Assessment of Progression-Free Survival as a Surrogate End Point of Overall Survival in First-Line Treatment of Ovarian Cancer: A Systematic Review and Meta-analysis | Cancer Biomarkers | JAMA Network Open | JAMA Network
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Figure 1.  Overall and Trial by Trial Treatment Effect on Overall Survival (OS) and Progression-Free Survival (PFS)
Overall and Trial by Trial Treatment Effect on Overall Survival (OS) and Progression-Free Survival (PFS)

HR indicates hazard ratio. The size of the squares is proportional to the sample size of the trial.

Figure 2.  Association Between the Hazard Ratio (HR) for the Surrogate End Point Progression-Free Survival (PFS) and for the True End Point Overall Survival (OS) by Type of Trial
Association Between the Hazard Ratio (HR) for the Surrogate End Point Progression-Free Survival (PFS) and for the True End Point Overall Survival (OS) by Type of Trial

Each trial is represented by a bubble of a size proportional to the trial sample size. The solid straight line is the linear regression model from the copula estimates that relates the PFS HR to OS HR: ln(OS HR) = 0.025 + [0.67 × ln(PFS HR)]. The shaded area corresponds to the 95% prediction limits.

Figure 3.  Observed and Estimated Treatment Effect on Overall Survival in Validation Trials
Observed and Estimated Treatment Effect on Overall Survival in Validation Trials

HR indicates hazard ratio; error bars, 95% CIs.

Table 1.  Trial Characteristics
Trial Characteristics
Table 2.  Overall and Subgroup Analyses of the Surrogacy of Progression-Free Survival for Overall Survival
Overall and Subgroup Analyses of the Surrogacy of Progression-Free Survival for Overall Survival
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    Is Progression-free Survival a Valid Surrogate Endpoint for Overall Survival in Front-line Ovarian Cancer Trials?
    Thomas J. Herzog, MD1; Robert L. Coleman MD2 & R. Wendel Naumann MD3 University of Cincinnati1, MD Anderson2, Carolinas Medical Center3 | 1) University of Cincinnati; 2) MD Anderson; 3) Carolinas Medical Center
    Paoletti et al evaluated the surrogacy of progression-free survival (PFS) for overall survival (OS) among clinical trials for initial treatment of ovarian cancer. They reported a strong correlation between the two endpoints at the individual level, but not at the trial level.[1] Based on these observations they concluded that PFS is not an adequate surrogate endpoint for OS in the initial treatment of ovarian cancer.

    The authors address a critical question in clinical trial design, namely, what should be the primary endpoint for trials enrolling patients for front-line treatment of ovarian cancer? Arguably, the principal goal is
    to select the most relevant endpoint that best demonstrates and contextualizes true patient benefit. We certainly agree that OS is the most definitive endpoint, but this parameter is significantly challenged in terms of practicality and utility in front-line ovarian cancer trials where post-progression survival is long, cross-over treatment is frequent, and exposure to multiple effective agents in subsequent lines of therapy is highly prevalent.[1-3] Utilizing OS as an endpoint, especially in front-line studies, would limit clinical trial feasibility as the prolonged survivorship would require many thousands of patients to have any statistical validity to demonstrate an OS effect even in a trial demonstrating a statistically significant PFS benefit.[4]

    We have concerns with the authors’ conclusion as individual trials, conducted serially, cannot adequately account for treatment cross-over nor the discovery and use of effective subsequent therapies, which may not have been known or considered at the time of primary trial reporting. Indeed, trials with PFS as the sole primary endpoint are consistently released with immature OS assessments. The impact of this phenomenon can be cohort survival extension, where OS migrates over time. Furthermore, the studies that were analyzed included a striking number of negative and/or minimally effective trials, whereby the PFS effect was small or non-contributory to any OS, thus one would not expect a positive correlation given these limiting conditions. In fact only one of the 17 trials had a hazard ratio for PFS < 0.80, which challenges any chance of demonstrating an OS effect. There appear to be a number of trials during the cited inclusion period with positive PFS outcomes that were not included, which calls into question selection bias that further undermines the author’s conclusions.

    We believe that the improvement in survival over time has in part occurred via the cobbling of PFS improvements from multiple lines of treatment to improve OS despite the fact there are no clinical trials in the initial treatment of ovarian cancer that have successfully demonstrated an improvement in OS as the primary endpoint. OS should be analyzed as a secondary endpoint to assure no diminution in effect, and the PFS bar may need to be raised as we develop better therapeutics.

