The assessment for current response and progression endpoints, such as objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and time-to-progression (TTP), has to be based on investigators’ professional knowledge and experience. Namely, their assessment could be influenced by subjective factors, including failure to diagnose new lesions, variability during tumor measurement, target-lesion selection, and different interpretations on non-target or immeasurable lesions (1). In addition, the knowledge of investigators regarding treatment assignment would influence their assessment as well, especially in trials with open-labelled design (2). The above subjective factors may impact the assessment for trial endpoints, and subsequently the expected outcome will over- or underestimate the true effect of the treatments from experimental arm (exp) to control arm (con), possibly causing systematic bias (3). Therefore, blinded independent central review has been increasingly implemented in recent phase III oncological randomized control trials (RCTs). During implementation, all imaging examinations are acquired as part of protocol and reviewed by independent physicians who are blinded to treatment assignments and various information of patients (4), in order to detect and control potential bias from local investigators.
In current stage, however, systematic bias between central and local assessments in phase III RCTs is out of evidence from studies. In 2011, Amit et al. conducted the first study on systematic bias according to 27 phase III RCTs (5). This study compared the treatment effects of PFS between central and local assessments through meta-analysis, and found no systematic bias. No evidence of systematic bias has been further verified by two subsequent meta-analyses based on 28 and 61 RCTs, respectively (6,7).
Nevertheless, no evidence of systematic bias at the level of treatment effects, does not mean the evaluation concordance between central and local assessments when directly comparing response status. Further, the reliability of local assessment, as well as unnecessary implementation of central assessment could not be concluded only based on no evidence of systematic bias. For example, when comparing with central reviewers, if in experimental arm the local investigators overestimated the endpoints but in control arm they did not overestimate or even underestimated the endpoints, evaluation bias would possibly occur, even there might be no systematic bias when comparing the treatment effects between central and local assessments in the same study. Based on this assumption, central review is still valuable in clinical trials.
In order to verify the value of central assessment to local assessment, in this literature review and analyses, we investigated response status of ORR and DCR between central and local assessments among recently-published phase III RCTs on all non-hematologic solid tumors.
Search strategy and study selection
In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement (8), a PubMed search was conducted by JRZ using the dates of Jan 1, 2010 to Jun 30, 2017. The retrieval formula was: (“neoplasms”(MeSH Terms) OR “neoplasms”(All Fields) OR “cancer”(All Fields)) AND random* AND (“Phase 3” OR “Phase III”) (English) (Human) (Clinical Trial, Phase III). Articles of inappropriate publication types were excluded, including reviews, systematic reviews and/or meta-analyses, guidelines, and commentaries.
Eligible articles were regarding the therapeutic efficacy of anti-cancer agents in phase III RCTs for patients with non-hematologic solid tumors. In these articles, imaging assessment for ORR and/or DCR was conducted by both central reviewers and local investigators. As some authors reported their data in more than one article, we used the name and/or clinicaltrials.gov identifier (NCT number) of eligible RCTs as search terms respectively to re-search PubMed (without the time interval limitation), to find out if there were more available articles of those trials. Endnote X7 (Thomson Reuters, New York, USA) was used in above process.
The process of data extraction was carried out independently and double-blindly by three reviewers working in pairs (Jianrong Zhang respectively blind to Yiyin Zhang and Shiyan Tang; in blocks of 17 articles allocated at random; discrepancies resolved by Wenhua Liang). To ensure consistency between reviewers, we used a same data extraction form, piloted the data extraction by using a sample of 16 included trials, and had discussion before and during the extraction process to confer how to properly extract and interpret the data.
Following characteristics of each trial were extracted: author, year, NCT number, mask (open/blind), sample size, tumor type, primary endpoint (central-assessed/local-assessed/other), and primary endpoint outcome (positive/negative/indeterminate). We also extracted ORR and DCR from both central and local assessments.
