literature on approaches to estimate real-world disease progression and their relative strengths and limitations, using lung cancer as a case study.

METHODS

A narrative literature review was conducted in PubMed to identify articles that used approaches to estimate real-world disease progression in lung cancer patients. Data abstracted included data source, approach used to estimate real-world progression, and comparison to a selected gold standard (if applicable).

RESULTS

A total of 40 articles were identified from 2008 to 2022. Five approaches to estimate real-world disease progression were identified including manual abstraction of medical records, natural language processing of clinical notes and/or radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. The accuracy of these progression approaches were assessed using different methods, including correlations between real-world endpoints and overall survival for manual abstraction (Spearman rank ρ = 0.61-0.84) and area under the curve for natural language processing approaches (area under the curve = 0.86-0.96).

CONCLUSIONS

Real-world disease progression has been measured in several observational studies of lung cancer. However, comparing the accuracy of methods across studies is challenging, in part, because of the lack of a gold standard and the different methods used to evaluate accuracy. Concerted efforts are needed to define a gold standard and quality metrics for real-world data.

Replication of Overall Survival, Progression-Free Survival, and Overall Response in Chemotherapy Arms of Non-Small Cell Lung Cancer Trials Using Real-World Data

PURPOSE

The utility of real-world data (RWD) for use as external controls in drug development is informed by studies that replicate trial control arms for different endpoints. The purpose of this study was to replicate control arms from four non-small cell lung cancer (NSCLC) randomized controlled trials (RCT) to analyze overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) using RWD.

PATIENTS AND METHODS

This study used RWD from a nationwide de-identified database and a clinico-genomic database to replicate OS, PFS, and ORR endpoints in the chemotherapy control arms of four first-line NSCLC RCTs evaluating atezolizumab [IMpower150-wild-type (WT), IMpower130-WT, IMpower131, and IMpower132]. Additional objectives were to develop a definition of real-world PFS (rwPFS) and to evaluate the real-world response rate (rwRR) endpoint.

RESULTS

Baseline demographic and clinical characteristics were balanced after application of propensity score weighting methods. For rwPFS and OS, RWD external controls were generally similar to their RCT control counterparts. Across all four trials, the hazard ratio (HR) point estimates comparing trial controls with external controls were closer to 1.0 for the PFS endpoint than for the OS endpoint. An exploratory assessment of rwRR in RWD revealed a slight but nonsignificant overestimation of RCT ORR, which was unconfounded by baseline characteristics.

CONCLUSIONS

RWD can be used to reasonably replicate the OS and PFS of chemotherapy control arms of first-line NSCLC RCTs. Additional studies can provide greater insight into the utility of RWD in drug development.

Bridging the divide between clinical research and clinical care in oncology: An integrated real-world evidence generation platform

Real world data (RWD) are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources; real-world evidence (RWE) generated by RWD analyses can become an important component of drug development programs and, potentially, regulatory decision-making. As a RWD source, electronic health records (EHRs) can now provide patient-level data at unparalleled depth and granularity. We propose a RWE generation framework that could maximize the synergy between RWD and prospective clinical trials by capitalizing on an emerging data curation infrastructure that may be applied to both retrospective and prospective research. In this platform, centralized data collection and monitoring could be enabled via routine EHR use, and seamlessly integrated with select intentional data capture during prospective study periods. By bridging the divide between routine care and clinical research, this integrated platform aggregates retrospective and prospective data, collected both routinely and intentionally. This approach makes clinical trial participation more available to patients, increasing the potential depth of data, representativeness and efficiency of clinical research.