    It is important to place the Paoletti study into context as many may just read the headlines and assume that we must return to an OS primary endpoint construct for ovarian cancer, which could restrict drug development and cause potential harm in assuring the best outcomes for our patients. The bottom line is that while not perfect, PFS is the best surrogate for OS that we currently have, and it remains the best primary endpoint for front-line ovarian trials.

    [1-3] Herzog TJ, et. al. Gynecol Oncol. 2014;132(1);2014;135(1) & 2017 Oct;147.
    [4] Broglio K JNCI 2009 101(23)
    CONFLICT OF INTEREST: Herzog: Scientific Advisory Boards: Az, Caris, Clovis, Genentech, GSK, Merck; Coleman: Research-NCI - SPORE, AstraZeneca, Abbvie , Clovis, Roche/Genentech, V - Foundation, Janssen, Merck, Novartis;ScientificAd Boards:Abbvie, Agenus, AstraZeneca, Biomarin, Clovis, GamaMab, Genmab, Immunogen, Incyte, Janssen, Merck, Roche/Genentech, Tesaro; Naumann:Astra Zeneca - consulting BioSutro - consulting/research Bristol-Myers-Squib - institutional research Clovis - consulting Genentech - consulting Merck (Endocyte) - consulting/institutional research OncoMed - consulting/institutional research Tesaro - consulting/ institutional research Eisai - consulting
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    Original Investigation
    Oncology
    January 10, 2020

    Assessment of Progression-Free Survival as a Surrogate End Point of Overall Survival in First-Line Treatment of Ovarian Cancer: A Systematic Review and Meta-analysis

    Author Affiliations
    • 1Groupe d’investigateurs national des Etudes des Cancers Ovariens (GINECO), Paris, France
    • 2Gustave Roussy Cancer Center and Institut National de la Santé et de la Recherche Medicale Oncostat, Villejuif, France
    • 3Department of Biostatistics, University of Versailles St Quentin, Institut Curie, Saint-Cloud, France
    • 4Scottish Gynaecological Cancer Trials Group (SGCTG), Cancer Research United Kingdom Clinical Trial Unit, Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom
    • 5Multicenter Italian Trials in Ovarian Cancer and Gynecologic Malignancies (MITO), Clinical Trials Unit, Istituto Nazionale Tumori– Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Fondazione G. Pascale, Napoli, Italia
    • 6Medical Research Counsel Clinical Trials Unit, University College London, London, United Kingdom
    • 7Japanese Gynecologic Oncology Group (JGOG), Jikei University School of Medicine, Tokyo, Japan
    • 8Canadian Cancer Trials Group (CCTG), University of British Columbia, Vancouver, British Columbia, Canada
    • 9Nordic Society of Gynaecological Oncology, Norwegian Radium Hospital, Oslo, Norway
    • 10European Organisation for Research and Treatment of Cancer, Radboud University Medical Center, Nijmegen, the Netherlands
    • 11Hellenic Cooperative Oncology Group, General Oncology Hospital of Kifissia, Nea Kifissia, Greece
    • 12Gynecologic Oncology Group (GOG), Roswell Park Comprehensive Cancer Center, Buffalo, New York
    • 13Department of Medical Oncology, Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
    • 14University of Milan Bicocca, San Gerardo Hospital, Monza, Italy
    • 15University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
    • 16Association de Recherche sur les Cancers dont Gynécologiques–GINECO, Université Paris Descartes, Assistance Publique–Hôpitaux de Paris, Paris, France
    • 17MITO, Istituto Nazionale Tumori di Napoli IRCCS Fondazione G Pascale, Napoli, Italy
    • 18JGOG, Foundation for Biomedical Research and Innovation at Kobe, Translational Research Center for Medical Innovation, Kobe, Japan
    • 19CCTG, London Health Sciences Centre, London, Ontario, Canada
    • 20Gustave Roussy Cancer Center, Villejuif, France
    • 21SGCTG, Beatson West of Scotland Cancer Centre, NHS (National Health Service) Greater Glasgow and Clyde, Glasgow, United Kingdom
    JAMA Netw Open. 2020;3(1):e1918939. doi:10.1001/jamanetworkopen.2019.18939
    Key Points español 中文 (chinese)

    Question  Is progression-free survival a validated surrogate end point for overall survival in first-line systemic treatment of ovarian cancer?

    Findings  In this systematic review and meta-analysis of 17 unique trials with individual data from 11 029 unique patients, a high correlation between progression-free and overall survival was found at the individual level, but a low correlation was found at the trial level.

    Meaning  These findings suggest that overall survival is the preferred end point in trials of first-line treatment or maintenance treatment, and progressive-free survival must be supported by additional end points if used as the primary end point.