First, we directly compared response status between two assessments in both experimental and control arms by pooled analysis with Mantel-Haenszel method. The measurement was odds ratio (OR), defined as the ratio of central-assessed response status to local-assessed response status: ratio greater than 1 indicated central reviewers overestimated response status compared with local assessment; significant discrepancy between two assessments was shown if P<0.05. Second, we investigated evaluation bias between central and local assessments through pooled analysis with Inverse Variance method. In this procedure, above calculated ORs between experimental and control arms were compared, and the ratio of OR was the measure: regardless higher or lower than 1, P<0.05 indicated significant evaluation bias. During above two procedures, we also made subgroup analysis based on trial characteristics: mask, sample size (based on median value of all included trials), tumor type, and primary endpoint with its outcome. All mentioned procedures were conducted in Review Manager 5.3 (The Cochrane Collaboration, London, England), with initially fixed-effect model. If the corresponding p value for heterogeneity was less than 0.05 or the I2 index (I2) was over 50%, we used random-effect model, in order to reduce the heterogeneity effect.
In order to investigate the concordance between two assessments, we conducted correlation analysis by using SPSS Version 23 (SPSS Software, Chicago, USA). The test for normality was completed first, followed by correlation analysis if normal distribution was indicated, we estimated the correlation by the Pearson correlation coefficient; if not, Spearman correlation was applied. Significant correlation was indicated when p value was less than 0.05.
Trial searching and characteristics
Direct comparison of response status between central and local assessments
Pooled analysis presented lower response frequency of ORR and DCR in central assessment compared with local assessment (Table 2, Figures 2 and 3), regardless in experimental [ORR: OR=0.81 (95% CI: 0.76–0.87), P<0.01, I2=11%; DCR: OR=0.90 (0.81–1.01), P=0.07, I2=42%] or control arm [ORR: OR=0.79 (95% CI: 0.72–0.85), P<0.01, I2=17%; DCR: OR=0.94 (0.86–1.02), P=0.14, I2=12%]. During above comparison, there was no significant interaction effect of therapy allocation (experimental arm versus control arm) in both ORR (P=0.56, I2=0%) and DCR (P=0.42, I2=0%).
In subgroup analysis (Table 2), the discrepancy between two assessments in trials with open-label design, positive primary outcome, and central assessed primary endpoints, was larger than the discrepancy in trials with blind design, negative primary outcome, and local-assessed primary endpoints, respectively. Correspondingly significant interaction effect was only found in con-ORR (P=0.04, I2=75%) between the mask pattern (open versus blind).
Evaluation bias between central and local assessments
No evidence of evaluation bias between central and local assessments was indicated by pooled analysis. Of ORR, the ratio of OR was 1.02 (0.97–1.07) (P=0.42); of DCR, the ratio of OR was 0.98 (0.93–1.03) (P=0.37). Subgroup analysis further verified no sign of evaluation bias, including the mask pattern and primary endpoint outcomes (Table 3).
Concordance between central and local assessments
Correlation analysis presented high-degree concordance between two assessments. The outcome (r) was 0.985 (P<0.01), 0.962 (P<0.01), 0.962 (P<0.01) and 0.926 (P<0.01) of exp-ORR, con-ORR, exp-DCR and con-DCR, respectively (Figure 4).
As we acknowledge, this is the largest study to investigate response status between central and local assessments in recent 28 phase III oncological clinical trials on different advanced solid tumors, and it is the first study involving DCR for this topic. Based on pooled analysis, we found even though local assessment estimated higher treatment efficacy than the efficacy of central assessment, this phenomenon existed in both experimental and control arms. In other words, both response statuses of central and local assessments between two arms were concordant. This was verified by correlation analysis. More importantly, there was no sign of evaluation bias between two assessments after further pooled analysis.
