Appropriate statistical methods are available to handle biases encountered in blinded, independent, central review (BICR) determined progression-free survival

While overall survival (OS) has traditionally been the standard evaluation for a new treatment in oncology since it is easily obtained and unambiguous, the endpoint of progression-free survival (PFS) is appealing due to the shortened observation time required to determine treatment efficacy, smaller sample size requirements and no confounding due to subsequent treatments. For many cancers, PFS has been demonstrated to be a valid measure of surrogacy for OS and an acceptable trial endpoint from regulatory agencies (1). FDA approval for many therapies including sorafenib for renal cell carcinoma, gemcitabine for ovarian cancer, and rituximab for non-Hodgkin’s lymphoma were based on PFS (1-4). However, PFS can be associated with measurement error and bias. Radiologic scans are the primary mode of assessment for solid tumors, and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria were developed to define changes to a scan that constitute a progression (5). Because of the potential for discrepancy in interpretation of RECIST criteria across radiologists, a process of blinded, independent, central review (BICR) was established in order to attempt to provide reliable and unbiased PFS assessments (6). Furthermore, BICRs are generally recommended for clinical trials submitted to the FDA for regulatory consideration(7).

Intended to mitigate assessment variability among local site evaluations, BICR-based analyses potentially introduce other biases, resulting from varying evaluation times or differential attrition rates between study arms, interval censoring, and informative censoring (8-11). BICR is typically performed retrospectively for the purposes of quality control across radiologic assessments rather than for individual treatment decisions (12). Typically, if a patient is deemed to have progressed by local evaluation, this triggers a sequence of events: the patient is off treatment, off protocol, and will not undergo additional scans. If the BICR cannot confirm the locally determined progression, the FDA has recommended that these cases be censored at the time of local progression for the BICR analysis of PFS (7). This then violates the independent censoring assumption required for standard survival analyses since patients in the BICR analysis are considered lost to follow-up for reasons related to the study and are then not representative of all censored observations (13,14). In the recent article entitled “Exaggeration of PFS by blinded, independent, central review (BICR)”, Stone et al. addressed the impact of this informative censoring on Kaplan-Meier (KM) estimates of median progression-free survival (15).

Stone et al. present a simulation study considering various scenarios of true progression times and correlations in timing between local and BICR identification of progression under the scenario of a 12-week radiographic imaging assessment schedule. The authors demonstrate scenarios under which KM estimates of median PFS are both under- and over-estimated due to informative censoring and conclude that KM estimates of median survival are biased even under the scenario in which local and BICR PFS times are identical (i.e., local review is sufficiently standardized). Given the interval censoring inherent in studies of PFS, the bias resulting from informative censoring in BICR analyses should instead be assessed utilizing appropriate analytic methods that take this into account, such as the nonparametric extension of the KM estimator (11,16-18). Since the traditional KM estimate of median PFS ignores interval censoring, the observed bias cannot be attributed entirely to informative censoring.

In general, methods that appropriately account for interval censoring should be the standard analytic approach for analyses of PFS (19). In studies of PFS that utilize BICR, the local estimate of PFS may be biased due to lack of standardization across radiologic reviewers and the BICR estimate of PFS may be biased due to informative censoring. We recommend that estimates from both analyses be presented and sensitivity analyses should be conducted in the BICR analyses to assess the potential impact of interval censoring. As indicated by Stone et al., potential analyses include inverse probability weighting and multiple imputation (20,21). An additional option is to consider sensitivity analyses at the extremes, where patients censored due to local progression can be considered to progress at the time of local progression or after all other patients in the sample. Though extreme, this provides estimates of the range of potential impact (22). In the past, implementing these more complex statistical methods may have been challenging due to lack of available software, but in recent years numerous resources have been developed to implement these analyses (23).

While Stone et al. demonstrated bias in KM estimates of the median survival, phase III trials are generally intended to assess treatment efficacy, often via a hazard ratio (HR) comparing PFS in the treatment arm to the control arm. Prior meta-analyses as well as the case study within the article by Stone et al. have demonstrated that the estimate of HR and it’s 95% confidence interval are consistent for analyses based on BICR and local review (24-26). Though the estimate of treatment efficacy is unbiased, the biased KM median PFS is often additionally reported. Thus, the above recommendations are pertinent only when median survival is reported.

Beyond modifications to the statistical method employed, a potential remedy that has been proposed is to move from retrospective BICR to real-time BICR. However, this presents a costly and logistically challenging solution that is potentially unnecessary after appropriately accounting for the interval and informative censoring (8).

In an era of increased awareness of the importance of reproducibility, we recommend that studies of PFS that utilize BICR implement rigorous analytic approaches and present sensitivity analyses when informative censoring mechanisms are potentially violated.

Acknowledgments

Funding: Authors acknowledge Memorial Sloan Kettering Cancer Center Support Grant/Core Grants (P30 CA008748).

Footnote

Provenance and Peer Review: This article was commissioned and reviewed by the Section Editor Jianrong Zhang (MPH Candidate, George Warren Brown School; Graduate Policy Scholar, Clark-Fox Policy Institute, Washington University in St. Louis, St. Louis, USA).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jhmhp.2019.04.01). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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