    Abstract

    Importance  The Gynecologic Cancer InterGroup (GCIG) recommended that progression-free survival (PFS) can serve as a primary end point instead of overall survival (OS) in advanced ovarian cancer. Evidence is lacking for the validity of PFS as a surrogate marker of OS in the modern era of different treatment types.

    Objective  To evaluate whether PFS is a surrogate end point for OS in patients with advanced ovarian cancer.

    Data Sources  In September 2016, a comprehensive search of publications in MEDLINE was conducted for randomized clinical trials of systematic treatment in patients with newly diagnosed ovarian, fallopian tube, or primary peritoneal cancer. The GCIG groups were also queried for potentially completed but unpublished trials.

    Study Selection  Studies with a minimum sample size of 60 patients published since 2001 with PFS and OS rates available were eligible. Investigational treatments considered included initial, maintenance, and intensification therapy consisting of agents delivered at a higher dose and/or frequency compared with that in the control arm.

    Data Extraction and Synthesis  Using the meta-analytic approach on randomized clinical trials published from January 1, 2001, through September 25, 2016, correlations between PFS and OS at the individual level were estimated using the Kendall τ model; between-treatment effects on PFS and OS at the trial level were estimated using the Plackett copula bivariate (R2) model. Criteria for PFS surrogacy required R2 ≥ 0.80 at the trial level. Analysis was performed from January 7 through March 20, 2019.

    Main Outcomes and Measures  Overall survival and PFS based on measurement of cancer antigen 125 levels confirmed by radiological examination results or by combined GCIG criteria.

    Results  In this meta-analysis of 17 unique randomized trials of standard (n = 7), intensification (n = 5), and maintenance (n = 5) chemotherapies or targeted treatments with data from 11 029 unique patients (median age, 58 years [range, 18-88 years]), a high correlation was found between PFS and OS at the individual level (τ = 0.724; 95% CI, 0.717-0.732), but a low correlation was found at the trial level (R2 = 0.24; 95% CI, 0-0.59). Subgroup analyses led to similar results. In the external validation, 14 of the 16 hazard ratios for OS in the published reports fell within the 95% prediction interval from PFS.

    Conclusions and Relevance  This large meta-analysis of individual patient data did not establish PFS as a surrogate end point for OS in first-line treatment of advanced ovarian cancer, but the analysis was limited by the narrow range of treatment effects observed or by poststudy treatment. These results suggest that if PFS is chosen as a primary end point, OS must be measured as a secondary end point.

    Introduction

    In 2012, approximately 240 000 women worldwide were diagnosed with an advanced ovarian, epithelial, fallopian tube, or primary peritoneal cancer.1 Approximately 75% of women have Fédération Internationale de Gynécologie et d’Obstétrique (FIGO) stage III or IV cancer at diagnosis. Initial management involves the combination of surgical cytoreduction and systemic chemotherapy. Carboplatin and paclitaxel constitute the universal standard regimen in the management of ovarian cancer, with a response rate of approximately 65%, median progression-free survival (PFS) ranging from 16 to 21 months, and median overall survival (OS) ranging from 32 to 57 months.2 Currently, OS is the criterion standard for the evaluation of treatment, but both OS and PFS have led to drug approvals by regulatory agencies (the US Food and Drug Administration and European Medicines Agency). Progression-free survival gives an earlier assessment of antitumor activity, requires smaller sample sizes, and is not affected by postprogression therapy. The Gynecologic Cancer InterGroup (GCIG)3 recommended that PFS can serve as a primary end point instead of OS, provided that secondary end points, such as quality of life, support the superiority of the investigated treatment. Evidence of the validity of PFS as a surrogate marker of OS in the modern era of different treatment types is lacking. In 2009, Buyse4 showed that PFS was a good surrogate marker of OS in ovarian cancer, but that study was limited to 4 trials that investigated standard cytotoxic regimens (cyclophosphamide plus cisplatin vs cyclophosphamide plus doxorubicin hydrochloride [Adriamycin] plus cisplatin) and used the older World Health Organization definition of progression. A correlation at the individual level measured by a Kendall τ of 0.84 (95% CI, 0.83-0.85) and at the group level measured by a Pearson correlation of 0.95 (95% CI, 0.82-1.00) was found. In these trials, treatment effect on PFS was associated with treatment effect on OS.