Comparing with two previous meta-analyses, first, our study further confirms their results. In Lima et al. study based on 13 RCTs on metastatic colorectal cancer (44), local assessment had higher ORR than central assessment; this higher-estimated finding was not just in experimental arm [OR=1.16 (1.09–1.22), P<0.001], but also in control arm [OR=1.16 (1.09–1.25), P<0.001]. Parallel with our research, there was no significant interaction effect between therapy allocation (P=0.81), and also no evidence of evaluation bias by their further analysis [ratio of OR=0.97 (0.90–1.04), P=0.35]. According to the results, Lima et al. concluded that, the need of complete-case central assessment should be reappraised (44). In another meta-analysis based on 21 trials with different tumors, Tang et al. investigated the variability according to the difference of ORR and median PFS between central and local assessments (18 trials with ORR, 8 trials with PFS) (45). Comparing with central reviewers, local investigators overestimated ORR [estimated mean difference =4.57% (2.95–6.19%)], but did not overestimate median PFS [estimated mean difference =−0.19 (−0.68 to 0.29) months]. No evaluation bias was indicated by further analysis regardless of ORR (P=0.54) or PFS (P=0.31). Tang et al. concluded, due to the variability between central and local assessments, central review should be considered when the primary endpoint is based on response or progression assessment in oncological clinical trials (45).
The difference between our research and the above two studies is, we included larger RCTs which are recently published from 2010 to 2017, and involved DCR for analysis. Additionally, we found lower treatment response of central assessments especially in trials with open-label design, central-assessed primary endpoint, and positive primary endpoint outcome. Namely, assessment by central reviewers seemed more “conservative” in open-label studies, or in trials whose primary endpoint was based on central assessment. However, no evaluation bias could be found regardless of summary synthesis or subgroup analysis, including above-mentioned subgroup circumstances of open-labelled versus blind design, central-assessed primary endpoint versus local-assessed primary endpoint, and positive primary endpoint outcome versus negative primary endpoint outcome.
According to no evidence of systematic bias (5,6), as well as high-degree concordance without evaluation bias between central and local assessment in previous and our meta-analysis, we consider, the implementation of central assessment for all enrolled patients is unnecessary in clinical trials. Instead, we are looking forward to understanding the usage of sample-based central review as an audit strategy in future trials (1,2,5,6,46). Its value deserves further investigation.
Our research has several limitations. First, the finding of our research may be not completely generalizable to all phase III clinical trials on advanced solid tumors, in that our included trials were implemented with both assessments’ response endpoints. Accordingly, we are unclear for the reliability of either assessment in trials which were only implemented or reported according to the result of one assessment. Second, even though we included trials with different solid tumors, heterogeneity analysis indicated, trials with different tumor types could have inconsistent findings when directly comparing DCR between two assessments. For example, in trials on non-small cell lung cancer, breast cancer and renal-cell carcinoma, central assessment underestimated treatment benefit on DCR compared with local assessment, but in trials on ovarian cancer, central assessment did not underestimate. However, regardless of above higher or lower treatment benefit, the OR of trials on different tumors represented same direction in both experimental and control arms. Given further analysis on evaluation bias, as well as following correlation analysis, these findings represented evaluation concordance between both assessments. Third, this meta-analysis was conducted based on study-level analysis, instead of individual-level analysis.
In conclusion, according to the finding that local assessment estimated higher treatment efficacy than the efficacy of central assessment in our direct comparison, we believe blind independent central review remains an irreplaceable method to monitor local assessment. However, we don’t believe its implementation for all patients is necessary in all trials, due to no evaluation bias between central and local assessments, as well as their high-degree evaluation concordance.
This article is the second academic output of the Design, Implementation & Report of Oncological Randomized Controlled Trials (DIRORCT) Research Project, which aims to investigate the quality of RCTs from study design, implementation to final report through systematic review and/or further analysis. We thank Ms. Xiaoru Deng (Guangzhou Medical University, Guangzhou, China) for advising the process of article searching, Ms. Elizabeth Burke (Washington University in St. Louis, St. Louis, USA) and Dr. Jieyu Wu (Karolinska Institutet, Stockholm, Sweden) for providing suggestions on manuscript revision. We also sincerely thank all authors, investigators, sponsors and patients for their effort and participation in our included studies.
Conflicts of Interest: Some data of this research has been presented at ESMO 2016 Annual Meeting.
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