    Since then, novel targeted therapies have been introduced, many of which are used as maintenance therapy. Among the tools to evaluate progression and response to treatment, cancer antigen 125 (CA125) level is an important marker in epithelial ovarian cancer.5 The GCIG integrated the elevation of CA125 levels into the radiological Response Evaluation Criteria in Solid Tumours (RECIST) to give a combined definition of progression.6 These combined criteria have never, to our knowledge, been investigated as an OS surrogate using the meta-analytic approach. Trials use different methods of assessing progression, including clinical or CA125-triggered and regular computed tomographic (CT) scans. The effect of such different assessment methods on the surrogacy of PFS also has not been assessed. To formally assess PFS measured by RECIST and combined GCIG criteria as a potential surrogate end point of overall survival, the GCIG meta-analysis group launched a prospectively planned pooled analysis of data from 11 029 individual patients (individual patient data [IPD]) and 17 randomized clinical trials of first-line therapy (initial treatment, intensification treatment, or maintenance treatment) in advanced ovarian cancers.

    Methods

    This report follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)–IPD guidelines for the registration of the protocol, trial identification, data collection and integrity, assessment of bias, and sensitivity analyses.7 This meta-analysis was registered with PROSPERO (CRD42017068135). The Ethics Committee of Gustave Roussy Cancer Center, Villejuif, France, approved this study, and the French data protection authority waived the need for informed consent for the use of deidentified data.

    Trial Selection

    In September 2016, a comprehensive search in MEDLINE of publications on advanced ovarian cancer was conducted. The GCIG groups were queried for potentially completed but unpublished trials. Eligible trials were randomized clinical trials of systemic treatments in patients with previously untreated ovarian cancer (or investigating maintenance treatment after first-line systemic treatment) with a minimum sample size of 60 patients in total and published from January 1, 2001, through September 25, 2016, with both OS and PFS available. Investigational treatments considered were initial, maintenance, and intensification therapy that consisted of agents delivered at higher dose and/or frequency compared with that in the control arm. The investigators of all identified trials that met the eligibility criteria were contacted for IPD sharing.

    Data and Outcomes

    We requested data for all individual patients (whether or not they had been included in the primary analysis) enrolled in each trial. Overall survival was defined as the time from randomization to all-cause death or the date of the last follow-up used for censoring. Progression-free survival was defined as the time from randomization to progression or second cancer when this information was available, time to all-cause death, or the date of the last follow-up used for censoring, whichever came first. Detailed information on the type of progression was requested; this included the definition of progression, the radiological and/or clinical evaluation that documented progression, and serial measurements of CA125 levels. Assessment of progression was grouped into 3 main categories: (1) clinical examination and monitoring of 2 increases of CA125 levels to trigger CT scan confirmation of progression, (2) radiological monitoring based on RECIST, and (3) both CA125 levels and radiological assessment in line with the GCIG recommendations. Patients alive without documented disease progression were censored at the date of last follow-up. All data were centrally reanalyzed and checked for inconsistencies. In particular, diagnostic tools for randomization quality were systematically applied.8,9 Analysis of surrogacy was performed January 7 through March 20, 2019.

    Statistical Analysis

    Forest plots were used to display the hazard ratios (HRs) overall and for individual trials, which were then used for the evaluation of surrogacy of PFS for OS. The HRs compared the hazard of an event in patients treated with an investigational regimen with the hazard in patients given the control treatment. A fixed-effect approach was implemented, and HRs were obtained from the expected and observed numbers of events. The pooled HR was then adjusted for the trial. The χ2 heterogeneity test and I2 statistic were used to investigate the overall heterogeneity between trials.10 Survival curves were estimated with the actuarial-based approach of Peto et al11 to account for the multiple trials. Evolution of the median survival time was assessed using a linear trend test at the trial level weighted by the number of events. Surrogacy can be evaluated at 2 different levels. At the individual level, correlation between PFS and OS means that patients with longer PFS are expected to have longer OS. However, this may only reflect the natural history of the disease, whatever the treatment is. For the assessment of the trial-level surrogacy, the treatment effect on PFS was correlated with the treatment effect on OS; in other words, we evaluated how much of the treatment effect on OS could be predicted from (or explained by) the treatment effect on PFS. We used the Kendall τ (a rank-correlation coefficient) between PFS and OS to assess surrogacy at the individual level and the coefficient of determination (corresponding to the explained variation) between the natural logarithm of the HRs for PFS and OS to assess surrogacy at the trial level.12-15 For both coefficients, 0 indicates absence of correlation, whereas 1.00 indicates perfect correlation. At the individual level, the association between the distribution of the true (OS) and surrogate (PFS) end points was evaluated using a bivariable model based on the Plackett copula combined with trial-specific Weibull models for PFS and OS.13 The treatment effects on PFS and OS were obtained from the bivariate model. The linear association between the 2 treatment effects was estimated, which in turn provided the coefficient of determination R2 for trial. Following the FLASH (Follicular Lymphoma Analysis of Surrogacy Hypothesis) initiative16 and a report of childhood acute lymphoblastic leukemia,17 a surrogate was considered to provide a reliable prediction of the treatment effect on OS from the PFS HR, when the trial-level correlation exceeded 0.8 and its 95% prediction interval excluded 0.6. This predefined threshold is arbitrary and served to limit post hoc biases (ie, choice of the threshold based on the data). Analyses were performed on an intention-to-treat basis (all patients analyzed in their allocated group irrespective of possible protocol deviations).

    Sensitivity and Subpopulation Analysis

    Leave-1-out cross-validation was implemented to assess the prediction performance of the regression model. The validation process was performed on all but 1 trial, and OS HR was predicted from the PFS HR for the left-out trial and compared with the observed value. The process was repeated for each of the 17 trials to identify potential influential trials and investigate the robustness of the results. Preplanned subgroup analyses investigated the surrogacy measures by definition of progression, by study design (initial, intensification, or maintenance treatment), and within trials that used paclitaxel and carboplatin as the control arm.

    External Validation

    To assess the external validity of our results, we used 16 trials for which we had not been able to receive IPD from the sponsors. Two of us (X.P. and E.K.) independently extracted the HRs and confidence intervals for PFS and OS from summary statistics published in these trials.18 The HR on PFS reported in the publication served to predict HR on OS that we in turn compared with the published HR on OS. All analyses were done using SAS, version 9.4 (SAS Institute Inc), with macros developed by Tomasz Burzykowski, PhD, and R, version X, using R surrosurv package, version 1.1.25 (R Project for Statistical Computing).19 Two-tailed P < .05 calculated using the test for heterogeneity was considered to signify statistical significance. Confidence and prediction intervals were computed at the 95% level.

    Results
    Trials’ Descriptions

    As illustrated in eFigure 1 in the Supplement, 37 trials were identified from the literature search and their investigators were contacted. Individual patient data were obtained on 11 029 unique patients from 17 unique eligible randomized clinical trials with documented OS and PFS.2,20-36Table 1 lists the trial-level characteristics of the 17 studies; eTable 1 in the Supplement gives an assessment of the risk of bias. In 10 trials,20,21,24-29,32,36 carboplatin and taxanes were the comparator. Seven studies20,22,24,26,28,32,36 investigated initial treatment; 5 studies,21,24,25,30,33 intensification treatment; and 5 studies,23,27,31,34,35 maintenance treatment. Four trials tested molecularly targeted treatments.23,27,31,34 A total of 10 trials21-23,25,26,30,33,34,36 used CA125 levels to trigger follow-up CT scans after an initial increase in the biomarker. Six trials24,27-29,31,35 used the GCIG criteria (1 multinational trial used both), and 2 trials20,32 used CT scan only. Data on both end points were available for all 11 029 patients, of whom 7436 experienced progression and 5138 died during follow-up. Detailed information about patients’ characteristics by allocated treatment arm and median follow-up are provided in eTable 2 in the Supplement. Median patient age was 58 years (range, 18-88 years); 5990 (54.3%) had Eastern Cooperative Oncology Group performance status of 0 at enrollment; and 8497 (77.0%) had FIGO stage III or IV disease. eFigure 2 in the Supplement shows the Peto survival curves for PFS and OS. No statistically significant time trends in the median OS and PFS according to the date of the first randomization were detected. The median OS ranged from 2.7 to 6.2 months and the median PFS ranged from 0.9 to 2.3 months (see Table 1 for median survival and eFigure 3 in the Supplement for their representation over time). No time trends according to the date of the first randomization were detected. Figure 1 shows a forest plot of the treatment effects on OS and PFS for all trials (eFigure 4 in the Supplement gives forest plots grouped by progression assessment criteria). Overall and at the trial level, the effects of investigational chemotherapy on PFS and OS were almost null (HR for PFS, 0.97 [95% CI, 0.93-1.02]; HR for OS, 0.99 [95% CI, 0.94-1.05]). No heterogeneity across trials was detected for any of the end points (I2 = 0% [P = .70] for OS and I2 = 0% [P = .60] for PFS) (Figure 1).

    Individual- and Trial-Level Associations

    The individual-level association, as measured by the Spearman rank correlation coefficient, reached 0.885 (95% CI, 0.879-0.890). The Kendall τ estimate was 0.724 (95% CI, 0.717-0.732), indicating a good correlation between PFS and OS; that is, a patient who progresses later is more likely to survive longer than a patient who progresses earlier. On the contrary, a very low correlation was noted between ln(OS HR) and ln(PFS HR) (Figure 2), where ln denotes the natural log transformation of the HR for each end point. The coefficient of determination, R2 for trial, for the estimated treatment effects was as low as 0.24 (95% CI, 0-0.59), indicating a low correlation between PFS and OS at the trial level. The linear regression model from the copula estimates was ln(OS HR) = 0.025 + [0.67 × ln(PFS HR)]. Standard errors were 0.03 and 0.31 for the intercept and slope, respectively. This is shown as a straight line in Figure 2, where the x-axis represents the treatment effect on PFS and the y-axis represents the treatment effect on OS. The shaded area corresponds to the 95% prediction limits that indicate the range of effect on OS that can be expected for a given effect on PFS, but owing to the very poor correlation, it remains largely theoretical. Despite large sample sizes, some trials with similar treatment effect on PFS had a different effect on OS, including PFS HR of greater than 1.00 together with OS HR of less than 1.00, translating into uncertainty in the prediction.

    Sensitivity Analyses

    Leave-1-out cross-validation demonstrated the robustness of the results, because we had consistency between observed and predicted OS treatment effects for each trial based on the PFS (eFigure 5 in the Supplement). Only 1 strongly influential trial was identified; the OV-12 trial23 investigated tanomastat as maintenance therapy, which was interrupted by Bayer owing to negative results in other cancer types, and follow-up was stopped23; progression was assessed using CT scans after initial increase of CA125 levels. Excluding this trial increased the estimate of R2 for trial to a moderate value of 0.66 (95% CI, 0.40-0.93), more in line with previous results.

    Subgroup Analyses

    Subpopulation analyses that separately focused on maintenance and nonmaintenance trials confirmed that treatment effect on PFS poorly predicted treatment effect on OS: trial-level surrogacy was low for maintenance trials (Table 2), with R2 for trial estimates from 0.03 (95% CI, 0-0.35) for maintenance vs 0.67 (95% CI, 0.36-0.97) for nonmaintenance. The marked difference was mainly explained by the OV-12 trial in the maintenance subgroup, because the R2 for the trials increased to 0.78 (95% CI, 0.40-1.00) after exclusion of this trial; the small number of trials in this subgroup strongly increased the results’ instability. Trial-level correlation was also low (R2 for trial = 0.15; 95% CI, 0-0.56) in trials that compared investigational treatment with carboplatin and taxanes. In the 6 trials24,27-29,31,35 (4603 patients) that specified GCIG guidelines to assess progression, prediction of OS HR based on PFS HR was better (R2 for trial = 0.43; 95% CI, 0.02-1.00) than that in trials that used CT scan after the initial increase of CA125 level; however, the OV-12 trial23 again reduced the estimated association between the treatment effects in the trials that used other assessments of progression.

    External Validation

    Of the 20 trials in which we could not access the IPD (owing to refusal by the investigators,37-56 no response to our request, or data declared no longer available), we could extract HRs for 16 of them (8 testing initial treatments and 8 testing maintenance treatments).37-52 None of the trials demonstrated a statistically significant effect on OS, and only 3 studies43,49,51 reported a statistically significant reduction of PFS. eTable 3 in the Supplement and Figure 3 display observed OS HR and PFS HR with 95% CIs, and OS HR predicted from the model of Figure 2. Observed estimates for all except 2 trials fell within the 95% prediction intervals. However, the intervals are relatively large, reflecting the uncertainty around the prediction.

    Discussion

    This pooled analysis was performed on the IPD of 11 029 patients treated in 17 randomized trials of first-line treatment for advanced ovarian cancer initiated worldwide from 1995 through 2010. Although PFS was strongly associated with OS at the individual level, we did not find a strong correlation between the treatment effects on PFS and on OS (ie, HR on PFS did not predict the HR on OS at the trial level). Low correlation was observed in maintenance and nonmaintenance therapy trials. Overall, HRs on OS and PFS were close to 1.00, with little heterogeneity among trials whether maintenance or nonmaintenance treatments were explored. All trials required rigorous response assessment schedules, with clinical and physical examination, evaluation of CA125 levels, and CT imaging. At the trial level, PFS assessed by CT scans and CA125 levels following the GCIG guidelines was moderately correlated with OS in a subgroup of 6 trials.24,27-29,31,35 Nevertheless, the role of CA125 measurements is controversial. No international standard has been established, leading to variability in calibration, assay design, and reagent specificities,57 and CA125 level is not considered a stand-alone marker of progression.

    One trial can be seen as an outlier; the tanomastat trial was interrupted by Bayer owing to negative results in pancreatic and small cell lung cancer trials, resulting in poor follow-up for OS23; exclusion of this trial led to moderate trial-level associations. Nevertheless, even after exclusion of this trial, the trial-level correlation was below the predefined threshold. In trials for which we could not access IPD, no statistically significant treatment effect on OS had been reported. As external validation, we showed that for those 16 trials, the observed treatment effect (OS HR) fell within the interval predicted from PFS HR, but the interval was too large to draw accurate predictions on OS HR. These findings therefore do not support PFS as a substitute for OS in randomized clinical trials: demonstrating a reduction of PFS HR does not guarantee that a reduction in the hazard of death will be observed. If PFS is used, the GCIG criteria might be preferable as the means of assessment of progression.

    Previous exploration of surrogacy in trials of first-line treatments in ovarian cancers by Buyse4 found high correlation, but with 4 trials that were split into subunits to increase the number of treatment effect assessments. More recently, several authors58,59 found moderate to high correlations at the trial level (R2 range, 0.50-0.83) from summary statistics extracted from the literature. However, unlike IPD, literature-based meta-analysis does not enable consistent calculation of end points or the full use of survival-censored data after quality checks; in addition, estimation of joint model and hence accounting for the correlated PFS and OS measured in the same patient is insufficient, leading to potential biases.

    The choice of the best measure to quantify the treatment effect is controversial. Although the HR is probably the most commonly used relative measure, its validity is limited by the requirement to have a proportional hazard (ie, that the HR is constant over time). However, in the clinical trial International Collaboration on Ovarian Neoplasm (ICON7),27 this assumption did not hold for bevacizumab as maintenance treatment. The primary analysis was then based on an absolute measure, the difference in the restricted mean survival time between the 2 arms. The question of the surrogacy value of restricted mean survival times is to be explored in further analyses.

    Limitations

    The main limit of our approach is the lack of treatment effects as measured by HR in the collected trials. Indeed, the lack of heterogeneity in the treatment effects strongly limits our ability to detect an association between PFS HR and OS HR. The regression line in Figure 2 may have been more precisely estimated if HRs had been spread across a large range. However, as shown by the validation analysis, the trials that were not collected were also negative and followed the same association between PFS HR and OS HR; additional trials should not strongly modify the conclusions obtained from this large sample. The treatment of ovarian cancers is well standardized, probably thanks to the tradition of strong collaboration within the GCIG and European Society of Gynecological Oncological Trial groups (ENGOT). Most trials enrolled large numbers of patients, were multicentric (and many international), shared the same regimen as a control, and collected similar variables. This may explain the strong homogeneity in the trials’ results; this also supports the generalizability of our findings.

    A striking finding is the disappointing treatment effects measured on the PFS and the OS. This pooled analysis provides a useful benchmark for future trials. We hope that the recent improvements in PFS seen in a trial of poly–adenosine diphosphate ribose polymerase inhibitors60 translate into improvements in OS. So far, the combination of carboplatin and paclitaxel remains the standard chemotherapy backbone for first-line treatment.

    Conclusions

    Progression-free survival cannot be validated as a strict surrogate of OS for assessing treatment effects in randomized clinical trials of first-line treatments of advanced ovarian cancers. Our findings support the GCIG Fifth Ovarian Cancer Consensus Conference statement that OS is the preferred primary end point for first-line clinical trials with or without a maintenance component,3 but we recognize the practical challenges and the potential for confounding factors such as crossover and long postprogression survival. Progression-free survival is an alternative primary end point, but given that we have not been able to validate it as a surrogate of OS, following the US Food and Drug Administration and European Medicines Agency guidances,61,62 it should represent a favorable risk-benefit association with a large magnitude of the effect or it should contribute to delaying administration of more toxic therapies as second-line treatments; therefore, if PFS is chosen, OS must be measured as a secondary end point and PFS must be supported by additional end points, such as predefined patient-reported outcomes, especially for maintenance therapy.

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    Article Information

    Accepted for Publication: October 21, 2019.

    Published: January 10, 2020. doi:10.1001/jamanetworkopen.2019.18939

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Paoletti X et al. JAMA Network Open.

    Corresponding Author: Xavier Paoletti, PhD, Department of Biostatistics, Institut Curie, 35 Rue Dailly, 92210 Saint-Cloud, France (xavier.paoletti@curie.fr).

    Author Contributions: Dr Paoletti had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Paoletti, Ottevanger, Aravantinos, Pujade-Lauraine, Pignata, Glasspool.

    Acquisition, analysis, or interpretation of data: Paoletti, Lewsley, Daniele, Cook, Yanaihara, Tinker, Kristensen, Ottevanger, Aravantinos, Miller, Boere, Fruscio, Reyners, Pujade-Lauraine, Harkin, Kagimura, Welch, Paul, Karamouza, Glasspool.

    Drafting of the manuscript: Paoletti, Karamouza, Glasspool.

    Critical revision of the manuscript for important intellectual content: Paoletti, Lewsley, Daniele, Cook, Yanaihara, Tinker, Kristensen, Ottevanger, Aravantinos, Miller, Boere, Fruscio, Reyners, Pujade-Lauraine, Harkin, Pignata, Kagimura, Welch, Karamouza, Glasspool.

    Statistical analysis: Paoletti, Cook, Kagimura, Karamouza.

    Obtained funding: Paoletti.

    Administrative, technical, or material support: Paoletti, Lewsley, Daniele, Ottevanger, Miller, Reyners, Harkin, Kagimura, Karamouza.

    Supervision: Paoletti, Daniele, Aravantinos, Pignata, Paul.

    Conflict of Interest Disclosures: Dr Tinker reported receiving grants from AstraZeneca outside the submitted work. Dr Kristensen reported receiving grants from Pharmacia & Upjohn during the conduct of the study. Dr Pujade-Lauraine reported receiving personal fees and nonfinancial support from AstraZeneca and Roche Diagnostics and personal fees from Tesaro-GlaxoSmithKline, Clovis Oncology, Inc, and Incyte Corp outside the submitted work. Dr Kagimura reported receiving grants from the Japanese Gynecologic Oncology Group outside the submitted work. Dr Welch reported receiving grants and personal fees from AstraZeneca and personal fees from Roche Diagnositics, Amgen, Inc, Celgene Corporation, and Ipsen outside the submitted work. Mr Paul reported receiving grants from Cancer Research UK during the conduct of the study. Dr Glasspool reported receiving grants from Boehringer Ingelhem and Eli Lilly and Company/Ignyta, Inc, personal fees and nonfinancial support from AstraZeneca and Tesaro, personal fees from Sotio, Clovis Oncology, Inc, ImmunoGen, Inc, and Roche Diagnostics, and personal fees from AstraZeneca, Tesaro, AbbVie, Inc, ImmunoGen, Inc, and Pfizer, Inc, outside the submitted work and serving as Scottish Gynaecological Cancer Trials Group representative of the Gynecologic Cancer Inter-Group (GCIG) at the GCIG consensus conference. No other disclosures were reported.

    Funding/Support: This study was partly supported by grant PHRC-K 2017 from the Programme Hospitalier de Recherche Clinique en Cancérologie from the French Ministry of Health and by the French Ligue Against Cancer.

    Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Group Information: The Gynecological Cancer InterGroup (GCIG) Meta-analysis Committee for ovarian first-line treatments includes Adrian Cook, PhD (Medical Research Council), Gennaro Daniele, MSc (Multicenter Italian Trials in Ovarian Cancer and Gynecologic Malignancies), Rosalind M. Glasspool, MD, PhD (Scottish Gynaecological Cancer Trials Group), Trine Juhler, MD, PhD (Nordic Society of Gynaecological Oncology), Tatsuo Kagimura, PhD (Japanese Gynecologic Oncology Group), Elise Kohn, MD, PhD (National Cancer Institute–Cancer Therapy Evaluation Program), Gunnar Kristensen, MD, PhD (Nordic Society of Gynaecological Oncology), Ian McNeish, MD, PhD (National Cancer Research Institute), Austin Miller, PhD (Gynecologic Oncology Group), Petronella B. Ottevanger, MD, PhD (European Organisation of Research and Treatment of Cancer), Xavier Paoletti, PhD (Groupe d’Investigateurs National des Etudes des Cancers Ovariens), James Paul, MSc (Scottish Gynaecological Cancer Trials Group), Wendy Parukular, MD, PhD (National Cancer Institute of Canada/Canadian Cancer Trials Group), Sandro Pignata (Multicenter Italian Trials in Ovarian Cancer and Gynecologic Malignancies), Eric Pujade-Lauraine, MD, PhD (Groupe d’Investigateurs National des Etudes des Cancers Ovariens), Satoru Sagae, MD, PhD (Japanese Gynecologic Oncology Group), and Donshen Tu, PhD (National Cancer Institute of Canada/Canadian Cancer Trials Group).

    Additional Contributions: We thank all the women who consented to participate in the randomized clinical trials and the data centers of the various study sites that extracted individual patient data and addressed our queries. Monica Bacon and Katherine Bennett (GCIG operations office) and the executive committee continuously supported this initiative. Corneel Coens, MSc (European Organisation of Research and Treatment of Cancer headquarters), Arthur Miller, PhD (NRG Oncology), and the Medical Research Council Clinical Trials Unit at University College London provided support. These individuals received no compensation for their contributions.

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